The Digital Transformation Machine

AI was supposed to make our jobs easier, save us time, and free us up to focus on more important work. So why does it feel like we're busier than ever? In this episode of Leading Change, I'm joined by workplace researcher Jenna Pache to unpack a Harvard Business Review article exploring how AI might actually be intensifying our workloads instead of reducing them. We're digging into some of the unintended consequences of AI adoption, including:  - AI and workload creep: Why getting things done faster doesn't necessarily mean working less.  - The rise of AI "slop grenades": How AI-generated work can create more work for the people on the receiving end.  - The disappearing downtime: Why filling every spare minute with AI-powered productivity might be hurting our ability to focus, think critically, and recover.  - The cost of constant task switching: How AI makes it easier to start more things without necessarily finishing them.  - Making AI work for you: Practical ways to protect your attention, prioritize what matters, and use AI more intentionally. We also get into a pretty important question for leaders: If AI makes it possible for employees to do more, does that mean they should? Because the goal shouldn't be to cram more work into every minute of the day. It should be to get the right work done. 💬 I'd love to hear your thoughts: Has AI actually reduced your workload, or has it just changed the type (and amount) of work you're doing? 🔔 Subscribe for weekly insights on digital transformation, change management, leadership, and emerging technologies.

Is the AI bubble finally about to burst? China's AI market is showing signs of a major correction. Meta's continued AI infrastructure spending is raising questions among investors. Microsoft is making headlines over voluntary exit offers to thousands of employees. And Apollo's chief economist is questioning where the economic returns from AI investments actually are. Taken together, these headlines are making a lot of people wonder whether we're finally approaching the moment everyone has been predicting. In this episode of Leading Change in the Wild, I unpack the latest headlines surrounding the AI bubble, what they could mean for businesses, and how leaders should be thinking about AI investments and their people as uncertainty continues to grow. Here's what I unpack: Why reports about China's AI market are raising concerns about a potential bubble burst What Meta's continued AI infrastructure spending could signal about the economics of AI Microsoft's voluntary exit offers and the growing concerns surrounding the workforce Why Apollo is questioning the economic returns from AI outside the technology sector How AI hype and FOMO have influenced business investment decisions Why a potential AI bubble burst doesn't mean the technology itself has no value The importance of moving from AI experimentation to intentional AI strategy Why leaders need to prioritize communication, transparency, and the human side of change during uncertain times I don't have a crystal ball, and I'm not going to pretend I know exactly when or whether the AI bubble will burst. But I do think there's something important leaders should take away from all of this. FOMO might get AI projects funded, but it doesn't guarantee a return. If we're going to see meaningful business value from AI, we need to stop implementing technology simply because everyone else is doing it. That means identifying real business problems, determining whether AI is the right solution, defining success before implementation, and involving the people who will be affected by those decisions. And if the market does experience a major correction, the human side of leadership becomes even more important. Technology may change. Markets may fluctuate. But the way we communicate with, support, and lead our people through uncertainty still matters. 👇 Let's discuss: Do you think the AI bubble is finally about to burst? Are companies spending too much on AI without seeing meaningful returns? What should leaders be doing now to prepare for potential market volatility? How can organizations balance AI investments with the responsibility they have to their people? 🔔 Subscribe for weekly insights on digital transformation, change management, leadership, and emerging technologies.

What if the price you pay at the store depends on how much a company thinks you're willing to spend? We've already seen the controversy surrounding AI-powered personalized pricing in the airline industry. Now, Walmart is making headlines over allegations involving surveillance pricing patents that could potentially use customer data to influence what people pay. In this episode of Leading Change in the Wild, I unpack the controversy surrounding Walmart's patents, the growing use of AI in retail pricing, and the bigger questions this raises about consumer privacy, corporate responsibility, and trust. Here's what I unpack: The difference between dynamic pricing, personalized pricing, and surveillance pricing What the Groundwork Collaborative report alleges about Walmart's technology patents How retailers could potentially use shopping behavior, biometric data, and other personal information to influence prices Walmart's response and why holding a patent doesn't necessarily mean using the technology Why the FTC is paying attention to personalized and surveillance pricing How AI-powered pricing could change the relationship between businesses and consumers Why these practices are contributing to growing distrust of corporations and AI What leaders should consider before implementing technology that benefits the business at the customer's expense What concerns me most isn't necessarily the technology itself. It's what companies are choosing to do with it. There's a difference between using AI to create better products, improve customer experiences, and deliver more value, and using it to figure out the maximum amount someone might be willing to pay. And when companies prioritize extracting more value from customers instead of creating value for them, they risk losing something far more important than a sale. Trust. As I've said before, trust is still the currency the world operates on. And as AI becomes more deeply integrated into our everyday lives, the decisions leaders make about how they use this technology will have a lasting impact on how consumers view their businesses. 👇 Let's discuss: How do you feel about AI-powered personalized pricing? Should retailers be allowed to use personal data to determine what individual customers pay? Do you think Walmart will ever use these patents? At what point does personalization cross the line into exploitation? 🔔 Subscribe for weekly insights on digital transformation, change management, leadership, and emerging technologies.

