As AI moves beyond hype and into everyday operations, many organizations are asking harder questions about impact, trust, and return on investment. Three years on from ChatGPT’s breakout moment, leaders are no longer experimenting for novelty’s sake. They want to know where AI genuinely improves outcomes for employees and customers, and where it risks getting in the way.

In this episode of the AI at Work Podcast, I sit down with John Finch, Head of Product Marketing at RingCentral, to unpack how AI is changing customer interactions before, during, and after the call. We explore how tools like AI receptionists and real time agent assistance are helping businesses avoid missed calls, reduce friction, and support frontline teams without turning conversations into scripted or robotic exchanges.

John shares RingCentral’s perspective on why voice remains one of the richest and most strategic data sources inside modern organizations. We discuss how insights drawn from real conversations are shaping smarter routing, coaching, and workforce planning, and why sectors like healthcare and financial services are leaning into AI faster than others. At the same time, we address the common mistakes companies make when they bolt AI onto fragmented systems rather than embedding it into a unified platform.

Looking ahead to 2026, this conversation also reflects on what AI done well really looks like in the workplace. Not as a replacement for people, but as a way to remove pressure, improve performance, and create better experiences for everyone involved. As AI becomes more natural, conversational, and embedded into daily workflows, the line between digital and human support continues to blur.

So as AI becomes part of the fabric of customer operations, how are you balancing automation with empathy, and what lessons from your own organization would you share with others navigating this shift?

What happens when a field races forward faster than society can understand it, let alone shape it? And how do we balance the promise of superintelligence with the responsibility to ensure it reflects the values of the people it will eventually serve? In this episode of AI at Work, I sit down with Dr Craig Kaplan, a pioneer who has been building intelligent systems since the 1980s and one of the few voices urging a deliberate and safer path toward AGI. Craig brings decades of perspective to a debate often dominated by short-term thinking, sharing why speed without design can become a trap and why the next breakthroughs must be grounded in intention rather than chance.

Throughout our conversation, Craig explains why current alignment methods often rely on narrow viewpoints, which creates both ethical and technical blind spots. He shares his belief that the values guiding future intelligence should come from millions of people across cultures rather than a handful of researchers writing a constitution behind closed doors. Drawing on his work at Predict Wall Street, he illustrates how collective intelligence can outperform experts, why diverse viewpoints matter, and how these lessons shape the architecture he believes is needed for safe AGI and the superintelligent systems that follow. His clarity on the difference between tools and entities, and how quickly AI is shifting into the latter category, offers a grounding moment for anyone trying to navigate what comes next.

This episode moves beyond fear and hype. Craig talks openly about risk, but he also brings optimism about the potential for systems that are safer, faster to build, less costly, and more reflective of humanity. For leaders wondering how to prepare their organisations, he shares what signals to watch, why transparency and design matter, and how a more democratic approach to intelligence could shift the odds of a better outcome. If you want a clear, thoughtful look at the road ahead for AGI, superintelligence, and the role humans still play in shaping both, you will find a lot to chew on here.

Listeners wanting to learn more can explore superintelligence.com, where Craig and the iQ Company team share research, videos, papers, and ways to get involved. What part of this conversation sparks your own questions about the future we are building together?

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I sit down with Toby Hough, Vice President of People and Culture at HiBob, for a grounded and human conversation about how AI is reshaping the world of work, not by replacing people but by amplifying them. As an HR leader inside a company that builds HR technology, Toby brings a rare perspective on what it really means to balance efficiency with empathy in an AI-driven workplace.

We talk about the fear that still surrounds AI in many organisations and how leaders can help shift that mindset from anxiety to opportunity. Toby explains why HiBob is taking a “more with more” approach, using AI tools to empower employees rather than reduce headcount. From custom-built AI coaches that guide managers through feedback conversations to an internal platform with dozens of homegrown AI tools, he shares how democratising AI access can transform both productivity and trust.

Toby also explores how leaders can measure success in this new era, moving beyond cost savings to focus on adoption, engagement, and well-being. He highlights the delicate balance between automation and human connection, showing how HiBob invests equally in AI enablement and in-person leadership development. As we look ahead, Toby reflects on the evolving skills required to lead both humans and AI agents, and how the next generation of leaders will need to master curiosity, adaptability, and collaboration across both worlds.

