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Meta Wants Some Managers Back. Who Was Supposed to Do Their Work?
On September 10, 2026, Business Insider reported that Meta was asking some individual contributors in its Applied AI division if they wanted to move back into management roles. The report cited four people with knowledge of the reshuffle, with one saying participation was voluntary. Meta declined to comment.
That caught my attention because of something middle management expert Sally Foley-Lewis and I discussed about middle management and AI. Sally draws a useful line between the work AI can speed up and the work a manager still has to do. AI may remove routine or systematized tasks, but somebody still has to handle the human work around them.
Meta’s reported reshuffle does not prove that flatter teams failed or that AI cannot handle management tasks. It raises a more practical question for any company redesigning jobs around AI: when you remove a management layer, which responsibilities actually disappear, and which ones quietly land on somebody else’s desk?
Meta Flattened the Structure. The Responsibilities Stayed.
Meta’s September reshuffle makes more sense against the changes the company had been exploring earlier in the year.
In an August 26 Reuters investigation, reporters described Meta’s work on more “AI native” organizational structures involving smaller teams, more fluid roles, heavier use of AI, and fewer management layers.
The tempting headline is that Meta cut managers, AI fell short, and the managers came back. The reporting does not establish that sequence.
Business Insider’s September report involved some employees in one division. Reuters also reported that Meta considered a second restructuring wave earlier in 2026, then canceled planning for it on May 19. Reuters could not determine exactly why the company changed course.
The more useful question is where the management work went as the structure changed.
A manager may spend time preparing updates, collecting information, moving work between systems, checking routine output, and doing administrative follow-up. AI can reduce some of that work substantially. If those tasks shrink, the job may reasonably change with them.
The same role often contains work that is harder to eliminate.
Someone has to decide which of two legitimate priorities wins when they compete for the same person or budget. Someone has to explain why a decision matters to the team carrying it out. Someone has to recognize when an executive-level plan creates a practical problem lower in the organization. Someone has to bring a conflict across departments to the right person before it becomes a larger problem.
Those responsibilities do not automatically disappear when a reporting layer does.
They can move to another manager. They can be spread deliberately across a team. They can be built into another role. Trouble starts when everybody assumes somebody else owns them.
That makes Meta’s reported reshuffle more useful as a management-design problem than as another scorecard in the argument over whether AI is replacing jobs. Before a company celebrates removing a layer, it needs to know which responsibilities were automated, which genuinely became unnecessary, and which are still sitting inside somebody’s workload without a name.
AI Can Remove Tasks Without Removing Leadership
Sally Foley-Lewis makes a distinction that helps separate those categories.
“AI is a productivity tool. It is not a leadership tool.”
She is not arguing that managers should avoid AI. Her point is almost the opposite. Middle managers often perform individual contributor work, manage processes, and lead people at the same time. If AI can remove some of the repetitive or systematized work, she sees an opportunity to spend more time on the people side of the role.
That matters when companies redesign jobs after AI reduces reporting, drafting, documentation, or routine analysis.
A manager may no longer need to spend as much time compiling information that software can summarize quickly. A first draft may arrive faster. Routine documentation may become easier. Some coordination may be automated.
None of that settles what happens when two teams need the same resource, an employee sees a problem the system missed, or a senior leader hands down a decision that creates complications for the team expected to implement it.
Sally describes middle managers as operating in three directions. They “inspire down, collaborate across, and influence up.”
That is more specific than saying managers motivate people.
A middle manager may need to explain the purpose behind an assignment to the people doing it. That same manager might negotiate with another department that has its own priorities, then go upward with evidence and alternatives when the original plan needs to change.
AI can help with pieces of that work. It can summarize disagreement, organize information, prepare options, retrieve earlier decisions, or draft a message.
Someone still has to decide whether the disagreement matters and what to do about it.
Meta itself offers a separate example of that distinction. In a September 2 engineering post, Meta described an internal AI system built to learn from experts while retaining human review checkpoints and escalating ambiguous cases.
That is Meta’s description of one particular system. It does not establish how every team at Meta works, and it is not the same thing as the Applied AI management reshuffle. It does provide one concrete example of automation being designed around continued human review.
Sally makes a similar point from the management side. Leaders do not need to know every answer. She says they need enough presence of mind to ask good questions and enough calm to listen when someone else knows more.
That work is easy to miss in an efficiency calculation because it does not always appear as a discrete task you can delete from a spreadsheet. It shows up when responsibilities cross departments, priorities collide, an employee pushes back, or somebody has to translate a decision into something a team can actually carry out.
Sometimes the problem starts much earlier, with the way the work gets handed over.
A Handoff Can Look Efficient While Creating More Work
Sally told me about a manager she knows named Michelle who was handed a large folder by her boss and told, in effect, to take it to the team and make it happen.
Sally’s phrase for the handoff was “it was just a dump and run.”
She was careful not to turn the boss into a villain. Michelle later recognized that he probably gave her the assignment because he believed she could handle it.
The missing part was everything between assigning the work and making the work executable.
Michelle still had to understand what she had been given, decide how to explain it, figure out what mattered most, and bring her team along. The folder moved quickly from one desk to another. The interpretation, prioritization, persuasion, and coordination moved with it.
That is exactly the sort of work that can vanish from an org chart without vanishing from the day.
A company can remove a layer, combine roles, or automate administrative work and make the structure look cleaner. One person has fewer direct reports. One team has fewer meetings. A process has fewer steps.
Somebody may still be absorbing the work of translating priorities, resolving conflicts, gathering context, and deciding what the people doing the work actually need.
The same thing can happen without a restructuring. A senior leader can make a reasonable decision, hand it down in a few sentences, and assume the difficult part is finished. The manager below may then spend hours dealing with questions that were invisible at the point where the decision was made.
That is why I would be careful about measuring a flatter structure only by the number of roles, meetings, or handoffs removed.
Ask whether context, authority, prioritization, and coordination were actually removed. If they were not, ask who now handles them.
Sally also talks about middle managers having to push back respectfully when demands coming from above ignore priorities already in play. That does not mean every deadline gets challenged or every manager gets veto power.
It means somebody has to notice when two legitimate commitments are competing for the same capacity.
If nobody clearly owns that decision, the conflict still gets resolved. It just happens informally, late, or by whichever person is willing to absorb the extra work.
Check Who Owns the Work Before You Flatten Another Role
I would test one live project with five questions before changing the structure again:
- Who explains the actual outcome and why it matters?
- Who resolves the conflict when two important priorities compete for the same time or resources?
- Who gets cooperation from another team when the work crosses boundaries?
- Who carries an unresolved problem upward with useful options instead of simply passing along the complaint?
- Who checks whether the people doing the work have enough information, authority, and support to finish it properly?
One person may own several of those responsibilities. A team may deliberately divide them. There is nothing sacred about assigning each one to a traditional middle manager.
The important part is that the ownership is explicit.
Then ask what AI actually removed.
Maybe it eliminated much of the reporting work. Maybe it drafts the update, summarizes meetings, finds previous decisions, or prepares an initial analysis. Those can be substantial improvements.
If the five questions still need answers, though, then a large part of the management work remains. Some of it may be easier. Some may have moved to another person. Some may be spread across several roles.
Sally’s advice to “slow it down” matters here.
She describes herself as a fast decision maker and admits that she has rushed into decisions, then had to come back and fill gaps that might have been visible if she had slowed down.
Faster information does not require faster judgment.
If an AI system produces an analysis much faster, the extra time can be used to compare options, challenge an assumption, or notice a conflict before the decision reaches the rest of the team.
That does not mean every decision needs another meeting or another week of review. Sometimes nothing else will materially change the choice, and making the call is exactly the right move.
Other times, a few more questions expose the fact that the new structure has no clear owner for a responsibility everyone assumed had disappeared.
That is worth finding out before changing another role.
The Work Has to Land Somewhere
Meta’s reported September reshuffle leaves a useful decision for your own organization.
Before removing another management role, identify where its remaining responsibilities will land.
Maybe the answer is another manager. Maybe the responsibilities are divided deliberately among several people. Maybe AI genuinely eliminated enough work that very little remains.
The dangerous answer is nobody knows.
Before you remove another box from the org chart, make sure you know where the work inside that box is going.
Explaining priorities and resolving conflicts still need an owner, even when the routine reporting takes care of itself. Sally Foley-Lewis is also a co-author of our book, Decisions, Courage, and Action. If her distinction between automating tasks and leading people has you reconsidering a decision in your own business, pick up the book on Amazon and read more from Sally.
About Sally Foley-Lewis
Sally Foley-Lewis works with middle managers to build confidence, capability, communication, and leadership performance. Her leadership-development career spans more than 20 years, and she has worked with more than 20,000 managers and leaders across a wide range of organizations and industries. Learn more about her work at https://www.sallyfoleylewis.com/. Connect with Sally Foley-Lewis on LinkedIn.
Media and interview inquiries: To request an interview about middle management and team leadership, visit her media page.
1674: Revolutionary War Fiction and SCAD Survival with Suzy Ryan
Author, educator, and Ironman athlete Suzy Ryan joins Marketer of the Day to discuss her books, teaching, resilience, and the experiences that shaped her work. She shares her survival story after spontaneous coronary artery dissection (SCAD), which caused multiple heart attacks while she was training and teaching. After realizing something was seriously wrong, she was treated by cardiologist Dr. Kenneth Carr, who identified a dissection in her left anterior descending artery and placed long stents. Suzy reflects on recovery, faith, listening to warning signs, and her determination to see herself as a victor rather than a victim. Her experience also inspired her to speak publicly about heart awareness and encourage people to seek medical attention when symptoms feel unusual. Her candid story highlights how unexpected adversity can become a source of perspective, purpose, and connection.
Suzy also shares the inspiration behind Two Spies in Time, a historical time-travel novel built around an alternative version of the American Revolution. In the story, Benedict Arnold’s treason succeeds, the British win the war, and a modern protagonist travels from Commonwealth Kansas to colonial New York City in 1780. Suzy uses the changed timeline to imagine how a different outcome would reshape daily life, culture, and the identity of the United States. Her research brings figures such as George Washington, Nathan Hale, and Benedict Arnold into the story as complex people rather than one-dimensional names from a textbook. She discusses Washington’s leadership under pressure, Arnold’s motivations and betrayal, and Hale’s sacrifice as a young spy. By combining historical fiction, alternate history, and classroom-friendly storytelling, Suzy hopes to make the Revolutionary era engaging and memorable for readers.
Beyond her novels, Suzy reflects on her years as a middle- and high-school English and social studies teacher, and her goal of helping young people understand American history and the complicated lives behind its heroes. She connects that mission to a growth mindset, empathy, and her message that people can move forward without denying hardship. Suzy describes starting Warrior Publishing LLC, learning to format her own books, and collaborating with her husband, teacher friends, and audiobook narrator Todd McLaren to bring Two Spies in Time to readers. She also discusses Saving Summer and The Seaside Storybook, a student-created collection whose proceeds supported people experiencing homelessness. Suzy says stories can preserve lessons, honor the people who shaped us, and inspire the next generation. The conversation closes with reflections on motherhood, teaching, and quotes from Rudyard Kipling and Maya Angelou about seeing the best in people and the lasting impact of how we make others feel.
Quotes:
“You are not victims; you’re a victor, no matter what you’ve gone through, because I’ve been through a lot, and I’m a victor.”
“When you feel something like that, you get in to the doctor, even if you’re healthy, even if you don’t have a history of heart issues.”
“Everything is there for a reason. There’s no random fact, even though you may be reading and think, ‘How is she going to pull all this together?"
Contact Details:
- Read the Story of Resilience: Explore Suzy Ryan’s Website
- Discover More of Suzy’s Story: Follow Her on Instagram
- Follow Suzy Ryan on Facebook for More Inspiring Updates and Insights
- Subscribe to Suzy Ryan on YouTube for More Inspiring Content
- Saving Summer on Amazon
- Two Spies in Time on Amazon
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Your LinkedIn Posts Are Polished. Can a Prospect Tell How You Think?
LinkedIn just replaced an AI tool that helped write posts with one designed to interfere less with the person writing them. For financial advisors, that’s a more interesting decision than it first appears. It suggests the platform itself sees a difference between helping someone communicate more clearly and helping them produce another polished paragraph that could have come from almost anyone.
On September 15, 2026, LinkedIn announced that it was replacing its “enhance your post” feature with proofreading tools intended to preserve the member’s voice. By September 20, Post Proofreader was being reported as available to eligible Premium users. September 20 is useful as an availability marker, not proof that LinkedIn flipped one worldwide switch that day. For an advisor staring at a half-finished post, the practical question is simpler anyway: if the software can clean up your writing, what are you giving it to clean up?
That reminded me of an earlier conversation I had with digital communications specialist Mitch Slater, well before this LinkedIn change. Mitch had spent decades in financial services and, by the time we spoke, had moved into digital communications, helping advisors think about how they show up online. One of his recurring concerns was what happens after a referral. Someone hears your name, looks you up, and starts forming an opinion before you ever speak.
That makes LinkedIn content more than a publishing chore. A prospect can already find your title, firm, awards, credentials, and probably the same market commentary they’ve seen from five other advisors. The harder part is giving them some evidence of how you think. Can you explain something complicated without hiding behind jargon? What do you care enough about to say in your own way?
A better proofreading tool may help with the final few inches of that job. It can’t decide what was worth saying in the first place.
LinkedIn Changed the Tool, Not the Responsibility
LinkedIn’s September change is easy to overread. The company did not announce that AI-written posts were banned, that every polished paragraph would be punished, or that using writing assistance would somehow tank an advisor’s reach.
The change was narrower. LinkedIn replaced “enhance your post” with a proofreading-focused tool intended to improve a draft without rewriting it into something that no longer sounds like the member. Social Media Today reported on September 20 that Post Proofreader was available to eligible Premium users.
The new tool starts from a different premise. You supply the draft first. The software can then suggest corrections, shorten it, or clarify the language, with the user reviewing the proposed changes. The machine is closer to an editor than a ghostwriter.
That product decision landed in the middle of LinkedIn’s broader campaign against what it calls AI slop. Earlier in August, LinkedIn said more than one million members had used its “Seems like AI slop” feedback option. The company also said content it classified as AI slop was receiving 40% fewer overall views than it had a few weeks earlier.
That number needs a fairly large asterisk.
It was LinkedIn’s own aggregate measurement of content the company classified that way. It was not an independent audit. It does not mean one AI-looking post gets hit with a 40% reach penalty. It does not tell us what happens to a particular financial advisor’s post, and it certainly does not prove that changing writing style will generate more prospects.
LinkedIn has also said that a single piece of feedback does not automatically remove a post or establish a policy violation. Distribution uses multiple signals. The feedback mechanism is one input, not a red button that destroys a post because somebody thought it sounded robotic.
More important, LinkedIn’s own guidance still allows people to use AI for editing, proofreading, tightening, and clarifying their writing. The company’s stated concern is whether the finished content reflects the member’s actual experience, perspective, and expertise.
For advisors, that distinction is more useful than arguing about whether AI itself is good or bad.
A financial advisor can absolutely use a tool to fix clumsy wording, shorten a rambling explanation, or catch something awkward before publishing. There is no prize for leaving a typo in a post to prove a human wrote it.
The responsibility that remains with the advisor is choosing something worth saying.
If the starting material is a generic paragraph about the importance of planning, diversification, preparation, discipline, or “navigating uncertainty,” cleaning it up will not make it more distinctive. It may become smoother, shorter, and easier to read. A prospect still may not learn anything about how that advisor actually thinks.
The platform can change the editing tool. It cannot supply the observation, judgment, client question, or explanation that gives the writing a reason to exist.
A Prospect Should Be Able to Hear You Before They Meet You
Mitch described the advisor’s problem in much more human terms. When someone refers you, the next step is rarely mysterious. The prospect looks you up.
Mitch’s question was simple: “What are they going to find? Are they going to hear your voice?”
He was talking about podcasts, video, LinkedIn, and the broader job of becoming visible before a conversation ever starts. The useful part of that idea is not that every advisor needs to become a content machine. Your public presence should give someone more than a list of credentials.
Credentials still matter. Awards matter. Experience matters. A prospect may reasonably want to know whether you have been doing this for 25 years, whether your firm is established, or whether other organizations have recognized your work. Those facts answer legitimate questions about background and credibility, but they do not show what it feels like to hear you explain something difficult.
They do not show whether you can take a confusing issue and make it understandable. They do not show whether you notice the concern underneath someone’s question or whether you can talk about money without making the person feel like they have walked into a vocabulary exam.
That connects with something else Mitch said about his years as an advisor. He called the business “a return on relationship more than an ROI.” He was describing a profession where the client is not only evaluating technical knowledge. They are also deciding whether this is someone they can talk to, understand, and keep talking to over time.
LinkedIn cannot prove that relationship in advance. A podcast cannot prove it either. A clever post certainly cannot. Content can, however, provide a small sample.
Imagine two advisors with similar experience, comparable credentials, and solid firms behind them.
One publishes a post announcing another industry recognition. There is useful information there. The prospect knows the advisor has been recognized professionally.
The other publishes a plain-English answer to a question prospects routinely ask before their first meeting. Maybe the advisor explains what to bring, what does not need to be perfectly organized yet, and what kind of decisions the first conversation is actually meant to clarify.
Now the prospect has learned something different. They have seen how the advisor organizes a problem. They have heard the tone used to explain it. They can see whether the advisor makes a complicated process feel manageable or piles on another layer of terminology.
The first advisor may be excellent. The second advisor may be excellent. The difference is simply what the content lets an outsider observe.
Mitch was especially irritated by advisor content that technically exists but does not help the person on the other side of the screen. He talked about blurry PDFs, constant self-promotion, and posts that amount to “I’m great, I won this, I’m terrific.” His complaint was not that advisors should hide their accomplishments. It was that those accomplishments can become the entire message.
An advisor may have decades of experience and good judgment. The LinkedIn profile proves they exist. The awards show that they have been recognized. Yet the prospect may still have almost no idea how that person approaches an actual problem.
If an advisor feeds a generic awards announcement into an editing tool, the result may become cleaner and more readable. If the advisor feeds in a useful explanation of a real client concern, the tool may clean that up too. What matters is what the advisor supplied before the editing began.
For anyone using LinkedIn as part of a referral process, that is more practical than trying to reverse-engineer the algorithm. Look at your recent content and ask what a stranger could actually learn about you from it. Ignore the formatting for a minute. Ignore whether it sounds polished.
What evidence does it give them about how you think?
Useful Content Starts Before the Writing
The hardest part of a useful LinkedIn post usually happens before anybody opens an editing tool.
Mitch’s advice started with paying attention to what clients actually care about. That sounds almost too obvious, but it changes where the raw material comes from.
An advisor sitting alone at a desk asking, “What should I post today?” has created a difficult problem. Now they need to invent a topic, decide what the audience might care about, find something intelligent to say, and package the whole thing at once. Paying attention to recurring questions gives you a better starting point.
