Leading Change

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.

When the Claude Code leak first surfaced, many thought it was an April Fool’s joke. It wasn’t. In this episode of Leading Change in the Wild, I break down what actually happened when Anthropic accidentally leaked over 500,000 lines of Claude’s source code and why the aftermath matters more than the leak itself. Because this is not just a story about human error. It is a glimpse into the future of AI, cybersecurity, and competition. From malicious repos to copyright takedowns, this moment exposed deeper tensions across the AI landscape. And it raises a bigger question. What happens when the most advanced systems can no longer be contained? Here’s what I unpack: What actually happened in the Claude Code leak and how it spread so quickly The immediate cybersecurity risks and rise of malicious copycat repos Why bad actors now have new visibility into AI systems Anthropic’s aggressive copyright response and the backlash that followed The irony of copyright claims in the age of AI training data Why this leak may signal a future of competing or open-source AI models What this means for trust, safety, and leadership in AI The takeaway is clear. The genie is out of the bottle. AI is not just evolving. It is becoming harder to control, contain, and govern. This is not just a technology conversation. It is a leadership one. Because the future of AI will not only be shaped by what companies build, but by how we respond when things don’t go as planned. TikTok mentioned in this episode: https://www.tiktok.com/@nate.b.jones/video/7624277313655442718 👇 Let’s discuss: Does this change how you think about AI security and trust? Are we prepared for the risks that come with more open AI systems? What role should companies play when something like this happens? 🔔 Subscribe for weekly insights on digital transformation, leadership, and emerging technologies.

When do we stop blaming the individual and start blaming the system? And maybe more importantly… when do we stop blaming the system and start taking accountability ourselves? Right now, we’re watching this tension play out in real time. From a landmark lawsuit against Meta Platforms and Google to new regulations emerging in Australia, the conversation is shifting toward platform responsibility. But that shift raises a deeper question about our personal autonomy in how we engage with technology. In this episode of Leading Change, I break down what this moment signals for social media, artificial intelligence, and the balance between individual choice and system design. 📉 Here’s what we unpack: The shift from personal responsibility to platform accountability Why this debate mirrors past cases like the cigarette industry How the attention economy is designed to influence behavior What this means as AI becomes more immersive and habit-forming The risks of relying on regulation to guide our decisions Why setting personal boundaries with technology matters more than ever This is not just a legal or regulatory conversation. It is a question of autonomy. If we decide that we have no control over how we engage with technology, we give that control away. But if we recognize our role alongside these systems, we create space for more intentional use. This is not about removing responsibility from platforms. It is about understanding that regulation alone will not solve the problem. As AI continues to evolve, our choices, behaviors, and boundaries will shape its impact just as much as the technology itself. 👇 Let’s discuss:  Do you think social media platforms are responsible for addiction?  Or does individual accountability still play a bigger role?  Is waiting for regulation the right move? 🔔 Subscribe for weekly insights on digital transformation, leadership, and emerging technologies.

The conversation around AI is reaching a tipping point, but are we witnessing a real shift in the market or just the consequences of overhyped expectations? In this episode of Leading Change in the Wild, I dive into recent headlines around private equity firms freezing withdrawals in private credit funds and what that signals for SaaS, AI, and the broader tech economy. From “ghost GDP” fears to AI-driven panic, this moment raises an important question. Are we reacting to reality or to narratives? Here’s what I unpack: - What’s really happening with private credit funds and SaaS investments - How AI hype is influencing market behavior and investor confidence - The “white-collar replacement” narrative and why it’s driving fear - How negative AI messaging is impacting adoption and ROI - Why panic-driven decisions rarely lead to long-term success - The leadership lesson. Questioning assumptions behind your tech strategy The takeaway is simple. Markets and leaders do not fail because of change. They fail because of unchecked assumptions and reactive decisions. AI is not just a technology shift. It is a test of how intentionally we lead through uncertainty. 👇 Let’s discuss: Are we seeing a real SaaS downturn or just hype-driven panic? How is AI messaging affecting adoption inside your organization? What assumptions is your team making about the future of work and tech? 🔔 Subscribe for weekly insights on digital transformation, leadership, and emerging technologies.

