AI at Work

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?

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, 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 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.

What does AI actually change once you move beyond the pilot phase and into the messy reality of live deployment? In this episode of AI at Work, I sit down with Jack Siney, CRO and co-founder of FrontRace, to separate operational truth from industry hype and explore what he calls the “Great Upheaval” already reshaping how organizations generate revenue, measure performance, and define success.
Drawing on experience from the U.S. Navy’s Blue Angels program, PwC, multiple startup exits, and now hands-on AI implementation across hundreds of companies, Jack offers a practitioner’s perspective on where AI is delivering immediate value and where it is still falling short. We talk about why so many expensive initiatives fail to move the needle, how legacy KPIs are pushing teams toward the wrong outcomes, and why most automation breaks because organizations never fully documented the human steps they were trying to replicate.
A big part of our conversation focuses on sales leadership and the frontline reality. Jack explains how AI can finally decode the long-standing mystery of why two reps with identical activity metrics produce wildly different results, how decision engines built on a company’s own historical data can guide next best actions in real time, and why better data hygiene and process clarity matter more than buying another tool. At the same time, he is clear that today’s AI is an 80 percent solution that still demands human oversight, critical thinking, and constant tuning.
We also step back to look at the economic and cultural shift ahead. If productivity is no longer tied to headcount growth, what happens to the traditional link between company performance, employment, and spending power? And what mindset shifts do chief revenue officers and business leaders need to make right now to avoid incremental thinking and instead redesign how work gets done?
This is a grounded, candid conversation about readiness, responsibility, and real outcomes, recorded for leaders who want practical direction rather than another theory about the future of work. After listening, where do you see AI genuinely improving performance in your organization today, and where is it still a promise waiting to be fulfilled?

What if the best way to improve your negotiation skills was to rehearse the conversation before it ever happened?
In this episode of AI at Work, I sit down with Professor Alexandra Mislin from American University’s Kogod School of Business to explore how AI is quietly reshaping the way professionals prepare for high-stakes conversations. Recently featured in Fortune, Professor Mislin has been teaching her students to use AI as a negotiation practice partner, helping them clarify priorities, test assumptions, and even role-play difficult scenarios before walking into the room.
Negotiation is one of those skills we use every day, whether we label it that way or not. It shows up in salary discussions, scope changes, vendor renewals, internal disagreements, and those tense moments where trust feels fragile. The problem is that most people learn under pressure, with real consequences and little room to experiment. Professor Mislin’s approach offers something different. She teaches core negotiation skills first, then introduces AI as a thinking partner rather than a decision maker. The goal is not to outsource judgment, but to sharpen it.
We talk about how AI can help professionals clarify what they truly want before a conversation begins. We explore how tools can surface blind spots, generate counterarguments, and simulate different negotiation styles. Professor Mislin also shares why she is less worried about AI creating formulaic responses or overconfidence than many critics assume. In her view, reducing ambiguity can actually empower more people to advocate for themselves and engage in everyday negotiations they might otherwise avoid.
Trust, emotion, and identity remain at the heart of every negotiation. That human element does not disappear. Professor Mislin explains how AI can help diagnose a breakdown in trust or draft the structure of an apology, but sincerity still requires real human presence. As AI automates more routine exchanges, the competitive advantage will belong to those who know how to combine analytical tools with interpersonal intelligence.
We also look ahead to what negotiation education may become in an AI-rich workplace. Instead of occasional training sessions, professionals could have continuous, on-demand coaching. Yet the skills that remain uniquely human, listening deeply, regulating emotions, and making difficult calls under uncertainty, may become even more valuable.
If you have ever walked away from a difficult conversation thinking of everything you wish you had said, this episode offers a practical way to prepare differently. How are you using AI to think before you ask, and what changed when you did?

How do you move AI from a flashy demo on a conference stage to something that can handle real customer pressure on a Monday morning when the tickets are piling up?
In this episode of AI At Work, I sit down with Niraj Ranjan Rout, Founder and CEO of Hiver, to unpack what it really takes to build AI that works inside high-volume support environments. With more than 10,000 teams using Hiver, including brands like Flexport, Capital One, and Epic Games, Niraj has had a front-row seat to both the promise and the pitfalls of AI in customer service.
We talk about the difference between “slapping a chatbot” onto an existing problem and rethinking the entire support workflow. Niraj makes a compelling case that AI should function as infrastructure, embedded across triage, routing, drafting, summarization, quality assurance, and insights. Rather than replacing agents, the goal is to remove the repetitive, manual work that drains time and energy, so humans can focus on solving real problems and understanding how customers actually feel.
Our conversation also gets into the uncomfortable but necessary topics many leaders underestimate. Data hygiene. Governance. The reality that 98 percent accuracy is sometimes still not good enough. Niraj shares why clear handoff protocols between humans and AI are essential, and how organizations can avoid measuring ROI through surface metrics like deflection rates alone. Instead, we explore more nuanced signals, from sentiment shifts to long-term customer outcomes and team productivity.
We also discuss Hiver’s own journey from an email collaboration tool to an AI-native customer service platform. Niraj is candid about the noise in the market, from overblown promises to doomsday narratives, and how founders must stay close to customers while remaining hands-on with emerging models and agentic capabilities. Culture, he argues, is as important as code. Customer stories need to flow directly into product and engineering teams if AI investments are going to remain grounded in reality.
And yes, we even end on a musical note, with a nod to Jimi Hendrix and a reminder that creativity, whether in music or software, still comes down to craft and feel.
So here’s the question I’ll leave you with. As AI becomes embedded into every workflow, are you treating it as a shiny add-on, or are you redesigning your foundations so it can truly perform under pressure?