Leading Change

Google's latest AI announcement isn't just about a new feature.
 
It raises a much bigger question.
 
How much of our lives are we willing to let technology observe before consent becomes an afterthought?
 
In this episode of Leading Change in the Wild, I explore Google's new ambient audio memory feature, the rise of AI-powered wearables, and what these technologies reveal about the future of privacy, surveillance, and human consent.
 
Because this is not just about Google.
 
From Meta's smart glasses to Microsoft's workplace monitoring tools, we're seeing a growing trend toward collecting more of our conversations, behaviors, and daily interactions than ever before.
 
Here's what I unpack:
Google's new ambient audio memory feature and what it means for users
How AI-powered devices are expanding the scope of data collection
Why wearable technology is changing expectations around privacy
The growing tension between convenience and meaningful consent
How workplace AI monitoring is extending surveillance beyond personal devices
Why younger generations are increasingly pushing back against always-on technology
The leadership and societal questions we should be asking before these technologies become the norm
 
The takeaway is clear. AI is not just changing the way we work.
 
It is changing the relationship we have with privacy, trust, and consent.
 
This is not just a technology conversation. It is a leadership one.
 
Because once constant surveillance becomes normal, it becomes much harder to ask whether we ever truly agreed to it.
 
👇 Let's discuss:
 - Where should we draw the line between convenience and privacy? - Are companies collecting more data than consumers truly understand? - What role should consent play in the future of AI?
 
🔔 Subscribe for weekly insights on digital transformation, leadership, and emerging technologies.

Most organizations are trying to adopt AI using structures, processes, and leadership models that were built for the industrial age.
And that may be the biggest obstacle standing in the way of transformation.
In this episode of Leading Change in the Wild, I sit down with Jana, co-author of The Octopus Organization, to explore why traditional organizations struggle with change and what leaders need to do differently in the age of AI.
From bureaucratic decision-making to AI adoption metrics that drive the wrong behavior, this conversation dives into the patterns holding organizations back and the leadership shifts required to move forward.
Here’s what we unpack:
The difference between a "Tin Man" organization and an "Octopus" organization
Why traditional organizational structures struggle in today's environment
The anti-patterns preventing successful AI adoption
The dangers of chasing AI adoption metrics instead of business value
Why leaders need to personally experiment with AI before expecting their teams to adopt it
The role of distributed intelligence and decentralized decision-making
How to move from AI hype to meaningful business transformation
Why speeding up a bad process is not transformation
The takeaway is clear. AI does not just challenge technology strategies. It challenges the way organizations are designed.
Because the companies that thrive will not be the ones with the most AI tools. They will be the ones that learn, adapt, and empower people to solve real problems.
📚 The Octopus Organization by Jana and Phil is available now!
👇 Let’s discuss:
Which anti-pattern do you see most often in organizations today?
Are companies focusing too much on AI adoption and not enough on value creation?
What would it take for your organization to become more adaptive?
🔔 Subscribe for weekly insights on digital transformation, leadership, and emerging technologies.

Anthropic has issued another warning about artificial intelligence. But this time, the concern is not job displacement or productivity.
It is the possibility that AI could soon begin improving itself.
In this episode of Leading Change in the Wild, I break down Anthropic’s latest report on recursive self-improvement and what it means if AI reaches a point where it can build, test, and improve future versions of itself with minimal human involvement.
But beyond the technology itself, this report raises some deeper questions.
Who should be leading these conversations? And what happens when the companies warning us about the risks are also the companies building the technology?
Here’s what I unpack:
What recursive self-improvement actually means
Why Anthropic believes we may be approaching a major AI inflection point
The challenge of keeping humans in control of increasingly capable systems
The “prisoner’s dilemma” at the center of AI development
Whether AI companies can simultaneously be the warning system and the builder
How regulation, competition, and incentives collide in the AI race
The connection between recursive AI, model collapse, and the dead internet theory
The takeaway is not just about technology.
It is about incentives, accountability, and who gets to shape the future of AI.
Because if the people raising the alarm are also the people benefiting from the outcome, we need to ask harder questions about how these decisions are being made.
👇 Let’s discuss:
Should AI companies be leading conversations about AI regulation and ethics?
Are we approaching a point where AI can meaningfully improve itself?
Is a global pause realistic, or are we already too far down the path?
🔔 Subscribe for weekly insights on digital transformation, leadership, and emerging technologies.