Maybe clients keep asking what documents they should gather before a planning conversation. Maybe they are confused about what an advisor actually does between meetings. Maybe the same misunderstanding comes up whenever markets get ugly. Those are examples of the kind of recurring questions an advisor can notice in ordinary work, rather than topics invented from scratch because the content calendar says Tuesday needs a post.
Mitch’s frustration with blurry PDFs fits here. His alternative was deliberately simple. Instead of uploading a dense document and hoping somebody reads it, take out your phone and explain the three main points.
He was not making an algorithm claim. There is no evidence in our conversation that LinkedIn automatically rewards a phone video or punishes a PDF. His point was about communication. If the important information can be explained clearly, give the person a reasonable way to understand it.
That also leaves more room for the advisor to sound like an actual person.
One of Mitch’s own examples had almost nothing to do with finance. He described sitting at a piano and playing the opening music from Severance. He then used the show’s premise to ask people whether they would want to separate their work self from their personal self.
The piano mattered because it was real. Mitch plays. He watched the show. He recognized something current that interested him, then used it to open a conversation.
An advisor does not need to start playing television themes on camera. Personality can show up in much smaller ways. It may be the analogy you naturally use when explaining something. It may be a question that keeps bothering you after three client conversations in the same week. It may be the slightly odd example that makes a difficult idea easier to understand.
That is different from deciding that “authenticity performs well on LinkedIn” and manufacturing a personal detail because a marketing formula told you to.
The raw material should belong to you before a machine starts rewriting it.
This is also why Mitch is a poor fit for an anti-AI argument. Near the end of our conversation, he talked about taking AI classes, experimenting with ChatGPT, and trying to stay current as the technology changed. He was interested in using new tools, not protecting some imaginary pre-technology golden age.
That matters in financial services, where publishing also comes with rules that vary by firm and role. Mitch described his own persistence in getting podcasting accepted within his organization years earlier. His account was about working through a compliant process and eventually piloting the idea, not ignoring review because he thought the format was useful. An advisor can notice a recurring question, draft an explanation, use AI to tighten it, record a short version, or bring some personality into the presentation without treating a new tool as an exemption from the firm’s normal review process.
The advisor still has to listen closely enough to notice what deserves an answer, decide what they actually think about it, and explain it in a way another person can use. Better editing becomes especially valuable after that work has been done. It can remove clutter from something that already has a reason to exist.
Run the Name-Off Test on Your Last Post
Pick one LinkedIn post you published recently, remove your name, firm, title, photo, and anything else that identifies you, then read it again.
Could another financial advisor publish the same thing without changing much?
If the answer is yes, that does not automatically make the post bad. Some information is useful even when it is not distinctive. The problem is that a prospective client may finish reading without learning anything about why they would want to hear the explanation from you.
Take a hypothetical advisor writing about a first financial-planning meeting. The original draft says preparation is important, clients should gather relevant documents, and good organization can make the conversation more productive.
Nothing there is necessarily wrong. It is also easy to imagine hundreds of advisors publishing essentially the same paragraph.
The reader’s actual question is more concrete: What should I bring to our first meeting? Once the advisor starts there, the post has a job to do.
For illustration, the advisor might explain that someone does not need to arrive with a perfectly organized financial life. Bring the statements and documents you already have, make a note of anything you know is missing, and come prepared to explain what you want help deciding.
Compare what the two versions reveal.
The generic version tells the prospect that preparation is good. The revised version tells the prospect something about the advisor’s expectations. It suggests that the first meeting is not a test of whether the client has assembled the world’s cleanest binder. It gives the prospect a sense of what information matters and what kind of conversation the advisor expects to have.
That is useful even before the prospect decides whether to book anything.
It also gives the advisor more meaningful material to edit. AI can help shorten the answer, remove repeated language, tighten three points, or make an awkward sentence easier to understand. It should not invent the advisor’s experience, manufacture a client story, or add confident claims that the advisor cannot support.
This is where the name-off test becomes more useful than a vague instruction to “sound authentic.”
Take away the branding and ask what remains.
Does the post contain a question the reader genuinely has? Does the answer reflect something the advisor actually knows, notices, or explains in practice? Does it give the reader a small but useful understanding of what working through that issue might feel like?
The advisor can then decide whether text is actually the best format. Mitch’s suggestion was often much simpler than a big content-production plan: explain the main points into your phone. For some subjects, hearing a person explain an answer may reveal something that polished written copy does not. For another subject, plain text may be clearer. An audio clip may occasionally fit better. The choice should follow the material rather than a claim that one format always wins.
This exercise can be kept to four decisions:
- What real question is this post answering?
- What does the current draft actually tell the reader?
- What explanation can I supply from my own legitimate experience or expertise?
- Which format lets me communicate that explanation clearly within my firm’s normal review process?
None of this guarantees more views, more appointments, or special treatment from LinkedIn. Personality does not establish professional competence either. A human explanation still has to be accurate, useful, and appropriate for the advisor’s role.
What it gives the prospect is a small piece of evidence about how you take a question, think through it, and explain the answer. That evidence still matters if LinkedIn changes its ranking system again next month.
Make the Editing Tool the Last Step
For the next post, start one step earlier than the software.
Choose a real question you hear from clients or prospects. Write the answer the way you would actually explain it to someone sitting across from you. Then use the editing tool to clean up the wording, shorten the rough spots, or make the structure easier to follow.
If the source material already contains your judgment, explanation, and way of seeing the problem, editing can make it easier to read without stripping out the part that makes it yours. If the draft began as generic filler, cleaner wording will not suddenly reveal how you think.
Mitch’s earlier question still works as a useful test: can they hear your voice?
Not literally every time. You do not need a video attached to every post. The point is whether someone who finds you online gets any useful sense of how you think and communicate before deciding whether to speak with you.
For your next post, answer one question the way you would answer it for a real person. Clean it up afterward. When the next referred prospect searches your name, they should find more than credentials and a polished profile. They should find a useful example of how you explain the work.
Before a prospect ever speaks with you, your content gives them a sample of how you think. That’s something I kept coming back to after talking with Mitch Slater, who is also a co-author of Decisions, Courage, and Action. Pick up our book on Amazon to read Mitch’s contribution alongside the other co-authors.
About Mitch Slater
Mitch Slater spent decades in financial services before moving away from working as a financial advisor and into digital communication and media. In the source interview, he described helping advisors use podcasting, video, LinkedIn, and other formats to communicate more effectively and become easier for prospective clients to understand and find. His background also includes extensive podcasting, broadcasting, and advisor-focused media work. Connect with Mitch Slater on LinkedIn.
Media and interview inquiries: To request an interview about financial-advisor marketing and podcasting, contact Mitch Slater on LinkedIn.
Resources
Who Watches Meta’s AI Ads When the Marketer Needs a Break?
In a September 6 firsthand account, NewFrame Digital showed Meta AI creative recommendations that misspelled its Cliniverse brand and altered people in supplied imagery. Tyler Sinden published the examples while describing what his agency encountered during the ad-creation process. They were suggested creative variants, not proof that those altered ads ever reached customers, but somebody still had to notice the problems before approving anything.
The September example was not isolated from the broader supervision issue. On July 13, Business Insider reported that advertisers and agency executives were spending additional time checking AI-generated creative and campaign settings. Meta told the publication that advertisers are responsible for reviewing AI outputs. Automation may produce more of the work, but the decision to accept or reject it still lands on a person.
Stress and recovery coach Rita Ainsworth knows the working conditions behind that responsibility unusually well. After years in performance marketing and Meta advertising, she described an environment where the accounts did not stop running when the workday ended. Her experience raises a practical question as platforms automate more of the job: what actually needs checking, who owns that check, and when is it reasonable for that person to stop looking?
The AI Still Needs Someone Watching It
Meta’s advertising tools can generate or alter creative elements such as images and text, giving advertisers additional variations to review. For a media buyer, that can reduce some manual production while creating another job at the other end of the process: deciding whether what the system produced is acceptable.
Business Insider’s July 13 reporting drew on interviews with eight advertisers and agency executives. Some described spending additional time reviewing generated creative and confirming that unwanted enhancements or settings had not been applied.
That review work matters when a campaign is spending real money. A strange image or changed setting is not merely an AI curiosity if somebody is responsible for the account.
Meta told Business Insider that AI-generated material can contain mistakes and that advertisers are responsible for reviewing the output. The company also said its seed-image variation feature is off by default. Meta’s response itself leaves human review in the workflow.
The July reporting did not show one consistent resolution to the settings complaints. Some advertisers believed a bug affecting their clients had been fixed, while another agency executive reported a similar problem continuing. That disagreement belongs to the July reporting. There is not enough evidence to claim that the same problem remained unresolved for every advertiser through the October 3 cutoff.
The September account from NewFrame Digital showed a different and narrower problem. Sinden published AI creative recommendations that included a misspelled brand name, changed faces, and other alterations to imagery his agency had supplied. Again, these were recommendations encountered during creation. The source does not establish that the altered versions were published.
The examples still show why review exists. Someone looked closely enough to catch them.
For a media buyer, extra AI output can also mean extra material to inspect. Depending on the features being used, that review might include brand names, faces, claims, product details, formatting, or campaign settings. The exact list depends on the account and what the advertiser has enabled.
Meta’s September 29 announcement of Muse for Small Business adds separate context. Meta says Muse can connect with business accounts, analyze campaigns, prepare work, and assist with tasks across several business functions. The company also says nothing publishes, sends, or spends without approval.
Muse is not Advantage+ creative, and the announcement does not establish that Muse fixes the advertising problems described in July or September. What it does show is that Meta itself still describes human approval as part of a more automated business workflow.
That leaves agency owners and media buyers with a fairly ordinary management problem hiding inside an AI story. Someone still owns the decision.
When Checking the Account Becomes the Job
Rita described the environment in terms that will sound familiar to anyone who has managed live ad accounts: “Facebook ads don't stop running on the weekend, or if you want to take a holiday.”
In her case, the work did not stay inside office hours. Even when she was technically finished, she says she struggled to switch off from client accounts, poor performance, and what might need attention next.
Her experience eventually became much more serious. Rita described finishing a team call, opening a high-spend account she checked several times a day, and looking at numbers she normally understood.
“I saw the numbers, but I couldn't figure out what they meant.”
She describes that episode as part of a severe burnout that forced her out of the fast-paced advertising environment she had been working in. Her experience does not prove that constant campaign checking causes burnout in other marketers, and it does not suggest that Meta’s current AI products caused what happened to her years earlier.
It does show what continuous responsibility looked like for one person who actually did this work.
In our conversation about stress and performance, Rita also talked about high achievers who connect results with self-worth and feel uneasy when they stop producing. She recognizes that pattern in her own history and says she sees versions of it in clients she works with.
One version of that pressure is simple: if I stop, I will fall behind.
That can attach itself very easily to media buying because there are legitimate reasons to look at an account. Campaigns continue running. Money is being spent. Creative can change. Performance can move. A client can send a message that genuinely requires a decision.
The July and September reporting gives marketers more legitimate things to inspect when AI features are involved. Quality control is real work.
A different problem appears when the required review is finished and the account keeps getting reopened anyway.
A marketer may have a good reason to inspect a new creative variation, then check again later even though nothing material has changed. Another look can feel responsible simply because the account remains active.
That is where the operational problem and the personal one need to stay separate. Telling a marketer to relax while an unresolved campaign problem is sitting there is useless. Designing the job so one person is vaguely responsible at all hours is not a serious coverage plan either.
The review process needs enough definition that the person responsible can recognize the difference.
Give the Review Process a Stopping Point
The practical move I would make from Rita’s experience is to turn “keep an eye on the account” into something specific enough that a person can actually finish doing it.
Pick one active Meta campaign. Not the whole agency or every account you manage. Start with one campaign that currently matters and write down what its review job includes.
A short checklist is enough:
- What exactly needs to be checked?
- Who owns that review?
- When is the next planned check?
- What kind of change is important enough to interrupt that plan?
- Who handles a genuine problem if the primary person is unavailable?
This is my suggested application of Rita’s thinking, not a system she named or prescribed. The purpose is to document the responsibilities that already exist and expose the ones that are still vague.
Depending on the features being used, an AI-related review might include creative changes, brand names, product details, claims, faces, formatting, or important campaign settings. Another account may require a different list entirely.
There is no universal checking interval or spending threshold that makes sense for every advertiser.
A major launch may warrant much closer attention than a stable campaign. A solo operator may have nobody available to take over. An agency may have daytime backup but no formal overnight coverage. Those circumstances change what a reasonable review plan looks like.
Writing the plan down does not make those constraints disappear. It makes them harder to hide.
If nobody can cover an important problem, the owner may decide that a particular launch requires additional availability. That is an operational tradeoff. It is different from leaving every evening permanently open because something somewhere in the account might change.
Then look backward.
Take your last several unscheduled account checks and ask two questions: What new information appeared, and what decision did I make because of it?
Some checks will easily justify themselves. You caught a bad creative variation. Performance changed enough to require action. A client changed direction. Something happened, and looking at the account affected what you did next.
Other checks may show almost the same information you saw earlier and produce no different decision.
That pattern is worth examining.
Rita’s description of high performers helps explain why this can become difficult. She talks about the old feeling that if she stopped, she would fall behind. A job that already rewards vigilance gives that feeling plenty of legitimate material to work with.
The goal is not to classify every extra check as anxiety or every scheduled check as responsible. It is to know what job the check is supposed to perform.
Backup coverage matters for the same reason. A stopping point is easier to respect when responsibility has somewhere to go. For a team, that may mean another media buyer who knows enough about the account to handle a defined class of problems.
A solo operator has a harder decision. There may be periods where the choice really is between staying available and accepting that some noncritical issues will wait. That tradeoff needs account-specific judgment. Pretending the risk does not exist would be just as unrealistic as pretending someone can remain continuously available without a cost.
Rita talks about alternating concentrated work with recovery instead of continually pushing past signs that she needs a break. Applied here, that idea does not repair unreliable software or replace adequate staffing. The campaign still needs whatever review the account genuinely requires.
It does give the marketer a reason to define when that review has been completed for now.
The Person Reviewing the AI Is Part of the System
More automation does not remove the need to decide who is accountable for the output. If anything, the Meta examples make that responsibility easier to see.
The useful next move is to define the next required review before you close the account. Decide what you are waiting to learn or verify. Decide what would justify looking sooner. If somebody else can cover a genuine problem, make that ownership explicit.
Rita’s experience gives that operational decision some weight. She knows what it was like to keep carrying the account after the formal workday ended, and she knows what it was like to reach a point where familiar dashboard numbers stopped making sense to her.
Meta’s AI tools still require human review in the situations described here. The more useful question at the end of each review is whether another look at the dashboard would actually change what you do.
Deciding when a campaign needs your attention also means deciding when you’ve done enough for now. Rita Ainsworth is one of my co-authors in Decisions, Courage, and Action, and her experience of carrying work into every spare hour stayed with me. Read her contribution in the book on Amazon.
About Rita Ainsworth
Rita Ainsworth has spent more than a decade in performance marketing, including Meta advertising, and now also coaches high achievers on stress, recovery, and the pressure to stay constantly productive. She works primarily with entrepreneurs and performance marketers through coaching and training. Learn more at ritaainsworthcoaching.com. Connect with Rita Ainsworth on LinkedIn.
Media and interview inquiries: To request an interview about burnout and sustainable work habits, email hello@ritaainsworth.com.
1673: Digital Marketing Strategy That Converts with Stacey Kehoe
AI is accelerating digital marketing, but more automation does not always mean more growth. In this episode, Stacey Kehoe, founder of Brandlective Communications, explains why businesses should focus on their goals instead of chasing every new AI tool or marketing trend. She recommends using AI for administrative tasks while preserving human connection where trust, relationships, and decision-making matter. A recruitment agency’s experiment with AI recruiters illustrates how processing more candidates does not guarantee successful placements. Stacey’s core question is whether a tool creates meaningful value and moves the business closer to its objectives. Her practical advice is to start with the fundamentals, keep things simple, and build from there.
Stacey explains why businesses need a clear marketing strategy before choosing tactics such as social media, email, content marketing, paid ads, or web development. Strategy sets the direction and defines why the business is pursuing its goals, while tactics are the activities that help move it forward. She urges business owners to prioritize qualified leads, conversions, and revenue over vanity metrics like likes, shares, and impressions. Brandlective works with clients on everything from a specific part of the marketing funnel to a full-service digital marketing strategy. Its focus is helping growth-oriented businesses turn digital activity into a qualified pipeline and measurable business results. Kehoe also shares how to niche marketing messages while diversifying markets, services, and lead sources to reduce business risk.
Purpose also plays a central role in Stacey’s approach to building a business. Brandlective’s 1 Million Days of Impact initiative connects everyday business activities with social contributions, including clean water access and business training for women in East Africa. At the time of the conversation, the agency had delivered 529,000 days of impact toward its 2030 goal, and Stacey says the mission supports employee morale and attracts prospective team members. She also discusses her book, Get Online: Six Simple Steps to Building a Digital Marketing Strategy for the Non-Tech Savvy. Brandlective works with businesses seeking growth, particularly in the sales and healthcare sectors, and keeps revenue and conversion at the heart of its marketing work. Listen for practical ideas on AI, digital marketing strategy, business diversification, and building growth with purpose.
Quotes:
“It doesn’t matter if you can be a one-man band, you can be a multi-million-pound business, you can still make an impact.”
“Get the basics done. Then go do all your fancy stuff that you want to do afterwards. But just get the fundamentals in place, like 101, back to the beginning.”
“Strategy should be your plan, and it’s where you’re going and why you’re going there.”
Contact Details:
- Ready To Grow Your Brand? Let’s Make It Happen with Brandlective
- Connect with Stacey Kehoe on LinkedIn for Smarter Brand Growth Ideas
- Follow Stacey Kehoe on Instagram for Fresh Ideas to Grow your Brand and Business
- Don’t Let Your Brand Fall Behind: Follow Stacey Kehoe on Facebook Today
- Ready for Ideas that can Change How You Grow? Follow Stacey Kehoe’s Podcast on Spotify
- Want Smarter Growth Ideas? Follow Stacey Kehoe on X and Stay Ahead
- Get Online: Six Simple Steps to Building a Digital Marketing Strategy for the Non-Tech Savvy On Amazon
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Your AI Performance System Is Watching More. Are Your Employees Deciding Less?
Multiverse used to have managers observe roughly one teaching session per instructor each month. In September 2026, the company introduced Compass, an AI system designed to review every teaching session instead. Multiverse says the broader view can help managers identify quality concerns sooner, spot development opportunities, and coach instructors using more evidence than an occasional observation could provide.
