KPMG - Why AI ROI Depends More on Workforce Behavior Than Technology

How LaunchDarkly Is Helping Enterprises Control Shadow AI in DevOps

What happens when AI-generated code ships faster than humans can properly review it, and who takes the blame when something breaks? In this episode of AI at Work, I sit down with Cameron Etezadi, Chief Technology Officer at LaunchDarkly, to tackle one of the most uncomfortable questions facing modern software teams. As developers increasingly rely on AI coding assistants, copilots, and public LLMs to accelerate delivery, organizations are finding themselves caught between productivity gains and growing governance risks. Cameron explains why “Shadow AI” has become the modern evolution of Shadow IT, and why the stakes are far higher when AI-generated code is moving directly into production systems. We explore how engineering teams are balancing innovation with accountability, why runtime controls and kill switches are becoming essential in AI-native software development, and how organizations are struggling to maintain visibility into code generated by autonomous systems. Cameron also explains why he believes many companies are unknowingly exposing intellectual property, customer trust, and compliance obligations through careless AI use. The conversation also examines how the EU AI Act and Product Liability Directive could reshape software development globally. Cameron argues that organizations deploying AI-generated code are now effectively treated as manufacturers under emerging regulations, with accountability resting firmly on businesses shipping software, not the AI vendors creating the tools. From governance gaps and auditability concerns to token economics and developer productivity metrics, this discussion explores the operational realities behind the AI hype cycle. We also discuss why faster code does not automatically mean safer software, the hidden costs of AI-generated rework, and how some organizations are already spending more time fixing AI-assisted production issues than they expected. Cameron shares practical advice for boards, CISOs, and DevOps leaders on what questions they should be asking today before AI governance problems become tomorrow’s security incidents. If your organization is experimenting with AI-assisted development, this conversation offers a valuable reality check on where the risks are emerging, how the rules are changing, and why accountability still matters in an increasingly automated world.

KPMG - Why AI ROI Depends More on Workforce Behavior Than Technology

Why are so many organizations investing millions into AI while still struggling to prove meaningful productivity gains? In this episode of AI at Work, I spoke with Rahsaan Shears, Principal and AIQ Program Lead at KPMG, about a major new study conducted alongside the McCombs School of Business at The University of Texas at Austin that analyzed 1.4 million real workplace AI interactions. What emerged from that research challenges many assumptions business leaders currently hold about AI adoption, productivity, and the future of work. One of the most surprising findings was that the most effective AI users were not necessarily the most technical employees, nor even the people using AI tools most frequently. Instead, the highest performers were what KPMG calls “sophisticated users,” employees who learned how to think with AI, challenge it, iterate with it, and use it as a reasoning partner rather than simply a faster search engine. Rahsaan explained how this distinction is forcing organizations to rethink how they measure AI success. Many businesses remain focused on surface-level adoption metrics like license counts, prompt volume, or chatbot usage. But those measurements often fail to capture whether AI is genuinely improving decision-making, productivity, creativity, or operational performance. The real challenge, according to Rahsaan, is that most organizations still lack a framework for understanding what meaningful AI-enabled work actually looks like. We also explored the growing behavioral capability gap emerging inside organizations. While some employees are rapidly learning how to integrate AI into their workflows in sophisticated ways, others remain stuck using these tools for basic task acceleration. Rahsaan shared why this gap has less to do with age or technical skill and far more to do with curiosity, ambition, critical thinking, and an employee’s willingness to rethink how work itself gets done. One of the strongest themes throughout our conversation was the idea that AI should not be treated as a technology rollout alone. Rahsaan argued that organizations succeeding with AI are redesigning culture, workflows, decision-making structures, and team dynamics at the same time they deploy new tools. He compared today’s AI systems to toddlers: incredibly capable compared to where they started, but still requiring guardrails, coaching, supervision, and careful integration into everyday work. For listeners interested in organizational transformation, this episode offers practical insight into how KPMG is building AI-first behaviors through peer-led champion networks, embedded learning models, AI coaching inside the flow of work, and safe environments where employees can experiment without fear of failure. Rahsaan shared why psychological safety, curiosity, and continuous learning are rapidly becoming core business skills in the AI economy. We also discussed why organizations that fail to create agency for employees may struggle to scale AI beyond pilot programs. According to Rahsaan, many existing business processes were designed around the limitations of human workers, limitations that no longer fully apply once digital teammates and agentic workflows enter the picture. Companies willing to question long-standing assumptions about work itself are beginning to separate themselves from the rest of the market. This conversation moves beyond AI hype and focuses on the human behaviors, organizational structures, and operational changes that will ultimately determine who wins and loses in the AI economy.