Listen in for an honest discussion about the cultural, emotional, and practical realities of integrating AI at work, and why,

• Toby’s LinkedIn
• HiBob website
• In Good Company website

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The arrival of generative AI has sparked an uncomfortable question for many young professionals: What happens to entry-level jobs when machines can now write, analyze, and even converse as well as humans? In this episode of the AI at Work Podcast, I reconnect with Anshuman Singh, CEO of HGS UK, to discuss how automation and artificial intelligence are reshaping the early stages of a career, and what skills will define employability in the years ahead.

Anshuman brings a rare blend of optimism and realism to the debate. He traces how AI’s evolution from statistical tools to generative systems has amplified both opportunities and anxieties, particularly among graduates seeking their first big break. Drawing on research from MIT, ADP, and the World Economic Forum, he explains how AI is accelerating job displacement in certain functions, such as data entry and basic customer service, even as it creates entirely new roles in areas like AI training, ethics, and human-in-the-loop supervision.

We explore why adaptability, not fear, is the true competitive advantage in this era of rapid change. Anshuman breaks down three categories of emerging roles: AI specialist positions such as prompt engineers, collaborative roles that blend human creativity with machine intelligence, and augmented roles where humans use AI to enhance judgment and performance. He also warns that if companies automate entry-level work too quickly, they risk losing the apprenticeships and on-the-job learning that build leadership pipelines.

Our conversation turns to the human qualities that machines still cannot replicate, such as empathy, ethical reasoning, creative problem solving, and contextual understanding, and why these traits will define long-term success. Anshuman offers practical advice for workers and business leaders alike: redesign roles to keep humans in the loop, measure success by both human impact and automation, and invest relentlessly in learning cultures that help people evolve alongside technology.

If you are worried about AI replacing your job, this episode reframes the story. It is not about competing with machines; it is about understanding what only humans can do and leveraging that as your edge.

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In this episode of AI at Work, I sit down with Tom Totenberg, Head of Release Automation and Observability at LaunchDarkly, to explore what happens when artificial intelligence starts writing and shipping our software faster than humans can think. Tom brings a rare blend of technical insight and grounded realism to one of the most important conversations in modern software development: how to balance speed, safety, and responsibility in an AI-driven world.

We discuss the hidden risks of AI-fuelled shortcuts in software delivery and why over-reliance on AI-generated code can create dangerous blind spots. Tom explains how observability and real-time monitoring are becoming essential to maintaining trust and stability as teams adopt AI across the full development lifecycle. Drawing on LaunchDarkly’s recent investments into observability, he breaks down how guarded releases and real-time metrics are helping teams catch problems before users ever notice.

From the dangers of “vibe coding” to the rise of agentic AI in software pipelines, Tom shares why AI should be seen as an amplifier rather than a magic fix. He also offers practical advice for leaders trying to balance innovation with caution, reminding us that the goal is to innovate with intention — to measure what matters and build resilience through feedback and transparency.

Recorded during his time in New York, this episode captures both the human and technical sides of what it means to deliver software in an era where the line between automation and accountability is being redrawn.

I invited Kyle Hauptfleisch, Chief Growth Officer at Daemon, to strip the buzzwords out of AI and talk plainly about what moves the needle at work. The conversation began with an honest look at why so many pilots stall. It ended with a calm, workable path for leaders who want results they can measure rather than demos that gather dust. Along the way we compared two very different mindsets for adoption, AI added and AI first, and what that means for teams, accountability, and the way work actually gets done.

Here’s the thing. Plenty of organisations raced into proofs of concept because a board memo said they had to. Kyle has seen that pattern play out for years, and he argues for a simpler starting point. You do not need an AI strategy in a vacuum. You need a business strategy that names real constraints and outcomes, then you pick the right kind of AI to serve that plan.

AI Added vs AI First

This distinction matters. AI added means dropping tools into the current way of working. Think code generation that saves hours on day one, only to lose those hours later in testing, release, or approvals. The local gain never flows through to the customer.