That sounds reasonable. A manager responsible for quality should want enough information to know whether someone is doing the job well. Multiverse also says Compass does not make employment decisions and that managers are expected to review its findings before deciding whether anything needs to happen. At the same time, instructors interviewed by The Guardian described a different concern. Some said the constant analysis made them more conscious of what the system might flag, including teaching choices they believed were reasonable responses to what was happening in a session.
That tension reminded me of something executive coach and neuromanagement expert Carlos Davidovich told me when I interviewed him for Marketer of the Day. Carlos works with leaders who complain that their employees will not make decisions on their own. Then he looks at the rules those same leaders have created and sometimes finds the contradiction. They tell employees to think for themselves while still insisting on being consulted before a decision is made.
Carlos calls the result upward delegation. Eventually the employee learns that sending the decision back to the boss is safer than making the call.
An AI performance system can create a more sophisticated version of the same management problem if the person using it is not careful. More information gives you more chances to catch a genuine problem, but it also gives you more chances to intervene in decisions that may simply be different from the way you would have handled them.
That is why the Multiverse story is more interesting than another argument about whether AI belongs at work. The company has a legitimate reason to want better visibility into teaching quality, and the instructors raising concerns have a legitimate reason to care about how that visibility will be used. The management question sits between those two positions.
If you can see more of what your employees do, what exactly are you teaching them every time you respond?
More Data Can Solve a Real Management Problem
Carlos Davidovich uses the term “upward delegation” for a pattern that sounds backwards until you have watched it happen inside a team.
A manager says, “I want you to think for yourself.” Then the same manager adds another rule: “before making any decision, ask me.”
Carlos’s point is that employees respond to the rules that actually govern the workplace, not the independence the manager says they want. If the boss expects to approve the decision anyway, the employee eventually learns to move the responsibility back upstairs. As Carlos put it, “I delegate everything to my boss.”
That does not require a controlling manager with bad intentions. A manager can create the pattern while trying to stay informed, protect quality, or prevent expensive mistakes.
This is what makes the Multiverse case useful. According to the company, managers previously observed roughly one teaching session per instructor each month. Compass can review every session, identify possible quality concerns, and suggest areas for development. Multiverse says managers still review the findings and decide what, if anything, should happen.
One observation per month is a small sample. You may see an unusually good session or an unusually bad one. You may miss a recurring communication problem for weeks. If you are responsible for teaching quality, waiting for a complaint or hoping your occasional observation catches the right moment is not much of a system.
Earlier in 2026, Ofsted had also identified legitimate organizational issues at Multiverse, including high staff workloads and weaknesses in several areas, while describing coaches and instructors as skilled teachers. That report came before Compass and does not tell us whether Compass solves those problems. It does establish that management had real operational issues to address rather than an invented excuse to monitor employees.
More visibility can help with that.
The danger starts when better visibility quietly becomes a requirement that every employee decision line up with what the manager, rubric, or system would have chosen.
Carlos described leaders who complain that team members keep bringing them problems. In some cases, he finds that the leader helped create that behavior. The employee has learned that making the decision carries risk while escalating it transfers that risk to someone else.
More information does not automatically create that situation. A manager can receive far more performance data and still give employees substantial room to exercise judgment.
A recurring failure deserves attention. A choice that harms a customer, student, project, or team may need correction. A pattern that shows someone does not understand the job may require coaching.
A different method is not automatically any of those things.
Once software can identify far more deviations than a manager would ever have noticed personally, the manager has to decide which differences actually matter.
The Flag Is Where the Management Decision Starts
Multiverse’s strongest argument for Compass is that a flag is not supposed to be the final judgment. The company says managers review the system’s findings, compare them with what they know about the instructor and the session, and decide whether anything actually needs to happen.
A performance system can surface a possible problem. It can point to a pattern, a deviation, or a behavior that deserves attention. It still cannot tell a manager, by itself, whether the employee made a bad decision, used a reasonable alternative, or simply did something the system was not expecting.
The Guardian reported that Compass can assign risk statuses, confidence scores, and written comments for managers. Instructors who spoke with the paper said some flags could involve choices they considered legitimate adaptations to what was happening in the room.
That does not prove the system is wrong. Employee disagreement does not automatically make an assessment unfair, either. A teacher can sincerely believe an adaptation was useful and still have made a poor decision.
A manager can also prefer a different method without the employee’s method being wrong.
Suppose a system identifies a session as risky because an instructor departed from the expected structure. The useful question is not simply whether the instructor followed the preferred method. The manager has to examine what happened as a result of the choice.
Did the learner become more confused? Did the instructor skip something important? Did the change improve understanding? Was the instructor responding to a question or misunderstanding that the standard approach was not solving?
The evidence may support the flag. It may also show that the instructor exercised sound judgment.
Carlos has described leaders creating situations they later dislike without realizing how their own behavior contributed to them. Applied here, that gives managers a useful question to ask themselves. If every flag becomes a correction, what are employees likely to learn about making an unusual decision next time?
That is an application of Carlos’s thinking, not a finding about Multiverse.
Multiverse says human managers remain responsible for interpreting Compass findings. Keeping a person in the loop only helps if that person actually interprets the evidence instead of treating the system output as a verdict.
There was also an important limitation in the September rollout. Multiverse said instructors had been introduced to Compass while reports were still visible only to managers. Employee access was planned for October. As of the October 3 research cutoff, that planned access should not be treated as something already completed.
Transparency can change the quality of a review. If an employee can eventually see the report, understand the standard, explain the reasoning behind a decision, and challenge a mistaken interpretation, the manager has more information to work with.
None of that gives the employee the final say. Managers still set standards, identify weak performance, and correct decisions that create harm.
The useful distinction is between correcting a bad decision and correcting a departure from the manager’s preferred method.
Managers Can Train Employees to Stop Deciding
Take one recent performance correction, approval request, or decision that came back to you for review.
Start with the outcome the employee was responsible for producing. What was supposed to happen? What standard mattered? What would have counted as a real failure?
If the employee’s decision clearly violated a safety rule, created a legal problem, harmed a customer, skipped a required control, or produced a bad result that could reasonably have been anticipated, the response is fairly straightforward. Correct it, explain the boundary, and make the expectation clear.
The more useful exercise begins when the outcome is less obvious.
Suppose an employee chose a different method than the one you expected. Maybe they answered a customer in a different order, changed part of a presentation, handled a client objection without checking with you first, or adjusted a process because the usual approach was not working.
Now reconstruct the decision.
What did the employee notice at the time? What information were they working with? What were they trying to accomplish? Did they understand the standard they were operating inside, or did they simply guess?
An employee who misunderstands a requirement needs something different from an employee who understands the requirement and makes a reasonable judgment call inside it.
Then look at the evidence. Did the choice actually create harm? Did it improve the result? Is there enough evidence to know either way? Sometimes the honest answer is that the outcome was acceptable but the manager would have done it differently.
That is where control can creep in.
Carlos’s broader point is that leaders can create dependence without meaning to. If employees learn that every meaningful variation will be pulled apart afterward, they can start protecting themselves by escalating more decisions before they act.
Next, establish the employee’s authority.
What was this person actually allowed to decide without approval?
If the answer is vague, telling someone to “use good judgment” does not help much. The employee needs to know where the boundary sits.
You might decide that the employee can change the method as long as the outcome, safety requirement, customer promise, and budget stay within agreed limits. Another decision may require approval because the consequences are harder to reverse.
Autonomy does not mean removing standards or escalation boundaries. It means making those boundaries usable.
Your response should change depending on what the evidence shows. If the employee misunderstood the standard, teach the standard. If they ignored a known boundary, address the decision directly. If their judgment was reasonable, say so even if you would have chosen another method. If the situation exposed a gray area, clarify what they can decide next time without asking.
A useful correction should leave the employee with a better understanding of their authority than they had before.
Otherwise, the review may solve today’s disagreement while creating tomorrow’s approval request.
Review the Decision, Not Just the Deviation
Changing a control-heavy management pattern is harder than announcing that employees now have more freedom.
Carlos told me that when leaders change their behavior, they should expect some resistance. The team has already learned how the old system works. If employees have spent months or years being corrected for making decisions without approval, one meeting about “empowerment” will not erase that history.
His advice is practical. Set new expectations explicitly, then behave consistently with them.
A manager might tell the team that certain problems now belong to them to solve. They should make the decision, act on it, and keep the manager informed rather than stopping to request approval every time.
The difficult part comes when someone actually does that.
The employee makes a reasonable decision without checking first. The method is different from what the manager would have chosen. Nothing important was violated, and the result is acceptable.
If the manager immediately steps in and rewrites the decision anyway, the old rule is still in force.
The employee hears the announcement about autonomy but experiences something else: you can decide for yourself as long as you make the decision I would have made.
Detailed performance monitoring makes this harder. A system that surfaces more choices, deviations, and possible concerns gives a manager more opportunities to intervene. The manager has to become more disciplined about deciding which interventions are necessary.
Weak performance still needs to be addressed. A decision that crosses a known boundary, creates unnecessary risk, or repeatedly produces poor results should not be protected in the name of autonomy.
The harder test comes when the decision is defensible.
Maybe the employee solved the problem differently. Maybe they took a route the manager did not expect. Maybe the evidence is mixed and the manager simply has more experience and would have chosen another approach.
If you want employees to develop judgment, some reasonable decisions have to survive your disagreement.
The employee may also keep asking permission for a while. That hesitation does not automatically prove they are incapable of deciding. They may be testing whether the rules have actually changed.
When someone brings you a decision that already sits inside their authority, resist the reflex to take it back. Ask what they think should happen and what evidence they are using. Confirm the boundary if necessary, then leave the decision where you said it belongs.
The same principle applies afterward. Review the reasoning and the result without automatically treating a different approach as a mistake. When the judgment was reasonable, say so plainly. When something needs correction, explain which boundary or outcome created the problem.
Carlos’s phrase “let people think” sounds simple. In practice, it requires a manager to tolerate some decisions they would not personally have made.
What Does the Employee Learn From Your Review?
Multiverse’s move from roughly one observed teaching session per instructor each month to AI-assisted review of every session gives managers far more opportunities to notice something and step in.
That makes the quality of the response more important.
When you review a flagged decision, look past whether the employee did exactly what you expected. Determine what outcome mattered, what authority the employee actually had, what information was available at the time, and whether the choice caused harm or represented a reasonable variation.
Then make the next boundary explicit.
If the employee crossed a line that matters, tell them where it is and why. If their reasoning was sound, resist the temptation to correct the method simply because you would have done it differently. If the situation exposed a gray area, decide who owns that call the next time it happens.
You can collect more data, review more work, and catch more deviations than before. The useful measure is what happens afterward.
The real test comes the next time that employee has to make a judgment call and you are not in the room.
The next time your employee makes a reasonable decision differently than you would, you get to decide whether their authority holds. Carlos Davidovich is one of my co-authors in Decisions, Courage, and Action, and our conversation left me thinking about how often managers take that authority back without noticing. You can read Carlos’s contribution by picking up the book on Amazon.
About Carlos Davidovich
Carlos Davidovich is an executive coach and neuromanagement expert who works with leaders and management teams internationally. His work focuses on applying neuroscience concepts to practical leadership problems, including communication, decision-making, micromanagement, reframing, and employee autonomy.
He is the author of Five Brain Leadership: How Neuroscience Can Help You Master Your Instincts and Build Better Teams and has more than 20 years of executive coaching experience across Europe and North America. Robert Plank interviewed Carlos for Marketer of the Day episode 1648, “Neuroscience Leadership and the Five Brains with Carlos Davidovich.” Connect with Carlos Davidovich on LinkedIn.
Media and interview inquiries: To request an interview about neuroscience and leadership, contact CFEG’s media team and request Carlos Davidovich.
Resources
Multiverse: How We’re Using AI to Invest in Coach Development
The Guardian: Teachers at Euan Blair Firm Report Stress After AI Used to Rate Their Work
Marketer of the Day Episode 1648: Neuroscience Leadership and the Five Brains with Carlos Davidovich
MacKenzie Scott Published Fiction Again. What Should Returning Writers Revive?
On August 8, 2026, MacKenzie Scott started publishing her first published novel in 13 years, one installment at a time on Substack. Last Days in Two Nations (A History in Eighteen Minds) continued through an August 31 epilogue, turning a long gap between published novels into a concrete decision. She chose a format and put another work of fiction in front of readers.
Most returning writers are dealing with a much smaller and messier version of that decision. There may be an old manuscript on a hard drive, poems from 20 years ago, half-finished essays, a newsletter that quietly died, or a folder full of work that once mattered enough to save. The interesting question is what, if anything, is still worth bringing back.
That is where my conversation with poet WL Gertz became useful to me. WL Gertz described himself as being “back on the scene after a 50 year hiatus,” but his story is different from MacKenzie Scott’s 13-year gap between published novels. He said he wrote little for a long period, then returned with poetry collections that deliberately combine work from the early 1970s with poetry written in the last two years.
What caught my attention was that WL Gertz did not look at the older work and decide it belonged to some completely different person. He could still recognize himself in it. His style, as he put it, “really hasn’t changed in 50 years.” The bizarre and surrealistic qualities were still there, even though decades had passed and the person doing the reading was obviously older.
That creates a more interesting problem than simply asking whether old writing is good or bad. Some of it may be weak. Some may be dated. Some may contain one strange line, image, argument, or rhythm that still feels more alive than the polished material you could write today.
Returning to creative work starts with figuring out what still deserves another look.
A Return to Publishing Is Only One Decision
MacKenzie Scott’s August release is useful partly because the publishing choice was so specific. She chose Substack as the delivery channel and released the novel in installments during August. For a returning writer, that is a much more practical thing to study than the surrounding celebrity attention.
Access to publication has changed. That does not make all the other parts easy. Getting readers is a separate problem. So are receiving favorable reviews, building an audience that returns, and earning enough from writing to support yourself.
MacKenzie Scott already had enormous name recognition before the first installment appeared. An ordinary writer should be careful about treating the attention around her release as evidence of what Substack, self-publishing, or any other platform will produce for someone without that built-in recognition.
A platform can give you somewhere to publish. It cannot manufacture interest in the work simply because you clicked Publish.
The later criticism makes that distinction even sharper. On September 27, Business Insider published a review by Eve Sneider that was sharply critical of the novel’s storytelling and presentation. That is one critic’s judgment, not a final verdict on the book, but it is a reminder that putting work into circulation does not control the response.
Direct access to readers also does not remove the need for editing. Fewer logistical barriers between writer and reader do not make every draft ready, every strange choice effective, or every old piece worth reviving.
That matters if you have been away from creative work for years. You can spend months wondering whether you are still a writer, whether the market has changed too much, whether your old material matters, or whether you missed your chance. None of those questions has to be settled before you make a smaller decision about one piece of work.
WL Gertz talked about the new environment in practical terms. He remembers when finding obscure poetry, music, literary criticism, or older cultural material took much more effort. In the interview, he talked about the web making that material readily available and specifically mentioned Substack as one of the newer places where writers can publish and readers can discover their work.
He did not say that he personally publishes on Substack. His broader observation was about access. Writers have more ways to put work in circulation than they did when he was young and, as he said, had no idea how to market himself.
That difference is easy to underestimate if you stopped publishing years ago.
A writer coming back after a long absence may no longer be facing the same gatekeeping problem that existed when the work was first written. The harder editorial question can become whether the material deserves publication in its current form.
WL Gertz’s own return helps make that distinction useful. After a long hiatus from the scene, he selected older work, put it alongside newer work, and continued writing.
For an ordinary writer, the sensible scale is one piece and one bounded decision. MacKenzie Scott’s audience is not the model. The useful part of her example is that a return to publication can be an identifiable act rather than a declaration about the rest of your creative life.
Old Work Can Still Sound Like You Without Still Being Good
WL Gertz’s return is useful because he did not treat the older material as something that had to be disguised as new. He described Broken Dreams and Land of Forgotten Fish as collections that clearly separate poems from the early 1970s from work written much more recently.
He also included material connected to dreams magazine, the publication he was involved with in New York that mixed poems, manifestos, literary criticism, and writing about rock and roll.
The older work was not being passed off as if it had all been written yesterday. The newer work did not need to imitate the past.
What surprised WL Gertz was the continuity. Talking about his poetry, he said his style “really hasn’t changed in 50 years.” He described that style as bizarre and surrealistic, and he said he has never liked poetry that feels obvious or predictable.
For a returning writer, that creates a better question than simply asking whether something is old.
You may read something you wrote 15 or 30 years ago and immediately see problems. The sentences are too long. The argument wanders. The references have aged badly. A joke no longer lands. A paragraph explains something three times because you did not yet trust the reader to get it.
None of that makes the entire piece useless.
There may also be something in it that you would have trouble reproducing now. Maybe it is an image you would not think of today. Maybe it is a strange turn of phrase. Maybe it is a point of view you had before years of professional writing taught you to smooth every rough edge.
WL Gertz’s experience is interesting because he recognized the same creative fingerprint across decades. That does not make his early poems automatically good. He is quite blunt about the fact that there is bad poetry in the world, including plenty of it online.
What survived was recognizable character.
That distinction gets lost easily when you return to your own work. Writers can become protective of old material because it feels personal. They can also attack it because it is embarrassing to encounter an earlier version of themselves on the page. Either reaction can make editing harder.
Something coming from an earlier version of you does not make it precious or disposable.
The way WL Gertz assembled his collections provides another useful possibility. Older and newer work can sit next to each other without pretending there was no gap. Time itself can become part of the context.
A returning writer can use that idea without copying his format. An old essay can retain its original date. A poem can be presented with a short note explaining when it was written. An abandoned chapter can supply one scene or argument for something new. A rough piece can be revised instead of preserved intact.
The useful distinction is between identity and execution.
You may recognize your voice in an old piece while admitting that half the sentences need work. You may still like the central idea while deciding the structure has to go. You may find one paragraph that feels unusually alive sitting inside ten pages you never want anyone to see.
At that point, nostalgia has done all the work it can do. Editing has to take over.
The harder editorial problem is deciding whether the strange thing is worth preserving or whether you are defending a weak passage simply because you wrote it years ago.
Keep the Strange Line. Fix the Weak One.
An old piece can contain one sentence you still like and three paragraphs you no longer trust. That is a better starting point than declaring the whole thing brilliant or embarrassing.
WL Gertz gives us a reason to take unusual language seriously. He said he dislikes obvious poetry, described his own work as bizarre and surrealistic, and recognized those qualities in writing separated by decades.
That supports preserving something unusual when it is doing real work. Confusion by itself is not proof of depth.
A strange image can make a reader stop because it opens a new association. A confusing passage can make a reader stop because the sentence has no direction. Those may both feel unconventional on the page, but they are different editorial problems.
Suppose you are rereading an old essay and find one image you would never think to write today. It is specific, odd, and memorable. The paragraph around it circles the same point three times and never quite arrives anywhere.
You do not have to preserve the paragraph to save the image. Keep what has life in it and rebuild the argument around it.
