Leading Change

Ema Roloff
  • 5.0 (2)
  • Business
  • Updated Weekly

Welcome to Leading Change, where we dive into the real conversations shaping the future of work. Hosted by Ema Roloff, this series brings together business leaders, change-makers, and innovators to explore the intersection of technology, change management, and leadership in today’s evolving workplace. Each episode is packed with actionable insights, candid stories, and fresh perspectives on navigating transformation—whether it’s leveraging emerging tech, leading through disruption, or building resilient teams. If you’re passionate about creating meaningful change and thriving in the digital era, this is the podcast for you. Let’s redefine what it means to lead in a world where change is the only constant.

  1. 1d ago

    The Digital Transformation Machine

    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.

  2. Sep 1

    Cloudforce and the Future of SaaS

    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.

  3. Aug 25

    Cognitive Surrender and the Hidden Cost of AI

    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.

  4. Aug 18

    Has the AI Witch Hunt Begun?

    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.

  5. Jul 28

    Is the Future of AI Open?

    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.

  6. Jul 21

    Are We Replacing Trust with Surveillance?

    What happens when technology stops being a tool for trust and starts becoming a tool for surveillance? In this episode of Leading Change in the Wild, I explore the growing controversy surrounding Flock Safety cameras and why the debate extends far beyond law enforcement. Because this is not just a story about surveillance cameras. It is a conversation about what happens when organizations, governments, and leaders begin relying on technology to monitor people instead of building trust with them. Here's what I unpack:  - How Flock Safety cameras are changing modern policing  - The recent controversies surrounding surveillance and false accusations  - Why surveillance technology is raising new ethical questions  - The growing misuse of monitoring tools by those with access  - How workplace surveillance mirrors what's happening in society  - The relationship between trust, accountability, and technology  - Why leaders should think carefully before replacing trust with monitoring The takeaway is clear. Surveillance may reduce uncertainty, but it cannot replace trust. Whether we're talking about governments, police departments, or organizations, every new monitoring tool forces us to ask the same question: Are we creating safer systems, or simply less trusting ones? This is not just a technology conversation. It is a leadership one. Because the strongest organizations are not built on constant surveillance. They are built on trust, transparency, and accountability. 👇 Let's discuss:  - Where should we draw the line between security and surveillance?  - Can organizations build trust while increasing employee monitoring?  - When does technology become a substitute for good leadership? 🔔 Subscribe for weekly insights on digital transformation, leadership, and emerging technologies.

  7. Jul 14

    Is AI Breaking the Free Internet?

    57% of all internet traffic is now generated by bots. That statistic alone changes the way we think about the internet. In this episode of Leading Change in the Wild, I explore Cloudflare's latest announcement to block AI crawlers and why it may mark the beginning of a fundamental shift in how the internet is funded, searched, and experienced. Because this is not just about AI bots. It is about the future of the internet itself. Here's what I unpack: Why AI bots now generate the majority of internet traffic Cloudflare's new strategy to block AI crawlers How Google's AI search is changing the economics of the web Why the attention economy is beginning to break down Cloudflare's new pay-per-crawl marketplace for AI companies What happens when websites are no longer visited by humans Whether this could be the beginning of the end of the free internet The takeaway is clear. The internet was built on human attention. As AI increasingly consumes information instead of people, the business model that has powered the web for decades is being rewritten. This is not just a technology conversation. It is an economic one. Because the future of the internet will depend on who creates value, who consumes it, and who ultimately pays for it. 👇 Let's discuss: Should AI companies have to pay to access online content? Will Cloudflare's approach change the balance of power between publishers and AI companies? Is this the beginning of a paid internet for everyone? 🔔 Subscribe for weekly insights on digital transformation, leadership, and emerging technologies.

  8. Jul 7

    How AI Is Quietly Redefining Consent

    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.

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About

Welcome to Leading Change, where we dive into the real conversations shaping the future of work. Hosted by Ema Roloff, this series brings together business leaders, change-makers, and innovators to explore the intersection of technology, change management, and leadership in today’s evolving workplace. Each episode is packed with actionable insights, candid stories, and fresh perspectives on navigating transformation—whether it’s leveraging emerging tech, leading through disruption, or building resilient teams. If you’re passionate about creating meaningful change and thriving in the digital era, this is the podcast for you. Let’s redefine what it means to lead in a world where change is the only constant.

Information

  • Creator
    Ema Roloff
  • Years Active
    2024 - 2026
  • Episodes
    81
  • Rating
    Clean
  • Copyright
    © Copyright 2024 All rights reserved.
  • Show Website
    Leading Change