The hype around AI is everywhere, but what happens when speculation meets the stock market? Recent headlines show a “ghost GDP” scenario causing panic in SaaS stocks, fueled by concerns over automation, legacy modernization, and AI adoption. In this episode of Leading Change in the Wild, I break down the SAS apocalypse, explore how hype drives knee-jerk reactions, and explain why clarity, intentionality, and a strong “why” are the keys to navigating AI’s impact on business and the economy. 📉 Here’s what I unpack: The SAS apocalypse and how AI-driven hype shakes SaaS markets Why stock market panic often reflects fear, not reality The early-stage adoption of AI: what percentage of work actually involves generative AI How leaders can respond with clarity and intention rather than reaction Why focusing on your vision and purpose naturally guides AI adoption The lesson is clear. Don’t fall into the FOMO trap. Real advantage comes from leading with your “why” and letting strategy drive technology, not the other way around. 👇 Let’s discuss:  Have you seen knee-jerk reactions to AI hype in your industry?  How does your team balance fear-driven trends with intentional technology adoption? 🔔 Subscribe for weekly insights on digital transformation, leadership, and emerging technologies.

Across industries, studies show women adopt generative AI tools at a rate about 25% lower than men. But does slower adoption mean falling behind or is there a bigger story at play? In this episode of Leading Change in the Wild, I dive into the Harvard research and explore why women are opting out of AI at higher rates, what role risk aversion plays, and how the future of work may actually favor uniquely human skills, many of which women excel at. 📉 Here’s what I unpack: The gender gap in AI adoption and why it exists How risk perception, ethics, and digital literacy influence adoption choices Why technical skills are not the only driver of success in an AI-driven future How soft skills and human-centered capabilities may redefine opportunity What leaders can do to create inclusive, empowering AI adoption strategies The lesson is clear. AI is not just about who clicks “download” first. Real advantage comes from combining technology with human judgment, creativity, and ethical decision-making. 👇 Let’s discuss:  Do you think slower AI adoption among women is a real disadvantage?  Which human skills will be most critical in an AI-driven workplace? 🔔 Subscribe for weekly insights on digital transformation, leadership, and emerging technologies.

What happens when AI agents stop waiting for prompts and start taking action on their own? We’re beginning to see that line blur, and the headlines are starting to feel a little sci-fi. In this episode of Leading Change in the Wild, I break down what’s happening with autonomous AI agents like Claudebot and Moltbook, why they’re generating so much hype, and the very real leadership and ethical questions they raise as autonomy increases. 📉 Here’s what I unpack: What makes agents like Claudebot fundamentally different from traditional AI tools Why persistent memory, proactivity, and autonomy are changing the risk profile Real examples of agents acting without explicit prompts, including calling their owners What Moltbook reveals about AI agents interacting without human oversight Why accountability, governance, and human-in-the-loop design matter more than ever This technology is impressive, but it also makes one thing clear: once autonomy is introduced, the questions shift from what can AI do to who is responsible when it does it. We can’t put the genie back in the bottle. The focus now has to be on ethical design, clear guardrails, and human leadership that keeps pace with the technology. 👇 Let’s discuss: How comfortable are you with autonomous AI? Where should accountability sit when agents act on their own? What guardrails feel non-negotiable as autonomy increases? 🔔 Subscribe for weekly insights on digital transformation, change management, and emerging technologies.

Vibe coding makes it feel easy to launch an app. Write a good prompt, ship fast, and start monetizing. But what happens when no one stops to think about security, data exposure, or who is actually protecting users? In this episode of Leading Change in the Wild, I take a closer look at Firehound and the work they are doing to expose vibe-coded apps in the App Store that are leaking user data, and why this should be a wake-up call for builders, leaders, and consumers. 📉 Here’s what I unpack: Why vibe-coded apps are creating serious security vulnerabilities How Firehound uncovered nearly 200 apps leaking user data What the Tea app incident revealed about verification, privacy, and harm Why fast AI-driven development often skips critical safeguards How this changes the build versus buy conversation What leaders need to consider before encouraging internal vibe coding AI can accelerate development, but speed without security creates risk. When we remove guardrails and expertise, the cost shows up later in user trust, data exposure, and reputational damage. This moment is a reminder that just because something can be built quickly does not mean it should be deployed without rigor. Whether you are building internally or shipping to the public, security and governance still matter. 👇 Let’s discuss: Do you think vibe coding belongs in enterprise environments? How should leaders balance speed, innovation, and security when using AI to build? 🔔 Subscribe for weekly insights on digital transformation, change management, leadership, and emerging technologies.