Or suppose an old poem depends on a reference that made immediate sense when you wrote it but now requires context. Changing the poem is only one option. You might preserve the original language and add a short note explaining when it was written or what the reference meant at the time.
That is different from quietly rewriting the past until the old work sounds exactly like something you would produce today.
The same reasoning applies to voice. A younger version of you may have been less polished but more willing to say something awkward, funny, blunt, or unfashionable. Years of professional writing can improve structure while also sanding away habits that gave the work personality.
The goal is not to imitate your younger self. It is to notice whether the old piece contains something distinctive that no longer appears naturally in your newer work.
WL Gertz’s resistance to obvious poetry is useful here as a prompt rather than a rule. Look at the piece and ask what still feels specific enough that another writer probably would not have written it the same way. Then ask whether the surrounding material earns its place around that stronger material.
A distinctive sentence does not rescue a weak argument. A personal memory does not excuse ten pages of repetition. An unusual metaphor does not make a piece finished. You can preserve individuality while cutting aggressively.
One practical way to inspect an older piece is to read it once without editing and mark only what still catches your attention.
Read it again looking for the opposite. Find the passages that are vague, repetitive, unfinished, or dated in a way that creates confusion. On those first two passes, resist the urge to decide the fate of the entire piece.
Then decide what kind of object you actually have.
Maybe the original is mostly sound and needs revision. Maybe its original date and context are important enough that you should preserve them. Maybe one section belongs inside something new. Maybe the only thing worth carrying forward is a single image or argument. Nothing surviving inspection is also a legitimate result.
Opening an old file does not create an obligation to rescue it.
Once you know what is actually there, the decision shrinks to a manageable size. You can decide what happens to this one piece without having to decide whether you are returning as a writer forever.
Publish One Thing Before Planning the Next Ten
Once an older piece has survived that inspection, the next move should fit the material you actually have.
A piece that is mostly sound may deserve a real revision and a publication date. If its age is part of what makes it interesting, its original context may belong beside it. If the whole thing no longer works but one line still has energy, that line can become the beginning of something new.
There is no reason to turn this into a tour of publishing platforms. The important choice is the form that lets you complete this particular piece and put it somewhere another person can encounter it.
For one writer, that might be a revised essay. Another might record a short reading. Someone else may take one usable passage from an old draft and write forward rather than spending weeks repairing a piece they no longer believe in.
WL Gertz’s experience matters here because publication changed what he wanted to do next. He said publishing encouraged him to keep writing, then described the contrast with the long period before his return: “I didn’t write much, I would say for a long time, but now I really want to write again.”
That is his experience. It is not a promise about what publication will do to your motivation.
Your piece may attract very little attention. Finishing it may generate three new ideas, or none. You may discover that you loved revisiting the material but have no interest in returning to a regular writing schedule.
All of those outcomes give you more information than leaving the same file untouched for another five years.
A public deadline can help if you tend to keep revising indefinitely. That is simply an option. Pick a date close enough that you have to make decisions and far enough away that you can do the work properly.
Then keep the assignment from growing.
One revived essay does not require a 12-month content calendar. One rediscovered poem does not require reopening 200 files. A line worth saving does not obligate you to rescue the notebook around it.
Give the piece a boundary and complete the decision you made about it. That is enough to find out whether finishing this one leaves you wanting to write another.
The next decision can stay small.
WL Gertz’s return was not limited to looking backward. He brought earlier work and newer poetry into circulation, recognized parts of himself across that distance, and found that publishing increased his desire to write again.
Give that one piece an honest outcome, whether that means finishing it for readers, using part of it to make something new, or deciding that it has already done all the work it needed to do.
You may have one old piece worth finishing, even if you’re still unsure what comes after it. WL Gertz brought older work back into circulation and found himself wanting to write again, and he’s also one of my co-authors in Decisions, Courage, and Action. Pick up the book on Amazon to read his contribution.
About WL Gertz
WL Gertz" class="wp-image-263741"/>WL Gertz is a poet whose books Broken Dreams and Land of Forgotten Fish combine work from the early 1970s with more recent poetry. His writing draws on travel, New York’s literary and music culture, and the surrealistic style he says has remained recognizable across decades. Earlier in his career, he was involved with dreams magazine, which featured poetry, manifestos, literary criticism, and rock and roll writing. Connect with WL Gertz on LinkedIn.
Media and interview inquiries: To request an interview about poetry and returning to writing, use his contact form.
Resources
1672: Medieval Adventure Novels and Poetry with Stewart T. Monti Sr.
Meet Stewart T. Monti Sr., a prolific author whose background includes graduating from the U.S. Air Force Academy, flying C-130s for 2,000 hours, and earning two master’s degrees. In this episode of Marketer of the Day, he shares how a love of storytelling led him to write historical fiction, poetry, and books that encourage readers to believe in themselves. The Sir Edward Saga began with stories Monti wrote for his son’s Dungeons & Dragons games. Those early ideas grew into Mary and Sir Edward, a tale set in 12th-century England about a courageous young girl who helps a wounded knight. Mary’s compassion leads her family into danger and sets in motion events that shape the future, showing how one good deed can have far-reaching effects. Monti says the story blends medieval adventure with historical inspiration and the Golden Rule. After the book attracted interest from a Canadian company that developed a screenplay adaptation and producer pitches, advice to write a sequel helped the saga expand.
In Hillary and Sir Edward, Mary’s daughter Hillary grows up learning to cook and sew as well as blacksmithing, riding, and swordsmanship. When Sir Edward needs a page, Mary suggests Hillary, who disguises herself as a boy and begins training at King Henry II’s castle. Hillary’s journey challenges expectations about what girls could do in medieval England and celebrates courage, skill, and determination. She later rides in a jousting tournament in Sir Edward’s place when he is ill. The story also draws on the historical conflict between Henry II and his sons, as Hillary helps defend the king and is made a knight. Monti describes the book as an inspiring adventure for young readers who want to pursue what they believe they can accomplish. Its message is that, with self-belief and persistence, people can find a path toward goals others may consider impossible.
The third installment, Hillary on Crusade, follows the young knight Hillary and her squire Geoffrey as they join Richard the Lionheart on the Third Crusade. Monti says he is finishing the book for the Frankfurt Book Fair in Germany, scheduled for October 7–11, and that Chinese and Spanish translations and audiobook editions are planned. His collection 135 Poems for Today brings together poetry about everyday life, faith, love, birthdays, pets, occupations, and other subjects. Monti shares that he began writing poems for a men’s prayer group and also gathered poems to share through his Lions Club service. He reads a poem about the work and commitment of police officers, illustrating how his writing reflects people’s lives and service. The conversation also covers his marketing efforts, from YouTube and Facebook to billboards, conventions, and book fairs, including a recent showcase in Beijing. Readers can find information about the books, trailers, merchandise, and social media through Monti’s author website and look for his work at Amazon, Barnes & Noble, and the Frankfurt Book Fair display from Explora.
Quotes:
“If you believe in yourself, you can do it. That is what is important. Find the way to make it happen. Find the path that works for you.”
“Sometimes one good decision can create consequences far greater than we ever imagined, even changing the course of history.”
“I just enjoy poetry, and I like to write poems for people. It’s just a little knack I have, I guess.”
Contact Details:
- Discover the Stories Behind the Poetry: Explore Stewart’s Books
- Follow Stewart T. Monti Sr. on Instagram for Poetry, Stories, and Inspiration
- Join Stewart T. Monti Sr. on Facebook for Stories, Poetry, and Life’s Deeper Moments
- Mary and Sir Edward on Amazon
- Hillary and Sir Edward: A Steadfast Narrative for Readers of All Ages on Amazon
- Mary and Sir Edward: A Medieval Narrative for Readers of All Ages on Amazon
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When Employees Say the New System Is Making Work Worse, What Should a Manager Check First?
A scheduling system can look successful on a dashboard and still give a manager a very real problem when the next schedule goes live. On October 2, 2026, WIRED reported that nurses at HCA Healthcare described problems with Timpani, the company’s AI-assisted scheduling platform developed with Palantir. Some nurses said schedules ignored preferences or left them spending additional time trading shifts and trying to get problems corrected. HCA pushed back on the broader criticism, saying nursing leaders retain final authority over schedules and pointing to improvements it says Timpani has produced across the system.
That disagreement creates a harder management question than deciding whether the technology is good or bad. Dr. Julie Donley spent decades in psychiatric nursing and nursing leadership before becoming a leadership coach, and in our conversation she kept returning to the human side of change. Her earlier thinking offers a useful way to examine this situation, even though we weren’t discussing HCA or Timpani specifically.
A manager can have solid rollout numbers in front of them and still have an employee describing a specific problem that needs an answer. Sometimes the employee is struggling with change. Sometimes training, support, or the local process is the issue. Sometimes the system produced an incorrect result. The manager has to find out which one happened before turning a complaint into a story about someone’s attitude.
A Good Rollout Metric Can Still Miss a Bad Tuesday
HCA has reasons to describe Timpani as a successful rollout. In a company-supported June 2026 strategy paper later republished by HCA, the company reported that the platform had expanded to more than 130 hospitals and more than 1,200 nursing departments. HCA also said creating a schedule, which could consume much of a nurse manager’s day, had dropped to roughly two or three hours per scheduling cycle. The paper reported improvements in skill mix and a decline in turnover in departments using the system.
Those are company-reported results, but they still matter. A manager looking at them would have a reasonable basis for believing the technology is doing useful work.
Nurses interviewed by WIRED described a different part of the experience. They reported specific scheduling problems, including preferences they said were overridden and patterns that left them trading shifts or trying to get schedules corrected. Their accounts do not establish that Timpani is failing throughout HCA. They do show that broad performance numbers can miss a problem occurring on one schedule, in one department, for one employee.
HCA’s response matters too. The company said Timpani does not remove nursing leadership from the process and that local nursing leaders retain final authority over schedules. If the human manager still has final authority, what happens when an employee says the schedule is wrong?
A company may measure how quickly the first version of a schedule gets produced while an employee experiences the process through the calls, messages, trades, corrections, and approvals required afterward. A system can save substantial time overall while still producing an exception that takes too long to fix. A nurse can also be frustrated by a new process without that frustration proving the software is broken.
For the manager, the useful unit of analysis is smaller than the rollout report.
What did the employee request? What did the system produce? What policy or staffing requirement affected the result? Who had authority to change it? How long did the correction take?
Those answers tell you more about the immediate management problem than either a company-wide success metric or a broad complaint about automation.
Resistance Is a Possibility, Not a Diagnosis
Resistance is real. People get attached to familiar routines. They can dislike a new process because it feels awkward, unfamiliar, or slower at first. HCA’s earlier account of the Timpani rollout discussed adoption and described nurses as creatures of habit in the context of introducing the system.
Julie has a useful way to think about that kind of difficulty. In our conversation, she described her HARD framework, with HARD standing for “habits attachment resistance and discouragement.” She developed it while trying to understand why change can feel so difficult.
The framework becomes useful here only when a manager treats those as possibilities to investigate rather than labels to attach to an employee.
Imagine two complaints. One nurse says, “I don’t like this new scheduling system.” Another says, “I requested Tuesday off, the schedule put me on Tuesday, and I had to trade two shifts to fix it.” The first statement may point toward discomfort with the change. The second gives the manager something operational to check.
The emotional reaction may still matter, but the manager now has evidence to examine.
Two questions need to stay separate: How comfortable is this employee with the change? Did the system produce the expected result?
An employee can be skeptical of a new process and still identify a legitimate defect. Someone can prefer the old way while correctly pointing out that the new workflow creates an unnecessary exception. Someone can also be frustrated by a system that is functioning as designed and simply needs a better explanation or more support.
Julie’s framework gives a manager several possible explanations to consider. Is habit playing a role? Is the employee attached to the previous process? Is there resistance to a legitimate change? Has repeated difficulty created discouragement?
The manager still has to check the evidence before deciding which explanation fits. Look at the request, the output, the applicable rule, and the correction. If the complaint disappears under inspection, adjustment may be the larger issue. If the complaint survives inspection, calling it resistance does not make the operational problem disappear.
Support Means Knowing Who Can Fix the Exception
A rollout feels very different when an employee can point to a problem but cannot find anyone who can fix it.
Julie talks about support in practical terms. In our conversation, she asked, “What are the support systems? What’s the you know scaffolding that I need to support me right now.” She was talking about getting through difficult change more broadly, but the question fits technology rollouts well. Training is only part of support. Employees also need to know what happens when the system produces an outcome they believe is wrong.
Lee Barker, an inpatient psychiatric nurse at an HCA hospital in Kansas City, described that kind of difficulty in a September Working People interview. His account was not an independent audit of Timpani and does not establish how scheduling works throughout HCA. He described difficulty getting local scheduling concerns corrected far enough in advance to make the process workable.
That turns support into an operating question.
Who is allowed to override the schedule? Does the employee know who that person is? What evidence does the manager need before making a change? How quickly does the employee get an answer? If the same type of problem keeps appearing, does anyone recognize it as a recurring exception rather than treating each case as a fresh annoyance?
Suppose, hypothetically, an employee says the new system scheduled them on a day they had properly requested off. The useful response is not simply, “Give it time.” The manager can check the request, the generated schedule, the relevant staffing rules, and the person with authority to change the result. The employee then needs an answer explaining whether the system made an error, a policy overrode the request, or the request was entered incorrectly.
A correction worked when the immediate problem is resolved and the organization can explain why it happened. If nobody can trace the path from the disputed output to a clear answer, the support process deserves scrutiny too.
Measure the Time Spent Fixing What Automation Gets Wrong
One of HCA’s strongest arguments for Timpani is time. The company says nurse managers can build schedules much faster than before. That is a meaningful gain if it holds up in day-to-day work.
The harder question is what happens to the time saved after the first schedule is produced.
Julie talks about attention as something people too easily give away. In our conversation, she said that “one of the biggest gifts that we have that we often throw away is our attention.” She was talking about focus more broadly, but the same idea matters when a manager evaluates a new system. Time spent fixing exceptions is still work, even when it arrives in small pieces across several employees instead of one obvious block on a manager’s calendar.
That does not mean every correction proves the automation failed. Any scheduling process has exceptions. Someone calls out. Staffing needs change. A request conflicts with coverage requirements. The relevant question is whether the new process reduces the total burden or moves some of it elsewhere.
A manager might save several hours creating a schedule while employees spend additional time reviewing it, finding conflicts, locating someone who can authorize a change, arranging trades, and following up. The opposite may also be true. Timpani could remove enough manual work that occasional corrections remain a strong net improvement.
A deployment total cannot answer that local question.
Managers need to look at the less glamorous evidence. How many exceptions are being raised? What types keep recurring? How long do they take to resolve? Whose time gets consumed? Does the same issue reappear in the next scheduling cycle?
Technology rollouts are often measured by the work they were designed to eliminate. The work created around the new system can be easier to miss.
If Timpani produces a strong first schedule and only a small number of quick corrections, the efficiency claim gets stronger. If an organization repeatedly saves time at the front end and spends it again through trades, appeals, and manual repairs, that is useful management information too.
The goal is not to hunt for hidden failure. It is to measure the whole job.
Investigate One Complaint All the Way Through
The manager responsible for the team or rollout should pick one recent complaint and follow it all the way through.
Start with the expected result. What was the employee trying to accomplish, and what should the system have done? Compare that with the actual output. Look at the schedule, the request, the staffing rule, the approval path, and the correction itself. Identify who had authority to change the outcome, how long the employee waited for an answer, and whether the same kind of problem has happened before.
The result should be more specific than “the employee hates the new system” or “the software works fine.” Maybe the request was entered incorrectly. Maybe policy overrode the preference. Maybe the employee misunderstood the new process. Maybe the manager lacked enough authority to correct the exception quickly. Maybe the same scheduling problem keeps recurring and needs to be escalated.
The manager should be able to explain why the result occurred and whether the correction prevented, resolved, or at least clarified the same problem for the next cycle.
That is where the human side of leadership becomes operational. Julie has spent much of her career thinking about what happens when people move through change while still needing to feel heard, respected, and supported. A manager does not have to agree with every complaint. The manager does need to understand what happened well enough to respond to the actual problem.
Following one complaint through to an answer is a small enough job to do this week, and it may tell you something your rollout report missed. Dr. Julie Donley is also one of my co-authors in Decisions, Courage, and Action. Read Julie’s contribution in the book on Amazon for more from her alongside the other co-authors.
About Dr. Julie Donley
Dr. Julie Donley is a leadership coach, author, speaker, and former director of nursing in behavioral healthcare. Her psychiatric nursing and leadership experience informs her work with managers and executives on communication, conflict, stress, and the human side of change. She developed the CARE Leadership Model, built around communication, appreciation, respect, and empathy, and is the author of Leading at the Speed of People and Does Change Have to Be So Hard? Learn more at drjuliedonley.com. Connect with Dr. Julie Donley on LinkedIn.
Media and interview inquiries: To request an interview about leadership communication and managing change, use her speaking inquiry page.
Meta’s AI Sent a Buyer to His Door. What Did the Seller Actually Approve?
A buyer showed up for a Facebook Marketplace pickup that seller Matt Robb said he did not know had been arranged. According to reporting on the September 26 incident, Meta’s Muse assistant had handled the conversation, provided pickup information, and moved the transaction far enough along that the buyer made the trip.
The important correction came on September 29. Matt Robb said he had selected an “Allow Always” setting, expecting the system would still stop and ask before certain actions. Meta said its review found no breach of its privacy controls. That makes the incident more useful to a business owner, not less. The problem becomes much more ordinary: you thought you approved one level of authority, while the software treated your approval as permission to keep going.
That question came back to me when I interviewed Donna Blevins, who works with entrepreneurs on decision-making and AI workflows, about how she was using AI inside her own business. Donna wasn’t talking about Meta, and she wasn’t proposing a security framework. She was describing something much more practical: how she gives an AI assistant a job without immediately handing it the whole job.
Her example involved Google Drive. Before letting the AI create or reorganize anything, she told it to enter a folder, inspect what was there, and report back. “Don’t do anything. Come back and tell me what you see.” Only after she checked that first result did she authorize the next small action.
That sequence matters a lot more once the AI is talking to customers instead of organizing folders.
What “Allow Always” Actually Changes
The September 29 follow-up changes the story in an important way. Matt Robb later said he had selected an “Allow Always” permission. His expectation was that Muse would still stop and ask before certain actions. The follow-up reporting said that “Allow Always” authorized continuing messages using a template that contained his pickup information.
The evidence supports saying there was a mismatch between what Matt Robb thought he had authorized and what the permission allowed the assistant to keep doing. It does not support saying Muse secretly defeated its own controls or somehow operated outside any permission at all.
Meta’s position was that its review found no breach of privacy controls. That is Meta’s conclusion about its own system, not an independent technical audit, so I would not treat it as the last word on every detail. At the same time, the later permission review makes the simpler “AI ignored the rules” version hard to defend.