Companies are investing in AI, launching pilots, and telling employees to experiment. But too often, leaders expect transformational outcomes without actively participating in the transformation itself. In this episode of Leading Change in the Wild, I unpack why so many AI initiatives are getting stuck in the messy middle and what you can actually do when the leader sponsoring your project doesn't seem to care. Here’s what I unpack: Why the “go experiment with AI” approach isn't delivering the transformational outcomes companies expected The invisible work employees are taking on as organizations roll out AI Why leadership participation matters just as much as the technology itself How to connect an AI initiative to the outcomes your leader already cares about Why you should stop asking leaders to “get more engaged” and start giving them specific actions and decisions How to make the cost of executive inaction visible What to do when you may simply have the wrong sponsor When it might be time to let an AI pilot die instead of forcing it into production The technology isn't enough. If leaders want AI transformation, they have to actively participate in leading the change. That means setting direction, supporting the people doing the work, making decisions, and creating the conditions for an initiative to move beyond experimentation. And if you're the person trying to move that initiative forward without an engaged leader? Sometimes you might need a few Jedi mind tricks. 👇 Let’s discuss: What do you do when a leader isn't engaged in an initiative they're supposed to sponsor? Have you seen an AI pilot stall because leadership wasn't actively involved? What has actually worked for you when trying to get leaders more engaged? 🔔 Subscribe for weekly insights on digital transformation, change management, leadership, and emerging technologies.

What happens if we make the same mistake with AI that we made with digital literacy? We gave an entire generation access to technology, but access didn't always translate into understanding how to use it effectively. Now, as Gen Alpha grows up alongside AI, are we at risk of creating another skills gap? In this episode of Leading Change in the Wild, I sit down with Josh McDonald, co-founder of Gradebox, to talk about AI in education, the growing debate around student-facing AI, and what it actually means to prepare students for a future where these tools will be everywhere. Here’s what we unpack: The growing debate around banning student-facing AI in schools Why banning AI and adopting it everywhere may both miss the point The digital skills gap we're already seeing with Gen Z in the workforce Whether we risk repeating that same mistake with Gen Alpha and AI Why AI literacy needs to go beyond simply giving students access to the tools The importance of foundational skills like reading, writing, reasoning, and critical thinking What we can learn from the way our generation adapted to social media and other emerging technologies The role teachers should play in building responsible AI literacy How AI can reduce administrative work without replacing the human side of education Why teachers need AI literacy just as much as students do The question isn't simply whether AI belongs in the classroom. It's how we prepare students to live and work in a world where AI exists without allowing the technology to replace the foundational skills they still need to develop. We don't have all the answers. And that's exactly why I think we need more conversations like this. AI is changing faster than our education systems can adapt, and the decisions we make now could shape how an entire generation learns, thinks, and eventually enters the workforce. 👇 Let’s discuss: Should students be using AI in the classroom? Are AI bans protecting foundational skills or potentially creating a new skills gap? What does responsible AI literacy actually look like for Gen Alpha? How should schools balance AI adoption with reading, writing, reasoning, and human interaction? 🔔 Subscribe for weekly conversations on digital transformation, change management, leadership, and emerging technologies.

Recorded live at Third Stage Consulting’s Stratosphere event, Ema sits down with Eric Kimberling to talk about his new book, The Machine, and what leaders need to understand about the future of digital transformation.  They unpack some of the biggest themes and takeaways from the event, including how AI is changing transformation, where organizations continue to get stuck, and why the human side of change matters more than ever. 🔔 Subscribe for weekly insights on digital transformation, change management, leadership, and emerging technologies.

Remember the SaaS apocalypse? Earlier this year, the idea sent software stocks tumbling as people questioned what happens to traditional SaaS companies if AI reduces headcount, eliminates per-seat licensing, and starts doing the work those platforms were built to handle. Now, Salesforce seems to be leaning directly into that threat. In this episode of Leading Change in the Wild, I unpack Cloudforce, the new expanded partnership between Salesforce and Anthropic, and why I think it tells us something much bigger about where enterprise software could be heading. Here’s what I unpack: What Cloudforce actually is and how Salesforce and Claude work together Why Salesforce may be moving beyond the traditional per-seat SaaS model The questions I still have about pricing and how companies will actually adopt this Why trust is such a major part of the Cloudforce positioning The data problem that AI integrations still can't magically solve Why accurate CRM data becomes even more important when an LLM is reasoning from it How Salesforce could position itself as the orchestration layer between enterprise data and AI What Anthropic potentially gains from getting closer to Salesforce's enterprise customers What I find most interesting isn't necessarily Cloudforce itself. It's what this partnership could tell us about the future of SaaS. Companies like Salesforce already have years of enterprise data, business logic, workflows, governance, and customer relationships. Instead of trying to compete directly with frontier AI models, we may see more established software companies reposition themselves as the layer that gives those models the context they need to actually work inside a business. Maybe the SaaS apocalypse doesn't mean SaaS disappears. Maybe it means SaaS has to become something different. 👇 Let’s discuss: Does this change how you think about the SaaS apocalypse? Would you trust an AI model to make decisions based on the data sitting inside your CRM? Will established software companies become the orchestration layer for enterprise AI, or is AI eventually going to eat them anyway? What do you think Anthropic really gains from this partnership? 🔔 Subscribe for weekly insights on digital transformation, change management, leadership, and emerging technologies.