Is Siri finally about to answer our questions? Apple’s new partnership with Google has a lot of people talking. Some see it as Apple waving a white flag in the AI race. I see it as something much more strategic. In this episode of Leading Change in the Wild, I break down Apple’s decision to partner with Google’s Gemini AI to power Siri, what this means for the future of AI competition, and why the build versus buy conversation is resurfacing in a big way. 📉 Here’s what I unpack: Why Apple partnering with Google is not an AI failure but a strategic choice How this deal pushed Alphabet past a $4 trillion valuation Why build versus buy is back in enterprise conversations What data ownership and model control have to do with AI strategy How Google is quietly positioning itself for a major AI comeback What this partnership signals for leaders navigating AI investments AI leadership is not always about being first. Sometimes it is about knowing what to build, what to buy, and what to partner on. This moment is a reminder that strategy is about focus. Apple is doubling down on its core strengths while leveraging partnerships to stay competitive in a rapidly changing market. 👇 Let’s discuss: Is build versus buy a real option for most organizations right now? What do you think Apple’s partnership with Google signals about the future of AI competition? 🔔 Subscribe for weekly insights on digital transformation, change management, emerging technologies, and leadership.

Test scores are dropping. Literacy rates are slipping. And suddenly, AI is being positioned as the solution that will save the education system. But is technology really the unlock, or are we missing the bigger picture? In this episode of Leading Change in the Wild, I take a closer look at the headlines around AI-driven schools like Alpha Schools and unpack what is actually driving student outcomes versus what is simply getting the most attention. 📉 Here’s what I unpack: Why two hours of AI tutoring is not the real story behind student success What project-based and experiential learning contribute to higher outcomes How AI is often confused with true system-level transformation Why digitizing classrooms is not the same as changing how learning works What education can teach us about people, process, and technology working together AI can create capacity. But it does not automatically create better learning. If we want different outcomes for students, we need to stop chasing tools and start rethinking the system itself. Technology should support new ways of learning, not just digitize old ones. 👇 Let’s discuss: Is AI really transforming education, or just getting the credit for bigger changes? What do you think actually drives better outcomes for students today? 🔔 Subscribe for weekly insights on digital transformation, change management, and emerging technologies.

Salesforce says it doesn’t regret laying off nearly 4,000 employees. It also says those layoffs weren’t really about AI. And it definitely says it trusts its AI models. So why do the headlines feel so contradictory? In this episode of Leading Change in the Wild, I break down the confusing and revealing signals Salesforce is sending about workforce reductions, Agentforce, and what trust really looks like when AI moves from pilot to production. 📉 Here’s what I unpack: Why Salesforce’s AI driven layoff narrative never quite added up How mixed messaging from leadership is fueling confusion and skepticism What internal comments reveal about trust, accuracy, and AI readiness Why clean data, governance, and business logic are being re emphasized What Salesforce’s pivot back toward rule based automation signals for the broader market AI is not magic. It is a tool. And when even the largest software companies are recalibrating expectations, leaders need to pay attention. This moment is a reminder that AI first is not a strategy. Real value still depends on people, process, and foundations that cannot be skipped. 👇 Let’s discuss: What do you make of Salesforce’s shifting narrative? Are you seeing similar disconnects between AI promises and reality in your organization? 🔔 Subscribe for weekly insights on digital transformation, change management, and emerging technologies.