There is another layer here that matters to business owners. Meta describes Muse as using a separate permission system for actions connected to outside services. In its technical explanation, that permission layer can allow an action, block it, or ask for approval. That means there is a difference between the instructions you type into the conversation and the actual authority the product has been given.
Meta made that distinction even more relevant on September 29 when it announced Muse for Small Business, with connections to tools including Shopify, QuickBooks, Stripe, and Canva. Meta says publishing, sending, and spending require approval in that business version.
The September 29 announcement came after the Marketplace incident and is relevant here as business context, not as the product version involved in Matt Robb’s reported experience. It shows where these assistants are headed: closer to the parts of a business where a mistaken approval can become a real customer promise, a published change, or a transaction.
A prompt can say, “Check with me first.” A product setting may already say something broader. If those two instructions do not mean the same thing, the owner needs to know which one actually governs the next action.
That is where Donna Blevins’ much smaller Google Drive example becomes useful. She does not begin by giving the assistant a whole workflow. She starts by narrowing what it is allowed to do.
Donna Starts With “Just Go Look”
Donna Blevins’ example starts with a small instruction that sounds almost too simple to matter. She gives the AI access to a Google Drive folder and tells it to inspect what is there. Her exact instruction is: “Don’t do anything. Come back and tell me what you see.” That separates looking at the assignment from changing anything.
The assistant can look around, open folders, and report back. Donna still decides what happens next. She is not asking it to reorganize the drive, rename everything, build a course structure, or make a dozen changes while she hopes it understood the assignment.
After the assistant reports back, she gives it one limited task. Create a folder. It does that and gives her a link. Donna clicks the link and checks the result herself.
She then tells the AI to look at the structure of another course and use that as a model inside the new folder. It creates the structure, Donna checks it, and she notices a problem with the naming. She tells it the naming structure she actually wants, and the assistant adjusts.
That back-and-forth is important. The useful part is not that Donna found a magic prompt. She is reducing the size of the mistake she might have to catch.
If the first assignment is “look and report,” there is very little to undo. If the next assignment is “create this folder,” the completed result is easy to inspect. If the naming is wrong, she can correct it before the same mistake gets repeated across the rest of the structure.
She connects this directly to something she says elsewhere in the interview: work on one thing at a time and finish it.
I think that is where the example becomes useful for a business owner. A lot of AI delegation gets vague very quickly. “Organize this.” “Handle this.” “Take care of these messages.” “Run with it.”
Those instructions may feel efficient, but they make the finish line harder to see. If the assistant does five things at once, you now have five things to inspect and five places where your definition of “done” might differ from the machine’s.
Donna’s sequence makes the completed work visible. She can point to the folder, click the link, inspect the structure, and decide whether the result is good enough for the next instruction.
That does not prove her approach would have prevented the Muse incident, and Donna was not testing Muse when she described it. What I can take from Donna’s example is more specific. Her working habit keeps the assignment small enough that she can still see what authority she has granted and what result came back.
That becomes more important when the assistant moves from folders and file names into customer communication, where the finished result might contain a price, a promise, a date, or a commitment someone else will act on.
What “Handle This Customer” Leaves Unsaid
Suppose a prospective coaching client sends a message asking whether a workshop is a good fit and whether there is still room to join.
An AI assistant can do a lot of useful work before the owner ever touches the reply. It can pull together the workshop description, summarize the person’s question, draft a response in the owner’s tone, and flag anything that appears to require a decision. If the draft sounds stiff or generic, the owner can correct it the same way Donna corrects language that feels too academic or unlike her.
The owner still has to decide when a draft turns into a promise to a customer.
For example, an instruction written for this article might say:
“Draft a reply using the workshop information I’ve already approved. Do not send it. Flag any question about price, availability, refunds, dates, or special terms for me to decide.”
That is not Donna’s wording or a proprietary method. It is an application of the way she already works: give the assistant a bounded task, inspect the result, then decide what happens next.
The owner may be perfectly comfortable letting AI explain what the workshop covers. The owner may still want to personally decide whether to extend a discount, promise a seat, change a date, or make an exception. Those are not merely writing choices. They create expectations for another person.
This is also where Donna’s concern about voice becomes useful. She talks about feeding AI enough examples that the writing sounds like her and asking it to “donatize” material that does not. That matters, but sounding like the owner solves only one part of the problem. A polished reply can still contain a promise the owner never intended to make.
That is why chat instructions and product controls both matter. A sentence such as “ask me before sending” may help define the assignment, but it does not replace whatever permissions the software itself uses to govern sending, publishing, purchasing, or other actions. Different tools expose those controls differently, so the owner has to know what authority was actually granted in that product.
For one business, finished might mean a draft waiting for review. For another, it might mean a summary of the customer’s question with anything requiring an exception clearly flagged.
The narrower you make that finish line, the easier the result is to inspect. Once you trust the workflow and understand the permissions, you can expand it deliberately instead of discovering after the fact that “handle this” meant more than you thought.
Define One Finished Task
Donna’s advice about finishing three small things a day sounds like a productivity idea, but it also helps with delegation. A task is easier to hand off when you can tell whether it is actually finished.
For the customer-reply example, “handle these messages” is too fuzzy. A better finish line might be: prepare one reply using approved information, flag anything that requires a decision, and stop before sending.
That gives the owner something concrete to inspect. The draft exists. The unresolved question is visible. The assistant has reached the stopping point.
A useful five-minute exercise is:
- Choose one recurring customer task.
- Write down the exact finished result you want back.
- List the decisions the assistant may make without you.
- Mark the point where it must stop and wait.
This is an operational suggestion, not a proven security method. It does not replace the product’s own permission system or guarantee that every tool will behave the same way. Its value is simpler: you can see whether the assignment itself is clear before you widen the authority.
Donna’s one-task-at-a-time habit gives you a practical way to test that. Finish one small workflow, inspect what came back, correct what is wrong, then decide whether the next version deserves more room to act.
A checked draft can be a finished task. The assistant does not have to complete the entire customer interaction for the work to count.
Keep the Decision You Actually Want to Keep
The cleanest place to start is with one task you can inspect. Once you have one workflow you can inspect and correct, you can make a deliberate decision about whether it deserves more authority. Donna’s one-task-at-a-time approach keeps the handoff visible instead of letting the assignment quietly expand.
Before you hand over another customer task, decide what the assistant may finish and where you still want it to stop. Donna Blevins is also a co-author of Decisions, Courage, and Action, our book about making decisions and acting on them. Pick up the book on Amazon to read more from Donna and the other co-authors.
About Donna Blevins
Donna Blevins works with entrepreneurs on decision-making, self-talk, practical execution, and AI-assisted workflows. In her conversation with Robert Plank, she described using AI in small, reviewable steps, including inspecting Google Drive folders, checking completed work, correcting naming and tone, and giving the assistant one task at a time. More about her work is available at donnablevins.com. Connect with Donna Blevins on LinkedIn.
Media and interview inquiries: To request an interview about AI workflows and decision-making, use her contact form.
1671: Leadership Through Change and Self-Assurance with Erin Herman
Business leaders aren’t crumbling under change because it’s unfamiliar, they're exhausted because it’s constant, confusing, and out of their control. In this episode, keynote speaker and change management expert Erin Herman explains how organizations can turn change into a competitive advantage by pairing clarity with empathy to create genuine confidence. She breaks down why AI and other workplace shifts trigger fear around identity, certainty, and job security, and what great leaders do differently in those moments. Instead of reacting to surface-level resistance, Erin shows how to listen “between the lines” to uncover deeper concerns and rebuild trust. She also shares her KEG method (Courage, Empathy, Growth mindset) as a practical framework for taking small, courageous steps that accumulate into massive transformation. If your team is overwhelmed by change and stuck in survival mode, this conversation offers a roadmap back to alignment and momentum.
Erin also dives into the hidden forces behind self-limiting beliefs, especially for ambitious professionals and women in leadership. She reveals why women often wait until they meet 100% of job criteria before applying, while men typically move forward at around 40%, and how this mindset quietly blocks career growth. By cultivating a “delusional belief” in your own success, you can move forward even when fear is present, instead of waiting to feel perfectly ready. Erin contrasts fragile, externally-driven confidence with durable, internal self-assurance that can’t be shaken by gossip, criticism, or a bad performance review. With self-assurance as your foundation, you can process feedback, adjust your behavior, and still remain grounded in your worth. Listeners will walk away with a clearer understanding of how to replace hesitation and self-doubt with intentional action and resilience.
Through powerful personal stories from selling elevators in a male-dominated industry to securing a $300M commitment for St. Jude, Erin illustrates how boundaries, courage, and discomfort shape a fulfilling career. She candidly shares how she swung from having no boundaries and over-apologizing to being too rigid, and how she finally landed on healthy boundaries that protect her energy while empowering her team. You’ll learn how to say no without guilt, support others without carrying their burdens, and define your leadership style with clarity and care. Erin also unpacks the quote, “Don’t let your comfort zone become your cage,” and shows how micro wins and intentional risk-taking create long-term change. Whether you’re a founder, executive, or emerging leader, you’ll gain tools to lead with courage, design a culture that supports growth, and navigate change without burning out your people. If you’re ready to stop performing, start truly leading, and change your superpower, this episode will give you both the inspiration and the structure to begin.
Quotes:
"Great leaders don’t just answer surface-level questions; they listen between the lines to understand who’s feeling uncertain about their role, their identity, and where they fit in."
“Boundaries delivered with clarity and care are essential to leadership; they’re how we create trust, consistency, and real results.”
“Don’t let your comfort zone become your cage. When staying comfortable costs you more than the risk you could have taken, it’s time to move.”
Contact Details:
- Change Is Coming. Are Your Leaders Ready? Get Erin Herman on Your Stage
- Ready to Lead What’s Next? Connect With Erin on LinkedIn
- Follow Erin Herman On Instagram for Bold Leadership, Confidence, and Meaningful Impact
- Ready to Lead With More Courage & Confidence? Follow Erin Herman on TikTok
- Your Next Leadership Breakthrough Could Start Here: Follow Erin Herman on Facebook
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Too Busy for the Gym? What One-Minute Workouts Still Ask of You
A one-minute workout sounds almost too small to skip. That is part of what makes a new study published September 30, 2026 so interesting, especially if your usual excuse is that the day never leaves you a clean hour for exercise.
In the randomized trial published in Frontiers in Physiology, researchers followed 60 sedentary women ages 18 to 25 for eight weeks. Half were assigned to a resistance “exercise snacks” program, while the other half continued their usual routines. The exercise group improved more than the control group across the study’s measured lower-body strength and endurance outcomes. The appealing part is the size of each bout: one minute.
Fitness coach and author Phil Ross has talked about exercise snacks, small spaces, limited equipment, and finding ways to make training something you will actually repeat. When I spoke with Phil, he kept returning to an idea that gets lost when we obsess over workout length. The minute still has to contain some attention. You still have to do the movement, then come back and do it again.
That matters once you look past the headline-friendly part of the study. Participants were not assigned one minute of exercise per day. They were asked to complete 11 one-minute bouts each weekday and 10 each weekend day, with the bouts separated by at least 30 minutes. That added up to 75 minutes a week, with app-based instruction and monitoring built into the program.
For someone running a business from a desk, that creates a more useful question than whether one-minute workouts “work.” What has to be true about your day, your setup, and the way you approach those minutes for you to keep doing them after the first burst of enthusiasm is gone?
One Minute Is Not the Whole Commitment
The phrase “one-minute workout” is accurate in one narrow sense. Each exercise bout in the September trial lasted about 60 seconds. It becomes misleading if you picture someone standing up once, doing a minute of squats, and calling it a day.
The women assigned to the exercise group completed 11 of those bouts each weekday and 10 on Saturday and Sunday. The bouts were separated by at least 30 minutes, which spread the activity across the day instead of packaging it into a conventional workout. Over a week, the prescribed exercise added up to 75 minutes.
These were not necessarily casual movement breaks, either. Participants were expected to reach a perceived exertion of at least 7 on a 10-point scale. They received instruction before the intervention and used a mobile app with exercise videos, reminders, performance recording, and exertion monitoring.
That context matters when you look at the results. Researchers compared 30 women assigned to the exercise-snacks program with 30 women who continued their habitual lifestyles. After eight weeks, the exercise group improved more than the control group across the study’s measured lower-body strength, explosive-strength, and muscular-endurance outcomes.
That is useful evidence. It is also more specific than saying tiny workouts make you stronger.
The study involved sedentary women ages 18 to 25. It did not test older adults, men, people already exercising regularly, or people trying to manage back pain. It also did not compare exercise snacks head-to-head with a traditional 30- or 60-minute workout. You cannot take these results and conclude that ten scattered minutes are universally better than going to the gym, or even that every kind of brief exercise will produce the same results.
There is another reason to resist turning one trial into a universal formula. A systematic review and meta-analysis published in Frontiers in Nutrition on September 28 looked at 22 exercise-snack studies involving 994 participants. Across the available strength studies, the researchers did not find a statistically significant pooled improvement in strength. They rated the strength evidence as low certainty.
That does not put the review and the September 30 trial into some scientific cage match where one has to win. The review’s literature search ended on February 5, 2026, more than seven months before the newer trial was published, so it could not include the new results. The studies it did include also differed in their participants, exercises, schedules, and outcomes.
The reasonable conclusion is that the evidence is still developing and “exercise snacks” describes a broad idea rather than one standardized workout.
The part I find more immediately useful is what the September trial reveals about commitment. Researchers reported 100 percent adherence during the eight-week intervention. That is impressive, but it happened inside a structured study with instructions, an app, monitoring, and a defined finish line.
That environment matters.
Most of us do not have a research team noticing whether the 2:30 p.m. exercise break quietly disappeared underneath email, phone calls, client work, a kid pickup, or whatever caught fire after lunch. Nobody sends you a study reminder when you decide to keep working for another 40 minutes. There is no investigator waiting to record whether you followed the plan.
A shorter exercise bout removes one real obstacle. You no longer need to find an uninterrupted hour. It does not remove the need to decide what you are going to do, make it accessible when the opportunity appears, know how to perform it, and come back later to do it again.
That is a more interesting challenge than finding sixty seconds.
Short Does Not Mean Mindless
One reason short workouts get appealing so quickly is that duration is easy to measure. You did a minute. You checked the box. You moved on.
Phil Ross puts more attention on what happens inside that minute.
When we talked about exercise habits, Phil said, “Every time you go to swing that kettlebell or do your push ups, try to do it better than the time before.” That was not a comment on the September study. It was part of his broader coaching approach, and I think it gives us a useful way to think about what a short bout is actually for.
“Better” does not have to mean heavier weight, more speed, more repetitions, or turning every break into a miniature fitness test. It can mean cleaner movement, better control, better positioning, or simply paying enough attention to notice what you are doing instead of racing through the minute because the timer is running.
Suppose you have exactly three minutes between calls. You could grab a kettlebell, rush through a set while half-reading Slack, drop it back on the floor, and tell yourself you exercised. You could also treat the same small window as practice. You already know the movement, you give it your attention, and you notice when your form starts getting sloppy.
Those two versions may occupy the same amount of time. They are not necessarily the same experience.
The September trial measured outcomes such as strength and muscular endurance. It did not measure “quality of attention,” and it did not test Phil’s idea that trying to perform a movement better improves adherence or results. The study tells us what happened under one particular eight-week resistance-exercise protocol in sedentary young women. Phil gives us a separate practical lens for what a person might pay attention to when training outside a study.
I like that lens because it takes some of the weird pressure out of fitness goals. If the only score is how long you worked out, a one-minute bout can feel almost embarrassingly small. If part of the score is whether you practiced something with a little more control than yesterday, the minute has a job.
Maybe your schedule does not allow a traditional workout Tuesday afternoon. You still have a chance to practice something you already know. The useful question is whether you showed up with enough attention to make the repetition worth doing.
Phil also described exercise as an “opportunity of improvement.” His phrasing is a little different from the usual productivity language around habits. He is not telling you to optimize every spare minute. He is trying to remove the feeling that exercise is a chore you survive and replace it with the feeling that there is something small you can get better at.
That changes what a short session means. The goal is no longer to make the timer stop. The minute becomes practice.
Will You Repeat It Tomorrow?
The real test of a short workout starts when nobody is watching.
In the study, participants had a defined protocol, app support, instructions, and a research team behind the process. Ordinary workdays are less cooperative. Suppose a call runs long, someone needs an answer, lunch moves, and the exercise break you expected to take at 2:00 has disappeared by 4:17.
A missed window may simply show that the plan does not fit the workday yet.
Phil talks about this from the preference side. Some people like classes. Some want a training partner. Some prefer to work alone. He said the important thing is to “remove that dread factor” and find something you actually like enough to keep doing.
That does not tell you the correct exercise dose. It does help you think about whether the plan has any chance of surviving contact with a normal Wednesday.
If you hate the activity, resent the setup, or need twenty minutes just to get ready for a one-minute session, the small size of the workout will not save it. The friction is somewhere else.
This is where I would make the experiment very simple.
Pick two transitions that already happen in your workday. Maybe one is after your first morning call and the other is before lunch. Maybe it is when you finish recording an episode and again before you shut down for the day.
Decide what you would actually do in that window. Use a movement you already know how to perform safely. Figure out whether you need any instruction before you start. Make sure the space or equipment is available instead of turning the break into a scavenger hunt.
For one week, track only three things:
- What opportunity you planned to use
- Whether you actually used it
- If you skipped it, what got in the way
That is enough. You do not need a 30-day challenge, a color-coded habit tracker, and a new identity as someone who wakes up excited about burpees. You are trying to learn where the friction actually lives.
I have two kettlebells in my office. One is near the door and a larger one sits behind me. When Phil and I talked, I brought up how useful it can be just having them there between meetings.
That setup does not guarantee I will use them. It removes one obstacle.
If the kettlebell were in the garage under three boxes, I would have a different problem. If it is sitting a few feet away and I still leave it alone all afternoon, then equipment access was not the problem after all.
The same logic applies if your preferred activity is not strength training. Phil talked about people choosing different formats, including classes, training partners, solitary workouts, yoga, Pilates, and other activities. His broader point was that the activity and setting have to be something you can live with.
Suppose your planned break keeps failing because meetings slide over it. That points toward a scheduling problem. Suppose the time is available but you keep avoiding the movement because you dislike it. That points somewhere else. Suppose you are willing to do it, but every time the opportunity appears you are unsure which exercise to choose. Now the issue may be instruction or too many decisions.
A skipped opportunity is not automatically a character flaw. It is information.
That is especially important when we compare everyday life with a study reporting 100 percent adherence. A monitored eight-week protocol answers a research question under structured conditions. Your own week answers a different question: where does this idea actually break when nobody is organizing the day around it?
A one-week schedule experiment does not prove a habit has formed. It does not reproduce the September trial. It does not tell you that two short breaks are the correct amount of exercise for your health.