LaunchLemonade Founder Cien Solon On Building The Canva For AI Agents

What happens when AI agent creation stops being the job of engineers and starts landing in the hands of the people who actually understand the business problem? In this episode of AI At Work, I sat down with Cien Solon, CEO and Founder of LaunchLemonade, to talk about why the next chapter of AI may have less to do with hype and more to do with practical problem-solving. Cien describes LaunchLemonade as the Canva for AI agents, and that immediately caught my attention because it gets to the heart of what so many businesses are looking for right now. They do not want more jargon. They want a way to build something useful, quickly, securely, and without needing a room full of developers to make it happen. What I found especially interesting in our conversation was Cien’s argument that the real barrier to AI is no longer cost or technical complexity. In her view, those obstacles have already fallen away. The bigger issue now is mindset. Too many organizations are still stuck in observation mode, watching from the sidelines, waiting for perfect tools and perfect certainty. Meanwhile, others are already building, testing, learning, and finding ways to turn AI agents into something that supports growth, fills skills gaps, and creates new revenue opportunities. We also talked about what return on investment actually looks like in the real world. That part matters because so many AI conversations still float around in theory. Cien makes the case that the people best placed to solve business problems are the ones living with them every day, not the engineers guessing from a distance. That is a powerful shift in thinking. Instead of waiting until there is budget to hire another person, businesses can now identify a gap, map out the workflow, and create an AI agent to help close it. There is also a bigger human story running through this episode. Cien shared examples of people who started out experimenting with prompts and basic no-code tools, then went on to build consulting businesses, launch products, sell courses, and reposition themselves in the market. One story that stood out was a university professor who used LaunchLemonade to learn, experiment, and eventually step into entrepreneurship full time. It is the kind of example that reminds us this technology is not only changing workflows, it is also changing careers and confidence. We also discuss the future of the no-code agent economy and where businesses need to focus next. Cien breaks people into a few camps, the observers, the operators, and the builders, and it makes for a memorable way of thinking about where each of us stands right now. Her message is clear. If you are still only watching, you risk falling behind. If you are building, the next challenge is no longer whether you can create something, but whether you can market it, sell it, and make it meaningful. By the end of this conversation, what stayed with me most was how accessible this all feels when someone explains it in plain English. This is not a conversation about futuristic abstractions. It is about people using AI to solve real business problems today, in ways that feel achievable rather than intimidating. So after listening, where do you see yourself in this new AI economy, observing, operating, or building, and what are you creating next?

Building The Workforce of Tomorrow With AI Co-Workers

What happens when software stops being something we use and starts becoming something that works alongside us? In this episode of AI at Work, I sat down with Mark Skelton, CTO at Node4, to explore how AI is moving beyond content generation and into something far more transformative. Mark has spent more than a decade operating at CTO level, helping organizations navigate shifts from traditional infrastructure to cloud, and now into a world shaped by AI agents, automation, and entirely new ways of delivering technology. We begin by unpacking the evolution from generative AI to agentic AI. While most businesses are now familiar with tools that create content, Mark explains that the real shift is happening as AI begins to take action. These agents can interact with systems, execute workflows, and handle tasks that previously required human input. It is a shift that brings both excitement and uncertainty, especially as conversations around AI co-workers become more common in boardrooms and across teams. A big part of our conversation focuses on what this actually looks like in practice. Rather than replacing people, Mark shares how AI is currently augmenting teams, supporting developers, automating repetitive work, and helping organizations move faster while still keeping humans firmly in the loop. There are still limitations, from hallucinations to data quality issues, which means oversight, validation, and strong governance remain essential. We also explore one of Mark’s boldest predictions, that the rise of agentic AI could fundamentally change how we think about software itself. Instead of logging into multiple SaaS platforms, future workflows may be driven through conversations with AI agents that access systems, retrieve data, and execute tasks on our behalf. That shift opens the door to new opportunities in orchestration, integration, and data strategy, while also raising important questions about how businesses prepare for what comes next. From the role of model context protocol servers as the connective layer behind AI agents, to the importance of guardrails across technical, operational, and cultural levels, this episode offers a clear and practical look at how organizations can start making sense of a fast-moving space. Mark also shares why data readiness, cloud adoption, and AI literacy are becoming the foundations that will separate those who adapt from those who struggle to keep up. So as AI agents begin to reshape how work gets done, where should businesses focus their energy today, and what does it really take to stay relevant in a world where software may no longer look the way it does now?

AI At Work: Dave West On Scrum, AI, And Better Stakeholder Collaboration

How do you keep product teams aligned when AI is speeding everything up, but people, priorities, and expectations are still pulling in different directions? In this episode of AI At Work, I sat down with Dave West, CEO of Scrum.org [http://Scrum.org], to talk about one of the most overlooked challenges in modern product development: stakeholder collaboration. While so much of the conversation around AI focuses on faster delivery, automation, and productivity, Dave makes the case that the real pressure point is still human. As teams ship more, communicate faster, and rely on AI to remove friction, weak stakeholder relationships become even harder to ignore. We unpack why Scrum.org [http://Scrum.org] has launched its new self-paced course, Effective Stakeholder Collaboration for Scrum Teams, and why Dave believes this topic deserves far more attention than it usually gets. He explains how AI is exposing old cracks inside organizations, from fuzzy expectations and unspoken assumptions to inconsistent communication and poor decision-making. We also talk about why product teams need a more disciplined approach to stakeholder engagement, one that is clear, intentional, and built around trust rather than vague alignment. What I found especially interesting in this conversation was Dave’s view that this is less about job titles and more about how real people work together. We discussed how product owners, Scrum Masters, and developers can build stronger relationships without creating confusion, why empathy and better listening can change the direction of a product, and how segmenting stakeholders by needs, motivations, and context can reduce what Dave describes as stakeholder drag. It is a practical conversation for anyone working in product, Agile, Scrum, or AI-driven delivery. We also went beyond the course itself and into the wider debate about whether Agile and Scrum still matter in the age of AI. Dave had a lot to say on that, and he did not hold back. His argument is simple: AI may help teams build faster, but it also makes it painfully obvious when they are building the wrong thing. If you care about AI at work, Scrum, product management, stakeholder engagement, or the future of Agile, this episode has plenty to think about. Do you believe AI will strengthen stakeholder collaboration or expose just how broken it already is, and what side of that debate are you on? Share your thoughts.