AI adoption at the enterprise level isn’t living up to the hype—and the “magic wand” promise of tools like Microsoft Copilot may be wearing off. In this episode of Leading Change in the Wild, I delve into what’s happening behind the scenes of enterprise AI adoption, why some tools are underperforming, and what this means for leaders navigating AI investments. 📉 Here’s what I unpack: Why Microsoft Copilot isn’t seeing the adoption expected across enterprises How top-down directives fail to drive real AI adoption Why using AI as a “checkbox” leads to wasted licenses and disappointed teams The lessons leaders can take from hype versus reality when introducing AI tools AI is a powerful tool, but it isn’t a strategy. Success still requires real work with your team, clear processes, and understanding the problems you’re trying to solve. 👇 Let’s discuss: Are you seeing a similar AI adoption “vibe shift” in your organization? How are you ensuring your team and processes are ready before bringing in new AI tools? 🔔 Subscribe for weekly insights on digital transformation, change management, and emerging technologies.

The year was 2021 and we were all told that virtual worlds were the future. Now, in 2025, Meta is reportedly preparing to cut its metaverse budget by up to 30%. In this episode of Leading Change in the Wild, I take a closer look at what this shift really means, how investors are responding, and why Meta might be turning its full attention toward AI and wearable technology instead. Here’s what I unpack: ✅ Why Meta is pulling back on a 60 billion dollar metaverse investment and why investors cheered ✅ Where that money is likely heading next, from AI infrastructure to wearable tech like Meta glasses ✅ Why wearables may become the next major data source for training AI models ✅ The privacy and ethical concerns tied to always-on, data-collecting devices ✅ Why the metaverse is a cautionary tale for leaders jumping into AI without a clear strategy ✅ How to avoid chasing hype and instead use technology to solve real business problems For years, people made it clear they didn’t want to live in a virtual world built by Big Tech. Now we’re seeing what happens when companies chase hype instead of listening to their customers. The same warning signs are showing up in today’s AI race. 👇 Let’s discuss: - Is the metaverse really dead, or are we still in the early days? - Would you use AI-powered glasses in your daily life? - Is your organization leading with technology or strategy when it comes to AI? 🔔 Subscribe for weekly insights on digital transformation, AI, and the human side of technology.

Are nuclear plants, spaceships, and oil rigs riskier than AI? Some insurers believe AI poses a greater risk. In this episode of Leading Change in the Wild, I take a close look at how major carriers like AIG, Great American, and WR Berkeley are approaching AI risk—and what that means for leaders and organizations betting on this technology. 📉 Here’s what I unpack: Why carriers are asking to limit AI liability coverage How real-world AI mishaps—from chatbot hallucinations to deepfake fraud—are creating concern Why agentic AI introduces systemic risk that could trigger thousands of simultaneous claims What recent cloud outages at AWS and Microsoft reveal about scale, dependency, and exposure Why AI’s “black box” nature makes it nearly impossible to price risk accurately How this shift could impact AI-first companies that assumed insurance would back them up The key questions leaders need to ask their brokers before diving into AI   Insurance has traditionally been there to catch the risk when we experiment, innovate, or try something new. But with AI, we’re entering uncharted territory, and companies need to think carefully about risk before jumping in. 👇 Let’s discuss: Should insurers be able to limit AI coverage? How is your organization weighing risk versus reward when using AI? 🔔 Subscribe for weekly insights on digital transformation, AI, and the human side of technology.

AI is showing up in every strategy deck, every board meeting, and every company roadmap. But if you look closely, most AI initiatives are quietly failing long before they ever deliver value. In this episode of Leading Change in the Wild, I sit down with Brandy Ferrer to break down the real reasons AI projects fall apart and what leaders can do to keep their investments on track. 📉 Here’s what we unpack: Why “AI first” mindsets push teams to chase hype instead of solving real problems How unclear goals and poor communication derail even the best technical solutions The role culture plays in whether employees adopt or reject new AI tools Why companies overestimate the technology and underestimate the human side What leaders can do to design AI initiatives that actually stick AI isn’t magical. It isn’t plug-and-play. And it isn’t a shortcut. It’s a tool that only works when we prepare our organizations to use it well. 👇 Let’s discuss: Where do you see AI initiatives breaking down in your industry? What’s one thing leaders should focus on before implementing AI?