For years, some of the biggest names in AI warned us that artificial intelligence would eliminate jobs, disrupt entire industries, and fundamentally reshape the workforce.
Now, many of those same leaders are saying something very different.
In this episode of Leading Change in the Wild, I break down the growing narrative shift coming from AI executives and why the people who once warned of mass job displacement are suddenly talking about productivity, augmentation, and the importance of human connection.
So what changed?
Here’s what I unpack:
The fear-based messaging that defined the early AI boom
Why AI leaders are changing their tone on job displacement
Sam Altman’s surprising comments about human interaction and AI
Dario Amodei’s shift from replacement to productivity multiplier
How IPO pressure, public sentiment, and adoption challenges may be influencing the narrative
Why human connection still matters in an increasingly automated world
The bigger leadership lessons hidden inside this messaging shift
The takeaway is clear. When the narrative changes this dramatically, it is worth asking why.
 
Because whether AI becomes a replacement, an enhancement, or something in between, leaders need to think critically about the messages they are hearing and who benefits from them.
This is not just a technology conversation. It is a leadership one.
Because the future of work will not be shaped by technology alone. It will be shaped by the choices we make about how we use it.
👇 Let’s discuss:
Why do you think AI leaders are changing their message?
Was the original narrative wrong, or is this new one?
Is the truth somewhere in the middle?
🔔 Subscribe for weekly insights on digital transformation, leadership, and emerging technologies.

College commencement speeches are supposed to inspire graduates about the future. Instead, many students are booing the moment AI gets mentioned.In this episode of Leading Change in the Wild, I break down the growing backlash against AI messaging at college graduations and why so many executives seem completely disconnected from how young people actually feel about the future of work.Because this is not just about AI. It is about the growing sentiment gap between leadership and everyone else.From viral commencement speeches to AI failures during graduation ceremonies themselves, we are watching a generation push back against the idea that an AI-dominated future is inevitable.Here’s what I unpack:
Why graduates are booing AI-focused commencement speeches
The growing disconnect between executives and young workers
How fear-based AI messaging is shaping Gen Z’s outlook on work
Why “adapt or get left behind” is failing as a leadership strategy
The contradiction in telling people they shape the future while also saying AI is inevitable
How AI hype is starting to overshadow human achievement and creativity
What leaders should be saying instead if they want real trust and adoption
The takeaway is clear. People are not resisting technology. They are resisting the way it is being forced on them.This is not just a technology conversation. It is a leadership one.Because the future of AI will not be shaped by fear, mandates, or hype. It will be shaped by how well leaders can bring people into the conversation.📄 Download the AI Strategy Gap report for deeper insights into the growing disconnect between leadership and employees.→ https://mailchi.mp/roloffconsulting/aigap👇 Let’s discuss:Were the students justified in booing these speeches?Do executives understand how younger generations feel about AI?What should leaders be saying about AI instead?🔔 Subscribe for weekly insights on digital transformation, leadership, and emerging technologies.

If AI is supposed to replace human productivity, why are OpenAI and Anthropic spending billions to build human-led services companies?
In this episode of Leading Change in the Wild, I break down the back-to-back announcements from OpenAI and Anthropic to launch venture-backed consulting and implementation firms designed to help companies adopt AI.
And hidden inside these announcements is a quiet admission.
AI is not a magic wand.
Because despite all the hype around instant productivity and “AI-first” transformation, companies are running into the same problem technology implementations have always faced. The people side of change.
Here’s what I unpack:
 Why OpenAI and Anthropic are launching AI-focused services companies 
 The real reason enterprise AI adoption has been so difficult 
 How the “AI magic wand” narrative is colliding with reality 
 Why buying AI tools without strategy creates confusion and waste 
 The ongoing gap between technology implementation and true transformation 
 Why leadership, training, and communication matter more than ever 
 The danger of skipping over change management in the rush to adopt AI 
The takeaway is clear. AI alone will not transform your business.
Real transformation happens when technology, leadership, process, and people work together.
This is not just a technology conversation. It is a leadership one.
Because the companies that win with AI will not be the ones that adopt it the fastest. They will be the ones that adopt it with the most intention.
👇 Let’s discuss:
 What do these new AI services companies signal to you? 
 Is your organization focused more on technology or strategy? 
 Do you think most companies are prepared for the people side of AI adoption? 
🔔 Subscribe for weekly insights on digital transformation, leadership, and emerging technologies.

Companies are racing to adopt AI. But what happens when they start incentivizing the wrong behavior?
In this episode of Leading Change in the Wild, I break down the rise of “tokenmaxxing” and how AI leaderboards inside major companies are driving massive usage… without delivering real value.
 