It gives you evidence about your own workday. Instead of vaguely saying “I am too busy,” you can see whether the obstacle is timing, setup, uncertainty, preference, or interruption.
That gives you something specific to change.
Progress Still Takes Time
Short sessions can reduce the size of the commitment without shortening the amount of time it takes to improve.
Phil gave a useful example when he described a 75-year-old client who eventually worked up to a weighted Turkish getup. According to Phil, the client had been training with him for a little under two years and needed about eight or nine months before he could perform the movement.
That is one person’s experience, not controlled evidence and not a benchmark for how quickly anyone else should progress. It is also an advanced movement, so the point is not that readers should copy it.
The useful part is the time horizon.
It is easy to hear “exercise snacks” and mentally connect short sessions with fast results. Those ideas do not automatically travel together. A workout can be brief while adaptation still takes weeks or months.
The September trial itself lasted eight weeks. It measured changes over that period under a structured program in a narrow population. It did not show what happens after six months, whether participants kept the routine afterward, or how quickly someone outside that population should expect to improve.
This becomes important when you start defining what you want the exercise to do for you.
Maybe your goal is functional rather than dramatic. You want a basic movement to feel more controlled. You want to feel more confident using a piece of equipment you already own. You want an activity that currently feels awkward to become familiar.
A short practice window may make those attempts easier to fit into the week. It does not eliminate the time required for improvement to accumulate.
That is where Phil’s older client becomes useful. The interesting part of the story is not a one-minute breakthrough. It is that a difficult movement eventually became possible after months of training.
There is something almost reassuring about that. You do not have to force every session to prove that the whole plan works. Today’s small bout can simply be another exposure to the thing you are learning to do.
That also gives you a better way to judge progress. A one-minute session can be evaluated immediately for whether you showed up and practiced with reasonable attention. The larger result belongs on a longer calendar.
Those are different clocks.
Confusing them is how a useful idea turns into another fitness disappointment. You try something small for four days, do not feel transformed, and decide the concept failed. Or you start increasing difficulty too quickly because the sessions feel too short to “count.”
Phil’s example points in the opposite direction. Give the work enough time to become work.
The same patience belongs in any experiment with short exercise. The size of the session can be small. Your expectation for how quickly your body changes does not have to be.
When you look at your own week, the first useful result may have nothing to do with strength. You may discover that the morning window works and the afternoon one never does. You may discover that the equipment is accessible but the activity itself is something you dread. You may discover that you are perfectly willing to exercise once the decision has already been made.
Those are useful findings because they tell you what to change before you build a bigger plan.
My kettlebells sitting in the office do not make me exercise. They make one particular excuse harder to use. The next question is whether I actually use the opportunity and keep using it long enough for the practice to mean something.
My office kettlebells make one excuse harder to use, but I still have to decide to pick them up. Phil Ross is one of my co-authors in Decisions, Courage, and Action, and his approach to repeatable effort gives you a useful starting point for taking action. Read Phil’s contribution in the book on Amazon.
About Phil Ross
Phil Ross is a fitness coach, college professor, and author whose work focuses on strength, movement, and practical ways to make exercise easier to fit into daily life. He created the BodyBell Method and has written several fitness books, including Exercise Snacks. His approach emphasizes technique, repeatable practice, and finding forms of training people are willing to continue. Connect with Phil Ross on LinkedIn.
Media and interview inquiries: To request an interview about exercise snacks and strength training, use his contact page.
Resources
McDonald’s AI Pricing Dispute: Who Really Gets the Final Say?
McDonald’s says its franchisees make the final call on menu prices. Reuters reported something messier. On September 29, 2026, Reuters published an investigation into McDonald’s existing pricing recommendation system and reported that five franchisees said they felt pressure to follow the company’s recommendations.
McDonald’s responded on October 1 that franchisees independently set their prices and can reject recommendations. The company also said the system does not set individualized customer prices and does not use real-time dynamic pricing.
That disagreement matters beyond hamburgers. If a company can truthfully say, “A human makes the final decision,” several practical questions remain. Who decided what the system should optimize? What information does the person making the decision actually see? Can that person disagree without creating another organizational problem? Who owns the result if the recommendation turns out to be wrong?
AI product executive Jason M. Riggs works on AI products in restaurant automation, where apparently simple outputs depend on a long chain of product decisions. In our conversation about AI leadership and decision speed, he described the complexity behind AI-powered drive-thru ordering, where customer requests can depend on menu data, modifiers, promotions, brand rules, point-of-sale systems, and human judgment during product development.
That does not give Jason inside knowledge of McDonald’s pricing system. It does give him a useful lens for examining what “human judgment” actually requires once AI starts recommending decisions that affect customers and the business.
For an executive, founder, or operator rolling AI into real decisions, the useful question is not whether a human technically remains somewhere in the loop. The harder question is whether that person has enough context, authority, and accountability to make the judgment meaningful.
The AI Recommendation Starts Long Before Someone Clicks Yes
The easiest place to look for human judgment is at the end of the process. The system makes a recommendation, someone looks at it, and that person accepts or rejects it. A lot has already happened before that moment.
Jason sees this in restaurant AI from the product side. When he described AI-powered drive-thru ordering, he pointed out that something as ordinary as ordering a hamburger gets complicated fast. Different brands use different language. Menus have modifiers, combos, coupons, limited-time offers, and rules about how those choices get entered into the point-of-sale system.
Drive-thru ordering and pricing are different problems. Jason did not build or evaluate McDonald’s pricing system. His example is useful for a narrower reason. It shows how much business judgment can disappear behind a clean recommendation on a screen.
Someone had to decide what the system was supposed to accomplish.
Reuters reported that McDonald’s pricing engine analyzes restaurant data and produces pricing recommendations for individual locations. Its reporting also described company involvement in parameters used to generate those recommendations, based in part on interviews with former employees of Tiger Analytics, which worked on the system.
Even if a franchisee ultimately decides whether to accept a recommendation, the recommendation did not arrive from nowhere. Someone selected inputs. Someone decided which outcomes matter. Someone decided what should count as an improvement and how the recommendation should be presented.
Those choices happen upstream.
The same issue shows up in smaller businesses. Suppose you give a sales manager an AI recommendation about which leads deserve immediate follow-up. The manager still has the final say, but the system may already have prioritized deal size, recent engagement, industry, or another signal before the manager sees anything.
If the system ignores something the manager knows from experience, the quality of the final human decision depends on whether the manager understands enough about the recommendation to challenge it.
Pricing can make those hidden choices particularly important because several reasonable objectives may pull in different directions. A recommendation could favor transaction growth, profitability, traffic, customer perception, competitive positioning, or some combination of them.
There is no need to guess which objective McDonald’s system prioritizes in a particular case. The Reuters reporting supports a more useful point. A recommendation is the output of choices made before the local operator ever receives it.
McDonald’s says franchisees independently set restaurant prices and may reject its recommendations. That distinction matters. The software recommends. The franchisee has the formal authority to act.
For an executive introducing AI into a consequential decision, formal authority is only one layer to inspect. You also need to know what problem the system was built to solve, what information shaped its answer, and whether the person receiving that answer knows enough to question it.
Otherwise, the final human review can become approval of a decision whose assumptions were settled somewhere upstream.
The next issue is what has to be true inside an organization for the ability to say no to function as real decision authority.
Having the Final Say Is Different From Being Free to Use It
Jason gave a useful example from his own restaurant AI work.
He described an AI system questioning an automatic upsell because the model considered it a mistake. From the model’s perspective, that recommendation could appear reasonable. From the business’s perspective, the upsell was intentional.
Someone had to know the customer, the business rule, and why the rule existed. Someone had to recognize that a technically plausible recommendation conflicted with what the company was actually trying to accomplish.
That is a more demanding version of human judgment than simply checking whether the AI produced an answer.
The analogy to pricing has limits. Jason’s drive-thru experience tells us nothing about whether a particular McDonald’s pricing recommendation is good or bad. It does show why an AI-assisted business decision can carry assumptions that are not obvious when it finally reaches the person expected to approve or reject it.
At McDonald’s, the company’s position is clear. Franchisees independently set menu prices and may reject the recommendations they receive.
Reuters reported a more complicated experience from some operators. Five franchisees told Reuters they felt pressure to follow the recommendations. That does not establish that franchisees lack the authority to say no. McDonald’s disputes any suggestion that the system removes their pricing control.
That tension is worth examining without pretending we can resolve it from outside the company.
Authority on paper and usable authority inside an organization are not always identical.
Imagine an operator receiving an AI-assisted recommendation that conflicts with something they know about their local customers. They may know a nearby competitor has changed an offer, that a particular item is unusually price-sensitive, or that a short-term event distorted recent demand.
The important issue is not merely whether there is a way to reject the recommendation. It is what happens when the operator does.
Can they explain the decision using information the system did not have? Does the organization want that local judgment, or does disagreement automatically look like resistance? Is the recommendation one input among several, or does it gradually become the default answer that requires special justification to challenge?
Jason described a related accountability problem in corporate decision-making: “nobody wants to make a decision because if you make a decision, you’re accountable, and you have sort of this group accountability, which means nobody’s accountable.”
AI can make that problem stranger.
The system recommended it. Corporate supplied the tool. The local operator approved it. Responsibility can become distributed across the process even though somebody still has to live with the outcome.
Meaningful human judgment requires more than keeping a person somewhere in the workflow. The person needs enough context to question the recommendation, enough authority to act on that judgment, and a clear understanding of who owns the result after the decision is made.
The Software Doesn’t Live With the Consequences
A person being present in the workflow does not automatically mean that person owns the decision.
AI can produce an answer, recommendation, or next step, but the system does not experience what happens after somebody follows it. Jason put this bluntly while talking about large language models: “they don’t care about your time, they don’t care about your money.”
He was not talking about McDonald’s pricing technology. The broader management problem still applies to AI-assisted decisions. Software can recommend an action without having any stake in whether it helps the customer, damages a relationship, squeezes a margin, creates extra work, or turns out to be wrong. Someone inside the business still owns those consequences.
That person needs more than permission to click yes or no. They need enough information to understand what the recommendation means and enough authority to challenge it when something important is missing.
Suppose a pricing recommendation looks mathematically reasonable but conflicts with something a local operator knows about the market. Meaningful judgment requires a way for that information to matter.
If the operator can technically reject the recommendation but has no useful way to explain why, or expects every disagreement to trigger another layer of approval, the organization has not necessarily created better human oversight. It may have created a slower rubber stamp.
Decision speed matters here.
Jason has argued that faster execution can expose slower corporate decision structures. Teams can produce analysis, prototypes, and recommendations faster than before, then sit waiting for finance, leadership, or another alignment meeting to make the next call.
Adding AI does not fix that problem if every automated recommendation creates another queue of humans waiting to approve it.
The answer is not to have a person manually bless every routine transaction.
In Jason’s restaurant work, the goal is eventually to let a well-designed system handle the situations it was built to handle. Human judgment matters heavily while the product is being designed, while exceptions are being defined, and when something consequential falls outside the expected pattern.
Keeping a human involved everywhere can defeat the reason for automating the work in the first place. The harder management job is deciding where judgment belongs.
For a consequential recommendation, someone usually needs to understand what the system is optimizing, recognize information it may not have, know when an exception deserves attention, and have clear authority to act. Someone also needs to look afterward and determine whether the decision produced the result the organization wanted.
The McDonald’s reporting makes that ownership question visible without resolving it for us. Reuters reported that several franchisees felt pressure to follow pricing recommendations. McDonald’s says franchisees retain pricing authority and may reject them. Those accounts should not be collapsed into a conclusion the evidence does not establish.
They do expose a useful test for any company using AI-assisted recommendations.
If a recommendation goes badly, who is expected to explain the decision?
If the answer is the operator, manager, or executive who approved it, that person needs more than ceremonial involvement. They need the information and authority required to exercise judgment before the consequences arrive.
Jason warned about the opposite habit directly: “Don’t let your human judgment atrophy, because it will.”
Keeping that judgment active does not require slowing every decision down. It requires deciding where human responsibility sits and giving that person enough room to use it.
Audit One AI Decision Before You Add Another Approval Layer
The practical way to use Jason’s ideas is to inspect one consequential AI-assisted decision already happening inside your business.
Do not start by asking whether the company needs more AI governance. That can easily produce another committee, policy document, or approval step. Start with one real recommendation that somebody is already expected to accept, reject, or act on.
First, write down what the recommendation is supposed to improve.
Is the system trying to increase revenue, reduce cost, improve conversion, shorten response time, protect margin, reduce errors, or improve customer experience? If nobody can state the goal clearly, it becomes difficult to judge whether the recommendation is useful.
Jason described the danger of approaching technology backward when he talked about a company pushing employees to use more AI simply because leadership wanted more AI. His reaction was blunt: “Here’s a solution. Go find some problems.”
The warning still applies after a system is already in place. You need to know what problem the recommendation is meant to solve.
Next, name the decision owner.
Not the department. Not “leadership.” Not “the team.” Put one role or one person beside the decision.
That does not mean one person performs every step or ignores everybody else’s expertise. It means someone has clear authority to make the call when the recommendation matters.
Then define what evidence would justify overriding the system.
This gives local knowledge, customer information, unusual circumstances, or better data a way into the process without turning every disagreement into an argument over whether somebody “trusts AI.”
A sales manager might know a large account has gone quiet because the buyer changed jobs. An operations leader might know a supplier delay makes the recommended schedule impossible. A restaurant operator might know something about local demand that is not represented in the data feeding a recommendation.
Human intuition does not automatically beat the system. Disagreement still needs a path.
If the only acceptable answer is whatever the system recommended, the person reviewing it does not have much judgment to exercise. If every disagreement requires three meetings and executive approval, you have created a different problem.
Finally, decide who looks at the outcome.
A recommendation should not disappear after somebody approves it. Someone needs to check what happened.
Did the decision improve the thing the system was supposed to improve? Did it create a customer problem somewhere else? Was the override justified? Did the recommendation expose something the human decision maker had missed?
That is where the organization can learn instead of merely accumulating approvals.
For one AI-assisted decision, write down four things: the goal, the owner, the evidence that can justify an override, and the person responsible for reviewing the result.
This is not a framework Jason named, and it is not a claim about how McDonald’s should structure its pricing process. It is a practical application of the problem his comments expose.
A recommendation can arrive in seconds. You do not necessarily need another layer of review around it. You need to know who owns the decision, what would justify saying no, and whether anyone checks what happened next.
Faster Decisions Still Need an Owner
The phrase “final say” sounds reassuring until you try to define it.
A person may technically approve or reject a recommendation, but that only matters if they know what the system is trying to do, can see when important information is missing, and have enough authority to act on their judgment without turning every exception into a bureaucratic fight.
Reuters reported that several McDonald’s franchisees felt pressure to follow pricing recommendations. McDonald’s says franchisees remain free to reject them. The reporting does not let us settle what every operator experiences inside that system, but it exposes a management question that will keep showing up as companies put AI into consequential decisions.
Who actually owns the call?
Jason has made the same broader point from a different direction. Faster execution only helps if decision-making keeps pace. In his words, “speed without clarity just creates chaos.”
Pick one AI-assisted decision in your business and make sure you can name the person who owns it, the evidence that would justify overriding the recommendation, and who checks the result.
If you cannot name that owner, the decision process is still unfinished.
An AI recommendation can arrive in seconds, but someone still has to own the call. Jason M. Riggs addresses that tension in “Decision Speed with Human Judgment,” his contribution to Decisions, Courage, and Action, the book we co-authored. Pick up the book on Amazon to read Jason’s chapter and the other co-authors’ perspectives on making decisions and following through.
About Jason M. Riggs
Jason M. Riggs is an AI product executive with more than 25 years of experience across software, hardware, cloud, and applied AI. In the September 4, 2026 episode of Marketer of the Day, he was identified as Chief Commercial and Product Officer at Audivi AI, where his work includes AI-powered drive-thru automation. His experience focuses on product leadership, decision speed, organizational accountability, and the role of human judgment as AI makes execution faster. He is also the author of The MACH-10 PM and The MACH-10 Leader. Connect with Jason M. Riggs on LinkedIn.
Media and interview inquiries: To request an interview about AI leadership and human judgment, visit his press page.
Resources
1670: Authority Economy, AI Trust and Business Growth with Tracy Repchuk
In today’s rapidly shifting AI-driven landscape, business owners and coaches are discovering that information alone is no longer enough to stand out. Tracy Repchuk explains how artificial intelligence has turned information into a cheap commodity, wiping out the advantage once held by experts who simply “knew more.” Instead, she introduces the concept of the Authority Economy, where trust, positioning, and credibility have become the true currencies of choice. Rather than trying to shout louder in a noisy marketplace, leaders must intentionally build authority capital that clearly separates them from AI-generated noise and short-term imitators. Tracy shares how decades of navigating technological disruption have shown her that those who adapt and elevate their leadership rise above the chaos. This episode is a roadmap for anyone who feels overshadowed by AI and wants to become the obvious, trusted choice in their niche.
The Authority Economy Framework that Tracy reveals is built on three core phases: diagnosis, alignment, and signals. First, she helps entrepreneurs identify the hidden authority gaps in their pricing, offers, positioning, and messaging so they stop guessing at what’s broken. Next, she walks through authority alignment across seven critical pillars, ensuring that everything from premium pricing to buyer-fit messaging reinforces a powerful, trust-building narrative. Only then does it make sense to amplify visibility and broadcast authority signals such as results, recognition, media features, and leadership presence. This strategic sequence prevents visibility from simply magnifying what is not working and instead turns it into a multiplier for impact and revenue. Listeners will walk away understanding why random posting, networking, and speaking are not enough without a solid authority infrastructure behind them.
Tracy also opens up about her personal journey as an introvert who has spent 40 years in business, spoken in 47 countries, and weathered every major technological shift from early PCs to the internet to social media and now AI. She shares how a near-death experience clarified her purpose: to help others fulfill their mission by combining her technical background with marketing and strategic thinking. The conversation highlights why leaders must claim a category-of-one position, let go of being merely “good,” and step fully into visible, confident authority before AI creates endless clones of their expertise. Listeners are encouraged to begin with her Authority Alignment Assessment at AuthorityIndexScore.com to diagnose their own authority leverage gaps and get a customized report on what to fix first. Tracy also discusses her new book, “12 Laws of the Authority Economy,” which lays out the governing principles entrepreneurs can use right now to organize and future-proof their businesses. If you want to transform from overlooked expert to sought-after authority in an AI-saturated world, this episode delivers the insights and next steps to make that shift real.
Quotes:
“Artificial intelligence made information abundant; what used to be our greatest asset is now the cheapest commodity in the room.”
“Visibility and authority are not the same thing; visibility simply magnifies reality, so if your message and positioning are off, getting seen more just exposes what’s not working.”
“You have to start focusing on authority-building assets, your authority capital, because that’s what makes you the trusted choice in an AI-saturated world.”