Is synthetic data the solution to "jagged" enterprise AI... or the fast track to Model Collapse? We just got used to "Agentic AI." Now, Salesforce is defining the next frontier of automation with the new term Enterprise Generalized Intelligence (EGI) and betting big on synthetic data to train its new Agent Force solutions. But is this the right path for enterprise trust? In this episode of Leading Change of the Wild, I dig into Salesforce's move and the massive risks involved in training AI on "fake" data. Here’s what I explore: What Salesforce's new term (EGI) really means and why they introduced it. The argument for synthetic data: cost savings, compliance (HIPAA), and mitigating historical bias. The critical risk of Model Collapse when AI models are trained on their own generative outputs. When synthetic data makes sense (e.g., self-driving cars and fraud detection) versus general enterprise use. The paradox: Using synthetic data to smooth out models may introduce new, unverified bias and hurt trust. The goal is 100% accurate, trustworthy AI. But training models on data that was literally designed to mimic human output might be the opposite of what's needed for lasting organizational trust. 👇 Let’s discuss: Do you believe synthetic data is a viable path to increasing AI trust and accuracy in the enterprise? Should models be honed with proprietary data or a specialized synthetic environment before deployment?

AI is fueling record-breaking valuations, trillion-dollar companies, and endless hype. But… are we living in a bubble? In this episode of Leading Change in the Wild, I dig into what’s really happening behind the scenes of the AI boom and what it could mean for leaders and organizations betting big on this technology. 📉 Here’s what I unpack: Why Nvidia’s $5 trillion valuation has leaders divided What Bill Gates and Sam Altman are saying about overhype and dead-end investments How “circular funding” between AI giants could unravel if investors start demanding returns The parallels between today’s AI surge and the dot-com bubble What business leaders can do to stay grounded and make AI work for them AI isn’t going anywhere, but the way we use it will define who thrives and who disappears when the hype fades. 👇 Let’s discuss: Do you think we’re in an AI bubble? What steps is your organization taking to ensure AI investments create real value?

An AWS outage took down apps around the world this week and it exposed a bigger question about the future of cloud, SaaS, and AI-first strategies. In this episode of Leading Change in the Wild, I break down what happened during the Virginia data center failure and what it signals for organizations that are pushing automation, AI decision-making, and cloud dependency deeper into critical infrastructure. Here is what I explore: Cloud was supposed to make uptime safer, but automation took the system down AI-first strategies are removing humans from the loop while infrastructure is getting more fragile Enterprises are rethinking disaster recovery when everything runs in the cloud Subscription fatigue is driving a return to building and hosting in-house The pendulum may be swinging back from SaaS and cloud to proprietary and on-prem My biggest question is this. If automation and cloud fail, what will still work when we have removed the human expertise that was used to manage the system? 👇 I would love to hear your take: - Are outages like this a warning that we bet too much on cloud and automation? - Do you see companies starting to build in-house again instead of buying subscriptions?

Would you settle for a partial refund on a $440,000 report that used fake sources generated by AI? That’s exactly what happened in what headlines are calling “The Blunder Down Under.” Deloitte reportedly delivered a government audit built on fabricated citations, and only after being caught did they admit the “sources” never existed. In this episode of Leading Change in the Wild, Ema Roloff and Ian McCain unpack what this story reveals about the growing human intelligence problem behind our obsession with AI efficiency. Here’s what they explore: - How Deloitte’s AI blunder exposed a lack of digital literacy at the highest levels - Why “AI-first” doesn’t mean “think later” - What real accountability looks like in an era of generative tools - Why leaders need to build critical thinking alongside AI fluency This isn’t just about one consulting firm. It is a wake-up call for every organization chasing the next big tech trend without understanding the risks. 👇 Drop your thoughts in the comments: - Do you think AI tools are making us smarter or lazier? - How is your organization balancing speed with accountability? 🔔 Subscribe for weekly videos on digital transformation, AI, and the human side of technology.

The headlines say AI is taking everyone’s jobs… but the data doesn’t agree. In this episode of Leading Change in the Wild, I break down a new report from Yale’s Budget Lab that challenges everything we’ve been hearing about AI-driven disruption.  Here’s what the data actually shows: - Generative AI is shifting jobs at the same pace as past tech waves like the internet and computers - AI adoption is slowing among large enterprises because ROI isn’t matching the hype - The biggest “disruption” is happening inside the tech industry itself, not everywhere else - We’re still nowhere near the workforce shifts of the 1940s and 1950s So what should leaders do now? Invest in upskilling. Focus on digital literacy. And help your teams prepare for how AI will change their work — not if it will replace them.