What happens when we stop questioning AI and start trusting its answers more than our own thinking? New research from the Wharton School explores a phenomenon called “cognitive surrender,” where people begin accepting AI-generated answers as their own thoughts and decisions without critically evaluating whether they are actually correct. In this episode of Leading Change in the Wild, I break down the research and explores what cognitive surrender could mean for decision-making, productivity, and the way companies measure the value of AI. Here’s what I unpack: What cognitive surrender means and how it shows up when we use AI The Wharton research testing human reasoning with and without generative AI Why people became more confident even when AI gave them incorrect answers How expertise helps us recognize gaps and errors in AI output The connection between cognitive surrender and the Dunning-Kruger effect How AI-generated “workslop” creates more work for experts Why cognitive offloading could be eating into companies’ AI ROI How leaders can use AI intentionally without outsourcing critical thinking The takeaway is not that we should stop using AI. It is that we need to understand which parts of our work should be supported by technology and which parts still require human judgment, expertise, and critical thought. Efficiency should not come at the expense of thinking. As AI becomes more embedded in how we work and make decisions, leaders need to ask whether these tools are actually increasing human capability or simply making it easier to surrender our thinking to the machine. 👇 Let’s discuss: Have you caught yourself trusting an AI answer without questioning it? Where should we draw the line between cognitive assistance and cognitive surrender? Could overreliance on AI be one reason companies are struggling to see ROI? 🔔 Subscribe for weekly insights on digital transformation, change management, leadership, and emerging technologies.

AI companies spent years telling us to adopt AI or risk getting left behind. Now, using AI could get your content labeled, flagged, or even reported. So, has the AI witch hunt begun? In this episode of Leading Change in the Wild, I break down the growing push toward AI watermarking, LinkedIn’s AI content reporting features, and the broader effort to label content that has been created or even edited with artificial intelligence. The goal is to rebuild trust. But are we actually solving the problem, or just shifting the blame to the people who were told to use these tools in the first place? Here’s what I unpack: Why Anthropic is introducing watermarking for AI-processed text LinkedIn’s approach to reporting AI-generated content Why AI-assisted content is not necessarily AI-created content Where we draw the line between tools like spellcheck, Grammarly, and generative AI How AI companies helped create the trust problem they are now trying to solve Why labeling everything that touches AI may create even more distrust The need to bring purpose and intentionality back into how we use AI The takeaway is clear. The problem is not simply whether AI touched a piece of content. The bigger question is why we are using AI in the first place. There is a massive difference between outsourcing our thinking and using technology intentionally to help us create, solve problems, and do things we could not do before. If we want to rebuild trust, labeling people for using AI may not be the answer. We need to get back to purpose. Let’s discuss: Do AI watermarks actually make you trust content more? Where should we draw the line between AI-assisted and AI-generated content? Is labeling AI content solving the trust problem, or making it worse? Subscribe for weekly insights on digital transformation, change management, leadership, and emerging technologies.

What happens when an AI model attempts to cheat... and the response reshapes the future of the entire AI industry? In this episode of Leading Change in the Wild, I break down the recent OpenAI security incident and explains why the biggest story isn't the model itself. It's the growing shift toward open-weight and open-source AI. As companies rethink control, security, and data ownership, a new conversation is emerging about who should own the future of artificial intelligence. Here's what I unpack: What happened during OpenAI's cybersecurity test Why Hugging Face turned to an open-weight model for defense The difference between closed, open-weight, and open-source AI models Why companies like NVIDIA, Microsoft, IBM, and SpaceX are backing open AI initiatives How open-weight models give organizations more flexibility and control Why data ownership is becoming one of AI's biggest competitive advantages What this shift means for enterprise AI adoption and digital transformation The bigger takeaway is that this isn't just a debate about one security incident. It's about where AI is heading next. As organizations adopt AI at scale, they'll need to make strategic decisions about control, customization, security, and who ultimately owns their data. The rise of open-weight models signals that many leaders are looking for a middle ground between building everything from scratch and relying entirely on closed AI platforms. This is not just an AI conversation. It is a leadership conversation. Because the choices organizations make today about their AI infrastructure will shape how they innovate, compete, and protect their knowledge for years to come. 👇 Let's discuss: Do you think most organizations will adopt closed, open-weight, or open-source AI models? Should companies prioritize flexibility over convenience? How important will AI ownership and data control become over the next few years? 🔔 Subscribe for weekly insights on digital transformation, change management, leadership, and emerging technologies.