Contact Details:
- Explore Tracy Repchuk’s Authority Economy and Turn Your Expertise Into Lasting Authority
- Ready for Your Next Level? Turn your Expertise Into Lasting Authority, Impact, and Growth
- Discover Your Authority Score: Unlock Your Next Level
- Connect with Tracy on LinkedIn and Elevate Your Authority
- Follow Tracy for Insights that Elevate your Authority and Impact
- Get Fresh Insights to Build your Authority, Influence, and Impact: Follow Tracy on X
- Unlock Powerful Business Insights: Follow the Tracy Repchuk YouTube Channel
- Grow your impact: Get inspired on Tracy Repchuk’s Pinterest
- 12 Laws of the Authority Economy on Amazon
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1669: A Life of Impact and Kidney Transplant Legacy with Dr. David Kountz
Segregation, poverty, and a complete lack of role models didn’t stop pioneering kidney transplant surgeon Dr. Samuel L. Kountz from transforming modern medicine, and his story is finally being told in depth. In this episode of Marketer of the Day, Dr. David Kountz, M.D. shares insights from his book A Life of Impact, a biography of his father’s journey from rural Arkansas in the segregated South to worldwide recognition as a transplant innovator. Listeners will hear how a childhood incident watching a surgeon repair a friend’s broken leg sparked an “impossible” dream for a young Black boy with no clear path into medicine. The episode explores how Dr. Samuel Kountz navigated racist barriers, built key relationships such as with future Senator William Fulbright, and persisted through under-resourced schooling to eventually enter the University of Arkansas medical school. You’ll learn how his background in chemistry and immunology positioned him perfectly to enter the brand-new field of organ transplantation in the late 1950s and early 1960s. This conversation sets the stage for understanding not just one man’s rise, but how his determination changed what was medically and socially possible.
The episode dives into Dr. Kountz’s remarkable surgical career, during which he performed over 500 kidney transplants at a time when the procedure was still viewed as highly experimental. Dr. David Kountz explains how his father combined technical excellence with extraordinary bedside manner, earning the trust of patients and families facing terrifying, unproven treatments. Listeners will hear how consent conversations in that era were radically different from today, requiring patients to take a leap of faith on what felt like science fiction, moving organs from one human to another to save a life. The story highlights that while modern audiences may take kidney, heart, and organ transplants for granted, in the 1960s this work was “like landing on the moon.” Beyond the operating room, the episode emphasizes how Dr. Kountz championed teamwork, insisting that transplant success depended on nurses, surgeons, and the entire care team performing in sync. These details give the audience a vivid look into the human, ethical, and emotional dimensions behind a procedure that is now routine but was once controversial and feared.
A key highlight of the conversation is the 1976 live kidney transplant on the Today Show, an audacious public education effort that led to roughly 20,000 new organ donors signing up in the weeks after it aired. Dr. David Kountz describes watching that broadcast as a teenager, understanding the enormous risk to his father’s reputation if anything went wrong, and witnessing his calm confidence born of repetition and mastery. The episode explores how this national TV moment helped demystify transplantation for both the public and skeptical physicians, shifting it from fringe experiment to mainstream medicine. Listeners also learn how Dr. David Kountz forged his own path in internal medicine and medical education, intentionally avoiding being a “carbon copy” of his father while still being guided by his example of joy, optimism, and purpose-driven work. He shares powerful life lessons, including his personal mantra of taking work seriously but not taking oneself too seriously, and using humor and levity to ease anxiety for students and patients. By the end, the episode frames A Life of Impact not just as a medical biography, but as a universal blueprint for resilience, focus, and staying committed to your goals despite discrimination, doubt, and seemingly insurmountable obstacles.
Quotes:
"He understood he needed to demystify it; to make it a very real kind of event so that people could see transplantation not as science fiction, but as a medical reality that would become more and more common."
"You should never not be willing to look for help and look for others who can help you achieve your dreams."
"Anywhere he would go and try to start a new transplant program, you could educate one very smart person who's the surgeon, but you also have to educate that entire team."
Contact Details:
- Discover a Legacy That Changed Medicine: Explore the Official Website of Dr. David Kountz
- Connect With Dr. David Kountz on LinkedIn for Insights that Inspire Healthier, Stronger Leadership
- Follow Dr. David Kountz on Instagram to Be Inspired; Think Differently; Live Purposefully
- Follow Dr. David Kountz on Facebook to Discover Insights that Inspire Change
- A Life of Impact: A Biography of Samuel L. Kountz, MD on Amazon
Podcast: Play in new window | Download (Duration: 27:19 — 37.5MB) | Embed
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1668: Rapture Timing, Tribulation and End-Times Clarity with Howard Hunt
End-times prophecy can be confusing, especially when popular teachings seem to conflict with Scripture and history. In this episode, Howard Hunt, author of “Warning Rapture: The Great Disappearance, An Eventual Problem in Timing,” shares insights from over four decades of intensive study on the rapture and the tribulation. He explains why he believes the widely accepted pre-tribulation rapture view is based more on assumptions, recent history, and financial interests than on clear biblical foundations. Drawing from passages like Daniel 9, Ezekiel 38–39, and 2 Thessalonians 2, he lays out a detailed sequence of prophetic events that challenges conventional thinking. Howard also uncovers the historical roots of rapture theory in the 1830s and how it spread through movements, teaching institutions, and study Bibles. His research raises tough questions about why so many believers expect to be removed from suffering when the apostles themselves endured great hardship. This conversation is designed to help listeners think critically about what they have been taught and return to a deeper, Scripture-first understanding of end-times doctrine.
Howard walks through the idea that a coming war involving Israel, Russia, and surrounding nations could trigger a peace treaty that marks the start of the seven-year tribulation. He explains how this covenant, linked to the Antichrist, sets prophetic “time” in motion and why the midpoint when the Antichrist stands in the temple and declares himself to be God is so crucial. Listeners hear how Howard differentiates between the general tribulation and the “great tribulation,” connecting it to the true wrath of God described in Revelation. He also discusses why miscalculations about rapture dates have caused real-world damage, from people selling possessions in anticipation of escape to believers becoming disillusioned when predictions fail. Through personal stories from ranching life, time in Israel, and ministry experiences, he illustrates how easily people can follow spiritual trends like sheep led astray. Howard emphasizes that a misguided hope of avoiding hardship could leave many unprepared for deception, persecution, and apostasy. This episode challenges Christians to rethink comfort-driven beliefs and to anchor their expectations in careful, contextual biblical study.
At the heart of Howard’s message is a call to spiritual readiness, genuine faith, and a living relationship with God rather than reliance on sensational timelines. He urges believers to “study to show yourself approved,” describing true worship not just as outward expression but as a lifelong pursuit of knowing God and His Word. The conversation highlights the urgency of preparing hearts, sharing the gospel, and strengthening the kingdom of God within, especially if current global tensions point toward a rapidly approaching prophetic season. Howard also shares how near-death experiences, personal loss, and hardship deepened his dependence on grace and convinced him that God can meet people anywhere, even at their lowest point. He reminds listeners that faith is activated through listening being quiet before God, using our “two ears and one mouth” to hear more than we speak. The episode closes with an encouragement to be ready, watchful, and compassionate toward others as prophetic events unfold. For those wrestling with questions about the rapture, tribulation, and what lies ahead, this discussion offers clarity, challenge, and a powerful invitation to seek truth beyond popular theories.
Quotes:
"It's the Bible that says to study to show yourself approved, a workman rightly dividing the word of truth. To worship God is to learn of Him."
"End-times deception is basically monetary. The rapture teaching went into the Scofield Bible, it went to the universities, and it really brought in the money."
"I just want to share information that may be beneficial to the brethren and to people who do not know the Lord, because there’s a lot of pre-trib stuff going on all over, and it’s deceptive."
Contact Details:
Warning Rapture: The Great Disappearance, An Eventual Problem in Timing on Amazon
Podcast: Play in new window | Download (Duration: 29:44 — 40.8MB) | Embed
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1667: Data-Driven Podcast Audience Growth with David Ledgerwood
Podcast downloads alone rarely prove whether a campaign reached the right audience or delivered business value. In this episode of Marketer of the Day, entrepreneur David Ledgerwood of Listen Network Co explains how targeted podcast distribution and audience analytics can help agencies move beyond vanity metrics and demonstrate ROI. Drawing on decades of entrepreneurial experience, Ledgerwood shares how he has built and bootstrapped a portfolio of niche businesses through changing markets and economic downturns. He discusses the value of pattern recognition, patience, and knowing when to refine an idea, or move on. His practical business philosophy centers on keeping cash reserves, limiting debt, and letting complementary ventures support one another. The conversation offers useful perspectives for founders, agency leaders, and marketers seeking sustainable growth.
Ledgerwood describes Listen Network’s approach to making podcast advertising more measurable by connecting campaigns with human downloads from carefully defined target audiences. With demographic and professional insights such as age, household income, and job titles, agencies can better understand who engaged with a podcast campaign. That data can help teams report on performance, build client confidence, and shape editorial decisions around audience response. He also explains why podcasting’s open RSS ecosystem can make attribution difficult, and why measurement tools can make the channel easier to evaluate alongside other digital marketing. Beyond podcast analytics, Ledgerwood outlines RIGGG’s owned-media strategy, spanning video, podcasts, newsletters, webinars, and community building. His approach emphasizes creating content infrastructure a company controls rather than depending entirely on social platforms, paid media, or third-party PR.
The episode also explores how to manage several ventures without scattering time and attention. Ledgerwood says his partner group uses a shared backlog and Scrum-like prioritization, shifting focus toward the work closest to revenue while accounting for capacity and well-being. He cautions against assuming every available hour will be productive and encourages founders to plan realistically, make deliberate progress, and avoid rushing. Family is central to his perspective: as a father of five, he prioritizes parenting and recommends putting phones away and making time for meaningful conversations with children. His guiding reminder, “Slow is smooth, smooth is fast,” captures the value of preparation, focus, and doing the work carefully. For entrepreneurs interested in podcast ROI, owned media, bootstrapping, sustainable business growth, and work-life balance, this conversation offers practical ideas and hard-earned lessons.
Quotes:
"The most important thing isn’t the money, it’s your time. You can only work on one or two things at the same time, so what rises to the top really matters."
"I’ve always been passionate about the podcast agency space, but we’ve all been fighting against an open, anonymous protocol. Podcasts don’t play along with traditional marketing data."
"If we can get really precise about who your podcast reaches, that becomes statistically representative of how your target audience behaves when presented with your show."
Contact Details:
- Put Your Podcast in Front of the Right Audience: Visit Listen Network
- Ready to Win Bigger Deals? Build Your Enterprise-Ready Agency Today
- Turn Your Expertise Into Content That Compounds: Start With Riggg
- Discover New Ways to Grow Your Business. Connect with David Ledgerwood on LinkedIn
- Follow David Ledgerwood on Instagram for More Business Insights
- Learn, Think, Grow: Follow David Ledgerwood on X
Podcast: Play in new window | Download (Duration: 31:27 — 43.2MB) | Embed
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1666: Employee Wellness, Sleep Habits with Cynthia Conigliaro
Business owners and corporate leaders aren’t just juggling deadlines and revenue goals they’re also battling exhaustion, information overload, and conflicting health advice. Today’s guest, Cynthia Conigliaro, founder of Work Well Webinars, brings over 25 years of experience in health, mental health, and organizational wellness to help professionals finally make sense of it all. With a background in weight loss, social work, and an MBA in organizational development, Cynthia bridges physical, emotional, and workplace well-being in a way that’s practical and sustainable. In this episode of Marketer of the Day, Cynthia breaks down why so many ambitious, health-conscious people still feel confused and frustrated. Between contradictory nutrition headlines, evolving research, and the constant buzz around new wellness trends, it’s easy to feel paralyzed. Cynthia explains how to cut through the noise by using trusted sources, listening to your own body, and partnering with qualified professionals while still taking advantage of the best that AI and modern tools have to offer.
A major focus of the conversation is sleep as a non-negotiable pillar of health. Cynthia shares simple but powerful strategies to improve sleep quality, from getting morning light within 15–30 minutes of waking, to building a consistent bedtime routine, to handling those middle-of-the-night wakeups without spiraling into anxiety. She also reframes self-care for busy entrepreneurs and leaders: it’s not indulgence, it’s an operational necessity. When leaders model healthy boundaries like not sending 10 p.m. emails they create cultures where employees are more energized, loyal, and productive. The episode also dives into AI and wellness, emphasizing how tools like ChatGPT can support prevention, self-awareness, and better doctor visits without replacing the human element of care. Cynthia shares how AI can help track habits, organize observations, and reduce fear in the moment, while still keeping medical professionals at the center of diagnosis and treatment. AI becomes a complement, not a substitute, for real-world expertise.
Finally, Cynthia explores the role of resilience in both life and business. She explains that resilience isn’t something a lucky few are born with; it’s a skill developed through facing stress, setbacks, and even trauma. By recognizing how much we’ve already overcome, we can build confidence to handle what’s next. Her philosophy of “count your rainbows, not your thunderstorms” captures the mindset shift that allows high achievers to pursue big goals without burning out. If you care about longevity, mental fitness, and building a healthier company culture, this episode will give you practical tools and a fresh, encouraging perspective.
Quotes:
“No one size fits all. What’s good for someone may not be good for you, so a lot of wellness is really listening to your body and choosing strategies that help you live optimally.”
“Sleep is a non-negotiable pillar of health. Some people need more, some need less, but we all need to understand why we actually need to sleep.”
“You really can’t pour from an empty cup. If a leader is exhausted and practicing unhealthy coping styles, it’s not going to set a good example for their employees.”
Contact Details:
- Bring Wellness to Your Team: Visit Work Well Webinars
- Start Your Wellness Journey: Book Your Wellness Coaching Session with Cynthia
- Ready to Take the Next Step Toward Better Well-Being? Connect With Cynthia on LinkedIn
- Follow Cynthia on Instagram and Book Your Wellness Coaching Session
Podcast: Play in new window | Download (Duration: 26:42 — 36.7MB) | Embed
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1665: Fandom Marketing to Create Passionate Fans with David Meerman Scott
Turning customers into raving fans isn’t just for rock bands and blockbuster brands, it's a repeatable business strategy backed by neuroscience. In this episode of Marketer of the Day, David Meerman Scott bestselling author of The New Rules of Marketing and PR, Fanocracy, and the upcoming The Fandom Playbook (late 2026) breaks down how fandom marketing can transform the way you grow your business in an era of AI slop and online chaos. Instead of chasing shallow clicks and fleeting attention, David shows how to build deep, emotional connections with the people you serve. You’ll hear how mirror neurons and our hardwired need for tribe and belonging explain why video, live events, and in‑person gatherings can create “I feel like I know you” relationships at scale even when you’ve never met your audience in real life. David shares practical ways to bring that same energy into your brand, from hosting conferences and meetups to simple client dinners that turn customers into a community. If you’ve ever wondered why fans camp out for product launches, travel across the world for events, or tattoo logos on their bodies, this conversation connects the dots and shows how those same principles apply to your business.
David also reveals one of the most surprising fandom strategies from his new book: tearing down the wall between your personal and professional life. Through stories like the skateboarding dentist who grew new patients by 33% and revenue by 23% just by sharing his passion online, David illustrates how authentic quirks and hobbies such as surfing, classic cars, Lindy Hop dancing, and more make you memorable and magnetic. Passion is contagious, and when you let people see what you genuinely love, you stop being just another provider and start becoming someone they root for. Another underused play David unpacks is unlocking hidden data inside your organization to create value that fans can’t get anywhere else. You’ll learn how companies like Hagerty Insurance built a massive fandom by freely sharing aggregated classic car values, and how a secure data‑room provider turned anonymized M&A activity into must‑have market intelligence for Wall Street. These examples show how so‑called “boring” data, when opened up and packaged well, can become a powerful fandom engine that attracts, educates, and retains your ideal audience.
Throughout the episode, David ties these ideas together with a clear promise: in a world dominated by algorithms, automation, and AI‑generated noise, leaning into humanity is the contrarian advantage. The Fandom Playbook: How to Create Passionate Fans in a World of Online Chaos distills his 20+ years of research into nine concrete plays you can start using today, whether you’re a startup founder, consultant, CMO, nonprofit leader, or creator. If you’re ready to escape online chaos, build a real tribe around your work, and make your marketing feel fun again, this episode will give you both the inspiration and the roadmap to start building your own fanocracy.
Quotes:
"As I was researching this idea of fandom, this thing came up again and again and again: it's a genuine human connection."
"If you can build a tribe of like‑minded people in your business, any way that you can have in‑person connections, you are building fans because you're building a tribe."
"Show us in your business world who you are as a person. What are those things you're passionate about? Because passion is infectious."
Contact Details:
- Turn Customers Into Fans → Explore David’s Strategies
- Stop Selling. Start Creating Fans. Discover the Fandom Marketing Strategy
- Discover Smarter Ways to Create Fans: Connect with David Meerman Scott on LinkedIn
- Follow David Meerman Scott on Facebook for Fresh Marketing Insights
- Want to Build a Fan-First Brand? Follow David Meerman Scott on X
- The New Rules of Marketing & PR on Amazon
- Fanocracy: Turning Fans into Customers and Customers into Fans on Amazon
- The Fandom Playbook on Amazon
Podcast: Play in new window | Download (Duration: 25:19 — 34.8MB) | Embed
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1664: Authentic Networking Dinners with Brandie Weaver
Traditional networking is getting louder, colder, and more automated, and that leaves a lot of business owners feeling disconnected. Instead of forcing small talk over stale coffee, Brandie Weaver of The Listening Table is bringing people back around a real dinner table, where intentional conversation, chef-led meals, and live music create authentic relationships that actually matter. Her traveling supper club attracts Americana music fans, entrepreneurs, and business owners who are tired of transactional networking and ready for genuine community. In this episode, Brandie shares how she turned a decade of producing large-scale music festivals into a high-touch, relationship-driven business model.
She explains how The Listening Table is carefully curated long before guests ever sit down: researching attendees in advance, placing people at the right tables, and using personalized place cards with funny facts to spark conversation and laughter. Phones are put away, awkwardness is eased, and even the most extroverted attendees feel supported walking into the room. The result is a space where people don’t just exchange business cards, they form friendships, travel to shows together, and collaborate on real projects. Brandie also walks through the business side of creating premium, intimate experiences in an industry with tight margins. Running The Listening Table alongside her experiential marketing agency, she uses per-seat pricing, memberships, and smart negotiation with chefs and artists to make each event sustainable without sacrificing quality. For guests, the ticket isn’t just a fancy night out, it's an immersive experience that would easily cost as much as a high-end concert and dinner, but with deeper connection built in.