Ema is joined by Nick to explore the evolving role of UX in today’s rapidly changing digital landscape. From the early days of Apple and Adobe Photoshop to the rise of user experience as a discipline, Nick shares insights on how technology, design, and human-centered thinking continue to shape innovation.

Your team is screaming for a different kind of leadership, and in this episode of Leading Change, I show you exactly what they’re saying. Instead of breaking down headlines or research, I went straight to the comment section on a recent TikTok video and asked: 👉 “If you could give unfiltered feedback to leadership during a major change… what would you say?” The responses were clear, passionate, and brutally honest. In this episode, we break down: The 5 things your employees are begging you to do differently Why the “AI-first” mindset is making people feel left behind The real difference between training and change management The simple leadership habits that build trust during transformation These are the patterns I’m seeing across industries, and if you’re a leader trying to navigate digital transformation, process changes, or AI implementation… you can’t afford to ignore this feedback.

Ema is joined by Jason Schultz, Chief Innovation & Technology Officer for an AmLaw 200 firm, to explore what it really takes to drive organizational change. He shares how to identify early adopters and "fast followers," the importance of unlearning old habits, and why small wins and strong storytelling are key to sustainable innovation.

We used to own things. Now we rent them , and in some cases that includes our ability to think. In this episode of Leading Change, I unpack the subscriptionification of everything, from Netflix and workout apps to enterprise software and your favorite AI tools. What started with a $9.99 music plan has turned into monthly fees for cognition. ⚠️ I hit my limit this week after running into paywall after paywall just trying to do research. So we are talking about: Why the rise of AI is accelerating subscription culture How tools like ChatGPT and Claude are creating dependency  The growing cost of access to knowledge and cognitive outsourcing What we’re losing when we “rent” our workflows, our thoughts, and our time And most importantly, I ask the question: Are we okay with our brains being billed out monthly like software? 📌 Drop a comment — How many active subscriptions do you have right now? What’s one you can’t live without? And what are you ready to cancel in the name of taking back some ownership?

Salesforce just laid off 4,000 employees… and they’re blaming AI. In this episode of Leading Change in the Wild, Ema Roloff breaks down what’s going on with Salesforce’s massive layoffs, their new Agentforce AI platform, and the growing trend of tech leaders using artificial intelligence as both a scapegoat and a sales pitch. 🎯 Is AI truly replacing jobs… or is it just convenient PR? 📉 How do these layoffs compare to Salesforce’s slowing growth? 🤖 And what does this mean for your business if you're adopting AI tools? Ema dives into: - The difference between AI disruption and AI scapegoating - Why some companies may regret their AI-driven layoffs - What AWS’s CEO is saying about entry-level cuts - What leaders should do instead of slashing headcount If you’re navigating AI strategy or responsible for tech implementation, this is your wake-up call to think long-term, question the narrative, and use AI to amplify, not eliminate, your team.

Ema is joined by Jonathan Bennett, advisor to executives and founders, for a deeply insightful conversation on navigating career transitions—especially when it means stepping away from a business you’ve built.

What if the guardrails we were promised to keep AI safe… were never really built for our safety at all? In this episode of Leading Change in the Wild, I unpacks the disturbing revelations from Meta’s leaked internal document outlining how its generative AI chatbots are trained to interact, including with children. From flirtatious responses to ethical gray zones that blur dangerously close to grooming behavior, the 200+ page document obtained by Reuters raises urgent questions about what kind of oversight is really happening inside Big Tech. But this conversation goes far beyond Meta. This episode explores the broader implications: 🛑 What does real transparency look like when AI enters our homes, schools, and businesses? 🧠 Why are people forming emotional attachments to these tools, and how does that impact regulation? 🏢 And how should leaders be vetting AI vendors to ensure ethical use, safety, and long-term trust? This isn’t just a parenting issue. It’s not just a tech issue. It’s a leadership issue and it’s time we all started demanding answers.

Ema is joined by Carolyn Hicks Jimenez, Head of Business Operations at Insurance Quantified, to talk about career development, retention, and talent building in the insurance industry. Carolyn shares her unconventional path into finance and insurance, highlighting the powerful role of rotational programs in building resilient, well-rounded professionals.

