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?