From engineers burning tokens to hit leaderboards to companies blowing through millions in AI spend, we are starting to see the consequences of chasing usage instead of outcomes.
Here’s what I unpack:
 What “tokenmaxxing” is and why it’s spreading across companies 
 How AI leaderboards are driving the wrong behaviors 
 The massive cost of AI usage without clear strategy 
 Why companies are burning through budgets faster than expected 
 The connection between AI spend and layoffs 
 What the data actually says about AI productivity gains 
 Why incentivizing usage instead of value is a leadership failure 
The takeaway is clear. More AI usage does not equal more productivity.
If you measure the wrong thing, you get the wrong outcome.
This is not just a technology problem. It is a leadership problem.
Because the way you incentivize behavior will determine whether AI becomes an advantage or a liability.
👇 Let’s discuss:
 Is your company tracking AI usage or actual outcomes? 
 Have you seen behavior like this inside your organization? 
 What should leaders be measuring instead? 
🔔 Subscribe for weekly insights on digital transformation, leadership, and emerging technologies.

There’s a growing gap in how AI is being experienced inside organizations. And most leaders are missing it.
In this episode of Leading Change in the Wild, I share original research from her audience that reveals a major disconnect between leadership and employees when it comes to AI adoption.
Because while leaders are optimistic, employees are overwhelmed. And that gap is creating more problems than progress.
This is not just about AI. It is about how we lead change.
Here’s what I unpack:
 The stark difference between leadership and employee sentiment toward AI 
 Why most companies don’t actually have an AI strategy 
 How hype and external pressure are driving decision-making 
 The reality of AI creating more work instead of less 
 Why poor training and unclear direction are hurting adoption 
 The “FOMO cycle” and how it keeps repeating 
 What leaders need to do differently to close the gap 
The takeaway is clear. This is not a technology problem. It is a leadership problem.
If you want real results from AI, you have to start with the problem, not the tool. And you have to bring your people into the process.
Because without alignment, strategy is just noise.
📄 Download the full report here: https://mailchi.mp/roloffconsulting/aigap
👇 Let’s discuss:
 Does this gap exist in your organization? 
 Is AI making your work easier or more complicated? 
 What would need to change for AI to actually deliver value? 
🔔 Subscribe for weekly insights on digital transformation, leadership, and emerging technologies.

44% of Gen Z workers say they’ve tried to “sabotage” AI at work.
But before we jump to conclusions, we need to ask a better question. Why?
In this episode of Leading Change in the Wild, I break down the latest data on Gen Z’s shifting sentiment toward AI and why this reaction has less to do with resistance to technology and more to do with how it’s being introduced.
Because this is not a story about a generation rejecting AI. It is a story about what happens when leadership gets the rollout wrong.
From fear-based messaging to “AI-first” mandates, we are watching a growing disconnect between how companies are deploying AI and how employees are experiencing it.
Here’s what I unpack:
 The data behind Gen Z’s declining trust and rising anxiety around AI 
 What “AI sabotage” actually looks like in the workplace 
 Why poor rollout strategies are driving risky and reactive behavior 
 The impact of fear-based narratives around job loss and automation 
 How AI adoption is increasing workload, not reducing it 
 The tension between productivity expectations and work-life balance 
 Why Gen Z’s pushback may actually be a signal leaders need to listen to 
The takeaway is clear. This is not a Gen Z problem. It is a leadership problem.
If you want adoption, you cannot skip the hard work. That means training, transparency, and real conversations about how AI will be used and why.
AI is not an easy button. And your people are not the barrier. They are the signal.
👇 Let’s discuss:
 Do you think Gen Z is resisting AI or responding to how it’s being rolled out? 
 How is AI impacting workload and expectations in your organization? 
 What would make AI adoption feel more intentional and less forced? 
🔔 Subscribe for weekly insights on digital transformation, leadership, and emerging technologies.

OpenAI just released its policy vision for the “intelligence age” and at first glance, it sounds promising.
But when you look closer, the story starts to fall apart.
In this episode of Leading Change in the Wild, I break down OpenAI’s latest policy document and the growing gap between what AI companies say and what they actually do.
Because this is not just about policy. It is about trust, accountability, and whether we should believe the narrative being presented to us.
From energy subsidies to workforce impact, this document raises more questions than it answers.
Here’s what I unpack:
Why OpenAI’s “pay their own way” stance contradicts real-world actions 
The role of public funding and who is actually subsidizing AI infrastructure 
The disconnect between “people-first” messaging and enterprise partnerships 
Why consulting-driven AI adoption often excludes the very people doing the work 
The limitations of how AI companies define “human-centered” roles 
The lack of real mechanisms for public and worker input 
Why this document feels more like a PR move than a true shift in strategy 
The takeaway is simple. Saying “people first” is not the same as acting like it.
If AI companies want trust, they need to earn it through action, not just policy statements.
This is not just a technology conversation. It is a leadership one.
Because the future of AI will not be shaped by what companies promise. It will be shaped by what they actually do.
👇 Let’s discuss:
 Do you trust AI companies to put people first? 
 Where do you see the biggest gap between messaging and reality? 
 What responsibility should companies have before regulation steps in? 
 
🔔 Subscribe for weekly insights on digital transformation, leadership, and emerging technologies.