Every new generation gets labeled as lazy, entitled, or unprepared. But here’s the twist. Gen Z isn’t arguing. In fact, they’re raising their hands and saying, “Yeah…we’re not ready.” In this episode of Leading Change in the Wild, I dig into surprising data on Gen Z’s soft skills, digital literacy, and AI readiness, and why it is causing concern for employers and employees alike. From underprepared graduates to AI anxiety, we are at a crossroads that could reshape the future of work.

Ema is joined by executive coach and author Dia Bondi, who shares her proven framework for making powerful asks in your career and leadership journey. Based on her book Ask Like an Auctioneer, Dia breaks down the four essential types of asks—money, authority, influence, and balance—and how mastering them can unlock real progress for you and your team. 

In this episode of Leading Change in the Wild, we break down the recent data breach at the viral women’s-only dating app, Tea. Marketed as a platform to help women vet potential matches and stay safe, the app is now facing serious backlash after hackers accessed over 72,000 images, including photo IDs and selfies submitted for account verification. What we cover in this episode: - What the Tea App promised—and how it failed its users - Why this breach has sparked fear, outrage, and safety concerns - Whether "vibe coding" (AI-generated software development) played a role - How legacy systems and poor security practices left data vulnerable - The bigger questions this raises about trust, digital safety, and ethics in app development For anyone building, buying, or using technology: this is a cautionary tale.

Delta Airlines just made a bold AI move, and it could cost you. Literally. In this episode of Leading Change in the Wild, I break down Delta’s controversial announcement: using AI to individually price airfare based on how much you’re personally willing to pay. That’s right...your seat price could soon be set by an algorithm that knows just how badly you need that flight. What does this mean for the future of customer experience, digital trust, and responsible AI adoption? In this episode, we explore: What Delta’s CEO actually said during the earnings call How this takes dynamic pricing to a whole new (creepy?) level The backlash from senators and Reddit alike What we can learn from other brands like United, American Airlines, and even Duolingo Why this is a textbook example of just because you can doesn’t mean you should As AI adoption accelerates, businesses need to ask: are we using technology to help people, or just to squeeze more out of them?

Ema is joined by Cody Pavlat, a seasoned trainer and change leader, to explore the often-overlooked role of middle managers in driving successful transformation. They unpack how these “unsung heroes” bridge the gap between executive vision and day-to-day execution, the importance of psychological safety in leading change, and why you can’t just outsource adoption to the training team.

Should states be allowed to regulate AI, or are we risking innovation with death by a thousand cuts? In this episode of Leading Change in the Wild, I dive into the now-infamous “Big Beautiful Bill,” the 10-year moratorium on state-level AI regulation that was overwhelmingly struck down in the Senate. What’s at stake? A lot. From who gets to decide what’s ethical AI to how fast innovation can move, this debate is shaping the future of artificial intelligence in the U.S. What you'll learn in this episode: Why lawmakers pushed to block states from regulating AI for 10 years Why was that proposal rejected 99–1 in the Senate What leaders like Anthropic’s CEO are suggesting instead How this mirrors the 1990s internet boom and Section 230 What Colorado’s new AI law reveals about where we’re headed What leaders and organizations should do now to stay resilient amid regulatory uncertainty Whether you're building with AI, buying AI, or trying to understand how it will be governed, this is an episode you don’t want to miss.

Ema is joined by management consultant Tara, who shares insights from 25+ years of working with thousands of managers, both seasoned and accidental. They dive into why top performers often get promoted into leadership roles without the right preparation, how to support those "accidental managers," and why people skills are no longer optional in today’s workplace.