AI first asks a harder question. How do we change the workflow so those gains survive from whiteboard to production? That can mean new handoffs, fresh definitions of ownership, and different review gates. It is less about tools, more about the shape of the system they live in.

Accountability sits at the center. Kyle raised a scenario where a lead might one day direct fifty software agents. The intent behind those agents remains human. So does the responsibility. Until structures reflect that, companies will cap the value they can safely realise.

From Pilots to Production

Kyle offered a simple mental model that avoids endless experimentation. Picture a Venn diagram with three circles. First, a real constraint that people feel every week. Second, usefulness, meaning AI can change the outcome in a measurable way. Third, compartmentalisation, so the work sits far enough from core risk to move fast through governance. Where those circles overlap, you have a candidate to run live.

He shared a small but telling example from Daemon. Engineers dislike writing case studies after long projects. The team now records a short conversation, transcribes it with Gemini inside a safe, private setup, and drafts the case study from that transcript. People still edit, but the heavy lift is gone. It saves time, produces more human stories, and proves a pattern the business can repeat.

Leaders can start there. Pick a contained problem, run it in production, measure the outcome, and tell the truth about the bumps. That story buys trust for the next step, which is how you scale without inflating the promise.

Humans, Accountability, and Culture

We talked about the fear that AI erases the human role. Kyle’s view is steady. Models process data. People set intent, judge context, and carry the can when decisions matter. Agents will take on more tasks. The duty to decide will remain with us.

Upskilling then becomes less about turning everyone into a prompt whisperer forever and more about teaching teams to think with these tools. Inputs improve, outputs improve. Middle managers, in particular, gain new leverage for research, planning, and option testing. The job shifts toward framing better questions and challenging the first answer that comes back.

In this episode I sit down with Mo Cherif, Vice President of AI Innovation at Sitecore, to explore one of the biggest shifts in business today: the rise of agentic AI. Unlike traditional AI models that focus on narrow tasks, agentic AI brings autonomy, reasoning, and collaboration between specialized agents. It is changing the conversation from automation to transformation.

Mo explains how agentic AI is reshaping marketing, customer engagement, and creativity. From hyper-personalized chat-driven discovery to removing repetitive project management tasks, we look at how AI can free marketers to focus on strategy, storytelling, and innovation. He also shares why success depends on three foundations: context, mindset, and governance.

We dig into Sitecore’s three pillars of brand-aware AI, co-pilots, and agentic orchestration, and how the company’s AI Innovation Lab, launched with Microsoft, helps brands experiment, co-innovate, and apply these ideas in practice. Mo also reflects on lessons from real projects such as Nestlé’s brand assistant and looks ahead to a future where personal AI agents interact directly with others on our behalf.

If you want to understand how agentic AI is moving from hype to real business impact, this episode will give you practical insight into what is already happening and what comes next.

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When access to advanced AI models is no longer the big differentiator, the real advantage comes from how effectively a business can connect those models to its own unique data. That was the central theme of my conversation with Rahul Pathak, Vice President of Data and AI Go-to-Market at AWS, recorded live at the AWS Summit in London.

In a bustling booth on the show floor, Rahul explained how AWS is helping organisations move from AI pilots to production at scale. We discussed the layers of infrastructure AWS provides, from custom silicon like Trainium and Inferentia to services such as SageMaker, Bedrock, and Q Developer, and how these combine to give enterprises the flexibility and performance they need to build impactful AI applications.

Rahul shared examples from BT Group, SAP, and Lonely Planet, each showing how the right blend of tools, data, and strategy can lead to measurable business results. Whether it is accelerating code generation, generating custom travel guides in seconds, or using generative AI to produce personalised content, the common thread is a focus on business outcomes rather than technology for its own sake.

A key point in our discussion was that most companies do not have their data ready to power AI effectively. Rahul broke down how AWS is helping unify siloed data and make it available to intelligent applications, turning a company’s proprietary knowledge into a competitive edge. We also touched on responsible AI, sustainability, and the operational challenges that come with scaling AI, from cost efficiency to security and trust.

For leaders still weighing up whether to invest in generative AI, Rahul’s message was clear: waiting too long could mean being left behind. This episode is a practical guide to what it takes to deploy AI with purpose and how to ensure it delivers lasting value in a fast-changing market.