AI at Work

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?

What does AI at work really look like once the hype fades and the day-to-day reality sets in?
In this episode of AI at Work, I’m joined by Nicole Leib, Regional Vice President of People for the Americas and Global Head of Inclusion at monday.com, for a grounded, refreshingly honest conversation about how AI is actually being used in modern organizations. We recorded this during CES week, when every headline seemed to promise disruption, reinvention, and job loss. Yet the data Nicole brings to the table tells a very different story.
Drawing on Monday.com’s World of Work: AI Edition report, produced in partnership with Nielsen and informed by millions of real workflows, Nicole explains why labor reduction is not the primary driver behind AI adoption. Instead, organizations are using AI to move faster, improve accuracy, and reduce the cognitive load placed on teams. This marks a clear shift into what she calls the operational era of AI, where success is measured by practical outcomes rather than futuristic promises. We unpack why the tools gaining traction are not the flashiest, but the ones that fit naturally into existing workflows and simply help people get through their day.
We also explore the human side of AI adoption. Nicole shares insights into why innovation is barely a motivator right now, what tool overload looks like in practice, and why simplification is becoming a real competitive advantage. Our conversation touches on trust, security, and governance, especially for larger enterprises, and why embedding AI into systems people already rely on matters more than adding yet another standalone tool. We also address the confidence gap around AI, including the striking gender divide where women are often using AI more while undervaluing their own expertise, and what that means for career progression.
By the end of the discussion, one idea stands out above all others. AI is not pushing people out of work. It is helping them step up, take on more strategic responsibilities, and rethink what valuable work looks like in a world of constant change.

As we look ahead to what the next phase of AI at work might bring, are our leaders ready to stop waiting for a perfect future moment and start treating AI as a core operating capability today, and how are you seeing that shift play out inside your own organization?

Useful Inks
Connect With Nicole Leib,
Introducing AI at work: From vision to value, Monday Research’s latest report
Follow Monday on LinkedIn

Thanks to our sponsors, Alcor, for supporting the show.

What if the most important jobs of the next decade already exist, but we just have not named them yet?
In this episode of AI at Work, I sit down with Marinela Profi from SAS to unpack how artificial intelligence is reshaping work at a deeper level than most headlines suggest. We are not just talking about tools, automation, or faster workflows. We are talking about new roles, new decision structures, and a fundamental shift in how humans and machines collaborate inside modern organizations.
Marinela brings a grounded, enterprise-tested perspective to agentic AI, cutting through the confusion that still surrounds the term. She explains why large language models are not agents, why autonomy is often misunderstood, and why most successful AI systems will always keep humans in the loop. We explore how agentic systems differ from traditional AI, how deterministic guardrails and probabilistic models must work together, and why governance needs to be designed into systems from day one rather than bolted on later.
One of the most compelling parts of this conversation is our discussion on future roles. A few years ago, no one imagined titles like cloud governance architect. Marinela explains why roles such as AI decision designers and AI experience designers are likely to follow a similar path. These are not abstract ideas. They are practical responses to real challenges organizations face as AI systems begin to act, decide, and operate at scale.
We also dig into where teams tend to go wrong. Too many organizations rush from pilots to hype without addressing data readiness, orchestration, or accountability. Marinela shares real examples from regulated industries, including banking, insurance, telecoms, and manufacturing, where agentic AI has moved from experimentation into production by focusing on decision workflows rather than flashy prototypes.
This is a conversation for CIOs, CDOs, business leaders, and professionals who want to understand what AI means for work beyond surface-level narratives. It is also for students and early-career listeners who want to prepare for roles that are still taking shape, but will soon be unavoidable.
If AI is becoming an expected skill rather than a specialist one, how do you prepare yourself and your organization for work that is already changing in front of us?
I would love to hear your thoughts after listening. Where do you see human judgment becoming more important as AI systems grow more capable, and which future roles do you think we will be talking about next year?

Useful Links
Connect with Marinela Profi
SAS Website
Follow SAS on LinkedIn

Thanks to our sponsors, Alcor, for supporting the show.

What happens when eighty percent of the global workforce receives less than one percent of technology investment, and why has this imbalance gone largely unchallenged for so long?
In this episode, I sat down with Emma Seymour, Chief Financial Officer of Deputy, to unpack the realities facing the world’s so-called invisible workforce. Deskless workers power healthcare, retail, hospitality, and frontline services, yet the tools built to support them have historically lagged far behind those designed for office-based teams. Emma brings a grounded, finance-led perspective on why this gap exists and why it is finally starting to close.
We explored how AI-driven workforce management is moving beyond hype and into practical, measurable outcomes. From optimizing staffing levels to reduce overstaffing and burnout, to giving workers more control over their schedules through self-service tools, Emma shared how Deputy is translating technology investment into real operational and human impact. We also discussed how AI is reshaping the finance function itself, automating admin-heavy tasks and freeing up teams' time to focus on higher-value work.
What also stood out in this conversation was leadership. Deputy’s predominantly female executive team offers a rare example of scaling a billion-dollar technology company while balancing high performance with high care. Emma shared how trust, accountability, and empathy shape decision-making inside the business, and why that culture matters just as much as product innovation when serving a workforce that has been overlooked for decades.
As AI continues to accelerate and workforce pressures intensify, what would it look like if more technology companies truly built for the people who keep the global economy running, and how differently might work feel if the invisible workforce finally became visible?
Useful Links
Connect with Emma Seymour
Learn more about Deputy,
Follow on LinkedIn
Thanks to our sponsors, Alcor, for supporting the show.