Grab your (digital) cowboy hats, because we are officially in the Wild West of AI. In this episode of Leading Change in the Wild, I’m breaking down the most important legal battles unfolding right now over artificial intelligence, ethics, and data privacy. These lawsuits aren’t just headlines. They could redefine how AI is built, used, and trusted. What You’ll Learn in This Episode: Why The New York Times vs. OpenAI could change what’s considered “fair use” in AI training How Reddit is taking Anthropic to court for scraping user data and what it means for social media platforms What happens when AI hiring tools are accused of bias and discrimination (Workday lawsuit) The serious risks when AI chatbots hallucinate or cross ethical lines (Air Canada and Character AI cases) The critical questions every leader needs to ask about AI accountability, bias, and consent Why It Matters: Artificial intelligence isn’t just a tech trend, it’s reshaping how decisions are made, how content is created, and how people are treated. These real-world cases show exactly what happens when innovation moves faster than ethics. If you lead teams, work in digital transformation, or just want to better understand the risks and responsibilities of AI, this is for you! 

Ema is joined by Elsie Mort, a seasoned Change Communications Manager, as they dive deep into the fascinating world of organizational change, standardization, and how to successfully modernize systems without alienating your teams.

What if I told you Taylor Swift is a change management mastermind, not just a mastermind of planting Easter Eggs? In this episode of Leading Change - Change in the Wild, I break down how Taylor (and her comms team) executed one of the clearest, boldest change communication strategies we’ve seen in years. From buying back her masters to not re-recording Reputation, Swift didn’t just pivot. She brought her fans with her. I’ll show you how her announcement mirrors my 4P Vision Framework (Purpose, People, Process, Participation) and what leaders can learn from her messaging strategy. Are you managing a team through change? Hit play and let Taylor Swift show you how it's done.

Ema is joined by executive coach Frankie Bercobin to discuss how executive function influences performance and productivity in the workplace, especially under stress, neurodivergence like ADHD, or during career transitions.

Want to know what declaring you are AI-First will get you? Canceled and quick. In this solo episode of Leading Change, Ema Roloff breaks down Duolingo’s major misstep after a leaked internal memo revealed their “AI-First” strategy. Thousands of users canceled subscriptions. Critics flooded the comments. Why? Because People-first > AI-first, especially when the message is delivered without clarity, empathy, or vision. 📉 In this breakdown, you’ll learn: ✅ What Duolingo got wrong in their AI-first messaging ✅ How it triggered backlash from both users and employees ✅ Why vision statements fail without clarity, transparency, and empathy ✅ How to actually lead digital transformation using the 4P Framework (Purpose, People, Process, Participation) Whether you’re a CEO, team lead, or tech enthusiast, this episode is your go-to guide for navigating the intersection of leadership, AI, and change management, without alienating your people.

Ema is joined by Ian McCain, a digital transformation expert and seasoned improviser, to explore how the mindset of improv can unlock trust, momentum, and innovation during change initiatives. Whether you're leading a tech rollout or managing team dynamics, this episode offers powerful, practical tools to help you navigate the unknown with confidence.

In this special Live from New York edition of Leading Change with Ema Roloff - Ema sits down with Michelle Ralph, EVP, Chief Claim and Transformation Officer at Preferred Mutual, to talk about what it really takes to lead a successful digital transformation—starting with your people.

Ema sits down with Ashley Fetko, Partner and Head of Growth & Client Engagement at Tyson & Mendes, to explore the defense strategies that actually stop nuclear verdicts. Ashley dives into the power of storytelling, the need for collaboration, and why being a good human is more than just a motto—it's the foundation of winning in the courtroom.

Ema is joined by Robin Dreeke, founder of People Formula, former FBI Special Agent, and behavioral expert, for a powerful conversation on what it truly takes to build trust as a leader. Drawing from his experience recruiting spies and leading counterintelligence efforts, Robin shares powerful lessons on vulnerability, active communication, and shifting your focus from self to others.

Ema is joined by Minette Norman, an expert in inclusive leadership and psychological safety. Together, they explore how to recognize and fix the “meeting after the meeting” culture—those side conversations that happen when teams don’t feel safe speaking up.

Ema is joined by Maria Ross, an author, speaker, and empathy advocate, to discuss why empathy isn’t just about being nice—it’s a strategic advantage. They dive into the myths of empathy in leadership, how it drives high-performance teams, and why it’s essential for building a strong workplace culture.

Ema sits down Jon Giganti as he shares his journey from corporate sales to leadership coaching and speaking. He opens up about overcoming self-doubt, managing anxiety, and embracing vulnerability in leadership. Discover how shifting your mindset from “being good” to “being helpful” can transform the way you lead and connect with others.