A big focus of the conversation is Brandie’s belief that the smallest details can have the biggest emotional impact. From vintage 1960s Reader’s Digest books hollowed out to reveal branded flasks, to desserts paired with guests’ favorite cocktails, every touchpoint is designed to make people feel seen, valued, and part of something special. As she looks ahead to future dinners in Driftwood and Bentonville and an expanded calendar of events in 2027, Brandie is committed to one simple goal: ensuring that everyone who joins The Listening Table walks out feeling better, more connected, and more human than when they walked in. If you’ve been craving deeper, more meaningful networking in a fast, AI-driven world, this episode offers a powerful blueprint for creating experiences that people never forget.
Quotes:
"All I can say is, you just have to come to a dinner and experience it; you'll understand, it's magic."
"We don’t just go to attend an event to wander around a room, never have anyone speak to us, and walk out feeling more lonely than we did before we arrived."
"As we disconnect in order to bring us all back together for connection, we just need to be nice to one another and think about these things."
Contact Details:
- The Table Is Waiting; Come Be Part of the Experience: Visit The Listening Table Today
- Turn Your Vision Into Meaningful Results: Explore Brandie Weaver Official Website
- Connect with Brandie on LinkedIn for Her Latest Insights and Ideas
- Follow Brandie on Facebook for Personal Stories, Honest Insights, and Ideas Worth Sharing
- Follow Brandie on TikTok for Quick Insights on Leadership, Partnerships, and Growth
- Don’t miss Brandie’s Next Insight: Follow Her on Instagram
Podcast: Play in new window | Download (Duration: 21:48 — 29.9MB) | Embed
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1663: AI Companions, Careers, and Corporate Culture with Josef Stetter
Business and career success in the age of AI won’t come from using more tools; it will come from using them more humanely and strategically. Today’s guest, Josef Stetter of Cultural Transformation Systems and TEDx speaker behind “Humans, Not Headcounts,” has helped over 12,000 people land jobs they love, with a 90% success rate of getting them employed in under three months using proven systems that blend AI with authentic human storytelling. In this episode of Marketer of the Day, Josef reframes AI not as a task-running “agent” but as a deeply personalized AI companion a system that knows your history, values, style, and accomplishments so well that it can write in your voice, surface forgotten strategies, and even preserve your legacy for your kids long after you’re gone. He shares how his own AI companion has already written successful summit applications, matched his exact tone, and helped him show up more powerfully in his career without losing authenticity.
Josef also dives into the brutal reality of today’s AI-driven job market, where a single remote posting can attract 20,000 resumes and applicant tracking systems decide who gets seen. He explains why generic AI-written resumes fail, and how to stand out by combining AI with quantified impact stories: not “I answered phones,” but “I handled 60 calls a day across 12 lines,” proving capacity, performance, and value at a glance. He reveals how he reverse-engineered top resume scanners and ATS platforms so clients can hit 95%+ match scores across dozens of systems with just one strategically built resume. Beyond individual careers, Josef tackles the crisis of corporate culture and employee disengagement, where disengaged workers are costing the global economy trillions of dollars in lost productivity. He argues that culture isn’t pizza Fridays; it's what people say when managers leave the room. Josef shows how misaligned values, unclear leadership, and the misuse of AI (every department running its own rogue tools) create compliance risks, burnout, and massive internal conflict. He outlines how organizations can introduce AI responsibly by setting clear guardrails, centralizing standards, and preserving human authority over decisions that truly require empathy, ethics, and lived experience.
This conversation is a powerful guide for anyone who wants to use AI to enhance human potential, not erase it. Whether you’re a job seeker trying to break through automated filters, a leader worried about AI chaos inside your company, or a professional who wants an AI “second brain” that actually sounds like you, Josef’s perspective will help you navigate the AI wave with strategy, heart, and integrity. Stay tuned to learn how to build AI companions, design standout resumes, and create workplaces where humans are treated as people not headcounts.
Quotes:
“AI can simulate empathy, but it cannot be empathetic. It can say ‘I’m sorry for your loss,’ but it will never feel the pain of an unfinished conversation with someone who’s gone.”
“It's not about just using AI for marketing tasks; it's about companionship; about having something that knows your stories, your traumas, and how you moved through them so it can help you connect more deeply with other human beings.”
“I’m not here to tell a company how to run its business; I’m here to tell leadership: if this is the culture you want, own it, and then attract the people who actually thrive in that culture.”
Contact Details:
- Discover How Cultural Transformation Systems Can Help You Build a Culture That Drives Results
- Ready to Turn Ideas Into Real Growth? Connect With Josef on LinkedIn and Start the Conversation
- Follow Josef on Facebook for Insights that Drive Change
- Follow Josef on Instagram for Powerful Insights that Can Change How you Lead and Grow
- Follow Josef on X for Fresh Insights that Spark Growth and Action
- Learning My Why on Amazon
- Humans, Not Headcounts on YouTube
Podcast: Play in new window | Download (Duration: 36:08 — 49.6MB) | Embed
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1662: AEC Project Controls and Cash Flow Mastery with Percy Williams
A technically successful AEC project can still turn into a financial disappointment when profits quietly fade and cash flow stalls. Today’s guest, Marine veteran and senior project controls leader Percy M. Williams has spent over 20 years on the financial front lines of architecture, engineering, and construction projects from sub-$10,000 jobs to $100+ million programs. Drawing on experience with major multidisciplinary firms like Aecom, Percy wrote “The AEC Project Controls Playbook” to help firms safeguard profitability, improve cash flow, and deliver on their promises with integrity. In this episode of Marketer of the Day, Percy breaks down why project controls aren’t just back-office paperwork; they're the guardrails that protect margins, stabilize cash, and prevent “profit fade” and “margin erosion.” He explains how seemingly small deviations from standard procedures, such as starting work without a fully executed contract, can snowball into seven-figure problems, delayed billing, and strained vendor relationships. Cash truly is king in the AEC world, and Percy shows how disciplined controls keep money moving and projects viable.
Percy also introduces his leadership framework built on character, competence, courage, and communication, with character as the foundation. Drawing from his Marine Corps background, he shares how a personal “code” values, ethics, and principles you refuse to compromise directly shapes project outcomes and financial performance. Technology and AI can help process data, but they cannot replace the human leadership required to influence behavior, honor commitments, and deliver projects within the profit and margin targets originally sold to the client. This conversation dives into lessons learned, common pitfalls, and practical best practices for AEC project managers, project accountants, and controllers. Percy highlights why frontline leaders must be treated as true business partners, empowered to make timely decisions in the best interest of both the project and the organization because by the time issues reach corporate finance, it’s often already damage control. The AEC Project Controls Playbook serves as a desktop reference, giving both new and seasoned professionals a way to regularly revisit fundamental principles, refresh their discipline, and continuously improve.
If you’re responsible for projects, profitability, or cash flow in AEC or any business that depends on contracts, billing, and delivery this episode offers a clear roadmap to protect margins, strengthen financial resilience, and ensure that what looks good on paper actually holds up in reality.
Quotes:
"When I say deliver projects with integrity, I mean if we sold a project at a certain profit and margin, it’s our responsibility to actually deliver within those parameters."
"You can have a project that looks healthy on paper, but if you don't maintain those project controls disciplines, you will experience profit fade or margin erosion."
"Character plays a key role in the project manager doing the right thing to deliver not just the services and materials, but also the financial commitments we said we would honor."
Contact Details:
- Your Path to Financial Wellness Starts Here: Discover PM&JM Financial Services
- Ready to Transform Your Financial Future? Connect with Percy Williams on LinkedIn
- Build a Stronger Financial Future: Follow Percy Williams on Facebook Today
- The AEC Project Controls Playbook on Amazon
Podcast: Play in new window | Download (Duration: 23:07 — 31.7MB) | Embed
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1661: Christian Trauma Healing and Forgiveness with Kari Dumouchel-Parker
Pain, loss, and unanswered questions about God can leave people feeling stuck, bitter, and far from hope. Guest author Kari Dumouchel-Parker has walked through decades of both mountaintop beauty and real-life trauma, and she’s turned those experiences into powerful stories designed to help readers heal. Drawing from over 30 years of backpacking in the Smoky Mountains, Kari weaves together physical danger, solitude, and problem-solving on the trail with the emotional and spiritual work required to face heartbreak, abuse, grief, and fear. In this episode, Kari shares how her long-distance hiking adventures, getting lost, crossing dangerous rivers, facing washed-out trails, and pushing through exhaustion became living metaphors for navigating life’s hardest seasons. She talks about the moment she chose to trust God’s sovereignty in the face of real-world danger on the Appalachian Trail, and how that same trust shows up in her characters and stories. These wilderness experiences shape the emotional landscape of her novel Sanctuary, where the main character, Tess, leaves everything familiar in Florida to start over in the West, confront her pain, and rediscover who she is.
Kari also dives into her core belief that real growth comes through pain. An easy life rarely forces anyone to look inward, but hard events expose our character, our blind spots, and our patterns. She explains the crucial choice we all face after a traumatic or painful event: either get stuck in bitterness and stay emotionally frozen in that moment, or take time to analyze what happened, what we contributed, and what we’re being invited to learn. For Kari, forgiveness is not about excusing the offender or pretending the hurt didn’t matter; it’s about stepping out of the emotional “ring” with that person and handing them over to God, so we can finally move forward with mental and emotional freedom. Her nonfiction book God Reigns introduces readers to who God is, what His attributes reveal about His heart, and the hidden snares that quietly stall spiritual growth. It tackles the everyday battles believers face, the lies that keep people stuck, and the reality that God deeply loves and pursues each person. Sanctuary, by contrast, uses the vehicle of romance and adventure to explore wounds, identity, and the longing for a great love. Tess encounters two very different men and experiences two distinct kinds of love, all while wrestling with grief, past trauma, and the search for purpose. Kari draws from her own loss of her husband, as well as real-life professional experiences that inspired the backstory of the antagonist, to ground the novel in emotional truth.
Throughout the conversation, Kari emphasizes identity, calling, and the current cultural confusion over who we are. She believes that knowing you are deeply loved by God and understanding something as basic yet profound as truly embracing your identity unlocks the ability to live out your calling with clarity and confidence. Her hope is that Sanctuary and God Reigns will help readers process their pain, release lifelong bitterness, and recognize the quiet ways God has been calling and “wooing” them all along. If you’re wrestling with trauma, spiritual questions, or the fear of starting over, Kari’s stories and insights offer both a challenge and an invitation: you don’t have to stay stuck in the past; you can step into healing, forgiveness, and a new chapter of life with God at the center.
Quotes:
“A lot of people don’t want to forgive because they feel like if they forgive, they’re releasing the other person from the bad event. They think they’re punishing the other person, when in fact they’re punishing themselves.”
“If your life is easy, you don’t have any reason to grow because you don’t have any chance. You think you’re okay; you don’t see that there’s a personality issue that may have led you down the wrong road.”
“When you forgive, God says they’re My problem; they’re not your problem. You can get out of the ring, go on and live your life, and whatever that person’s situation is, it’s not yours anymore.”
Contact Details:
- Discover Stories of Hope & Healing: Explore Kari’s Books Today
- Need Hope for Your Next Chapter? Follow Kari on Facebook for Faith, and Inspiration
- Don’t Let Your Past Define Your Future: Follow Kari on Instagram for Faith, Healing, and Hope
- God Reigns on Amazon
- Sanctuary on Amazon
Podcast: Play in new window | Download (Duration: 19:38 — 27.0MB) | Embed
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1660: Modern Ecommerce & Whatnot Live Shopping with Stephen Somers
Business owners don’t struggle because of a lack of online opportunities; they struggle because they’re overwhelmed by options, stuck in “learning mode,” and unsure which path will actually produce a reliable income stream. Today’s guest, Stephen Somers, co-founder of Marketplace Superheroes, has helped over 10,000 students build real e-commerce income using Amazon and now the fast-growing live shopping platform Whatnot. He’s gone from quitting his 9–5 to running a global education business, bringing in expert instructors, and guiding everyday people into profitable side incomes online. In this episode of Marketer of the Day, Stephen breaks down how social shopping and marketplaces like Amazon and Whatnot are creating powerful opportunities for regular people to build a modern, scalable income stream. He shares how Marketplace Superheroes evolved by buying out a partner, bringing in specialists like Mike Sieben for Amazon and Josh Garrett for Whatnot, and shifting his role from “do everything” founder to content-driven leader supported by a team of experts. Along the way, Stephen is transparent about the hard lessons: including launching a freight company during COVID, getting hit by an 8x spike in shipping costs almost overnight, and losing millions in the process while still keeping the core education business growing.
Stephen also explains why AI is an accelerator, not a replacement for serious entrepreneurs. Yes, AI can answer technical questions and speed up tasks, but it can’t replace nuanced strategy, real-world experience, or the accountability that comes from a community and coaches who are actually doing the work. Marketplace Superheroes now uses AI to build better tools for students, not to eliminate the need for training, coaching, and implementation. The big differentiator? Students who win are the ones who move past endless research and take consistent action testing products, launching streams, buying inventory, and iterating instead of consuming more content. A major focus of this conversation is Whatnot, an $8B-and-growing live auction marketplace where sellers stream from their phones and buyers bid in real time. Stephen explains why trading card games are currently one of the hottest categories, how his team went from zero followers to nearly $250K in 125 days and close to half a million dollars in sales, and how students like Shameen from Canada have generated over $50K in under 90 days even without being “typical” young livestream influencers. He also zooms out to the bigger trend of live social shopping, why TikTok and others are racing into this space, and how being early, but strategic can create a serious edge.
Stephen closes by sharing a powerful mindset shift inspired by Charlie Munger: “To get what you want, you have to deserve what you want.” Instead of obsessing over self-belief, he encourages aspiring sellers to believe in a proven system, follow the process step by step, and take the actions that a “deserving” person would actually take whether that’s launching an Amazon listing, going live on Whatnot, or finally committing to an e-commerce side hustle. If you’re interested in building a modern online income stream with Amazon, Whatnot, or live social shopping, and you’re ready to graduate from passive learning to real execution, this episode will show you what’s working right now and how Marketplace Superheroes are helping people do it.
Quotes:
"Nothing’s really changed; people used to get free information from books, then from YouTube, now from AI, but the problem is still the same: most people don’t do anything with it."
"Stop believing in yourself; it’s a waste of your time if you’ve never sold anything online before. Instead, believe in the system in front of you."
"We didn’t jump on Whatnot just because it was new; we did it because it perfectly aligned with what we already do and we wanted, for once, to actually be early on something."
Contact Details:
- Build your Online Business with Marketplace Superheroes
- Turn Amazon Into your Next Growth Opportunity: Connect with Stephen Somers on LinkedIn
- Follow Stephen Somers on Facebook for Amazon Business Insights and Strategies
- Follow Stephen Somers on Instagram to Unlock Smarter Amazon Strategies
- Join Stephen Somers on Threads for e-commerce Tips
- Subscribe to Marketplace SuperHeroes on YouTube for Proven Amazon Growth Strategies
- Follow Stephen Somers on X for Smarter Amazon Strategies
Podcast: Play in new window | Download (Duration: 29:05 — 39.9MB) | Embed
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1659: Children’s Book Self-Publishing and Strong Heroines with Amy Hawk
Amy Hawk covers Children's Fantasy in this Marketer of the Day episode. Amy Hawk explains how a story written for her niece became a children's fantasy about courage, independence, friendship, and believing in yourself. She began writing The Tales of Rumpus in 1998 because she wanted her niece to have a strong female hero who did not wait to be rescued.
Many aspiring authors dream of writing a children’s book that actually matters, but get stuck on questions like: “Is my story original enough?”, “How do I find an illustrator?”, and “What if I self-publish and nobody ever sees it?” Today’s guest, Amy Hawk, answers all of those questions through the real-world journey behind her inspirational children’s fantasy, The Tales of Rumpus. In this episode of Marketer of the Day, Amy shares how she wrote The Tales of Rumpus back in 1998 for her niece, long before “strong princesses” were common in mainstream media. Instead of another damsel waiting to be rescued, her heroine Princess Caitlyn is proactive, determined, and learning to stand on her own bringing themes of independence, earned confidence, equality, chosen family, curiosity, and human connection into a magical adventure kids actually want to read. Amy walks us through her free-flowing writing process letting characters “live in her head,” discovering the real ending as she wrote, and revisiting the manuscript every decade to refine it while realizing the story’s lessons still hold up over time. She explains why she finally decided it was “time to share” the book, and how modern self-publishing made it possible to publish a deeply personal project without compromising her vision.
We also dive into the real-world challenges of illustration and production. Amy describes collaborating with Page Publishing’s illustration team to pull the images out of her imagination like an enormous bullfrog and a round, cornerless castle onto the page. She talks candidly about when to stand firm (keeping Princess Caitlyn blonde and blue-eyed like her niece) and when to compromise on visual details to keep the project moving. Amy also previews how she’s now experimenting with AI-assisted illustration for future projects, balancing creative control with respect for traditional artists. Beyond creation, Amy is refreshingly honest about the hardest part for most authors: marketing. She shares her painful lessons with fake “promoters,” wasted marketing spend, and empty promises of Amazon visibility. You’ll hear what’s actually been working: legitimate partnerships, features in magazines and newspapers, radio and especially podcast interviews, plus in-person book signings where she connects with readers face-to-face. As a family nurse practitioner in the ER, she’s grounded, used to high-pressure environments, and very clear-eyed about the realities of chasing visibility as an indie author. Amy also previews her growing story universe and brand beyond The Tales of Rumpus. You’ll hear about Daniel the Dragon, a finished spinoff that’s now in final edits, as well as her planned adult murder mystery, Footprints in the Sand, built from real-life experiences and stories from the ER. Throughout, she ties her work back to her own life being adopted, finding a chosen family, and why those themes naturally show up in her fiction.
If you’re a children’s author, aspiring writer, or creative professional wondering how to bring a meaningful story to life, protect your vision, and navigate the messy world of self-publishing and promotion, this episode delivers both inspiration and hard truths. Amy closes with the quote that carried her through nurse practitioner school and now applies perfectly to authorship and entrepreneurship alike: “Will it be easy? No. Will it be worth it? Yes.” To learn more about The Tales of Rumpus, invite Amy on your show, or explore her upcoming projects, visit AmyHawkBooks.com or contact her via info@amyhawk.com or 573-280-7448.
Quotes:
“I'm a firm believer in things happening how they're supposed to, so I think it was a story that was meant to be shared when it finally was meant to be shared.”
“I love book signing because I love to meet people and just look them in the eye, because people can't lie to you when they look you in the eye.”
“I wanted her to have somebody who was super strong, who knew what she wanted, was going to go get it, and who was a female character.”
Contact Details:
- Discover your Next Magical Adventure: Explore Amy Hawk’s Books Today!
- Follow Amy Hawk on Facebook for More Stories, Book Updates, and Adventures!
- Discover More Stories, Magic, and Book Updates: Follow Amy Hawk on Instagram!
- Join the Adventure: Follow Rumpus Tales on X!
- The Tales of Rumpus on Amazon
Podcast: Play in new window | Download (Duration: 21:12 — 29.1MB) | Embed
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