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.

What if the food we eat every day is silently undermining our health, and AI holds the key to reversing it?

In this episode of AI at Work, I sit down with Jonathan Wolf, co-founder and CEO of Zoe, to explore the intersection of AI, microbiome science, and the future of personalized nutrition. If Zoe sounds familiar, it’s likely because of their groundbreaking COVID study app or their clinical trial published in Nature Medicine proving Zoe’s approach is more effective than standard dietary advice. But this isn’t just about test kits or health trends.

Jonathan shares the origin story behind Zoe, including how a chance meeting with Professor Tim Spector turned a pivot from adtech into a mission-led company focused on improving the health of millions. We explore:

• How AI is powering Zoe’s free new app launching in the US
• The dangers of ultra-processed food and what’s really inside your meals
• Why personalized advice and behavior change, not food tracking or perfection, are key to long-term health
• What shotgun metagenomics can tell you about your gut and why that matters
• The ethical challenge of combating food industry misinformation at scale

From photo-based food recognition to conversational AI that understands your microbiome, Jonathan breaks down how science, data, and product design are working together to make health advice smarter and more accessible.

Whether you're a founder thinking about your next pivot or someone just trying to eat better without obsessing over every bite, this conversation offers real insight and practical steps.

What if your tools could finally talk to each other and reduce meetings, manual tasks, and copy-paste chaos in the process?

In this episode of AI at Work, I sit down with Sanchan Saxena, Head of Product for Work Management at Atlassian, to unpack the thinking behind their new Teamwork Collection. Recorded live at Team 25 in Anaheim, this conversation explores how Atlassian is bringing together Jira, Confluence, Loom, and AI-powered agents into a single, streamlined experience.

Sanchan shares how his team is designing tools that not only integrate more deeply but also help companies work more effectively. We discuss how AI is now summarizing meetings, creating Jira tickets from Loom videos, and pulling historical campaign data directly into brainstorming sessions in a way that fits how teams actually work.

We explore:

• How the Teamwork Collection helps overwhelmed teams cut through digital noise
• Real-world use cases from companies like Rivian saving hundreds of hours a year
• Why context switching kills productivity and what a unified experience can solve
• The growing role of agentic AI in supporting, not replacing, teams
• How Atlassian is helping customers overcome change fatigue and adopt new workflows
• Why AI is no longer a luxury but a critical enabler of business velocity

Whether you're leading digital transformation or just trying to tame your team’s growing tool stack, this episode offers clear insights into where collaboration is heading and why simplicity, clarity, and connectedness are the new competitive edge.

Explore the Teamwork Collection at atlassian.com/collections/teamwork

Ask ChatGPT

In this episode of AI at Work, I sit down with Mike Mason, Chief AI Officer at Thoughtworks, to explore what happens when the generative AI hype starts to settle and businesses begin asking the real questions. What’s working, what’s not, and what does mature adoption actually look like in 2025?

Mike brings a practical, deeply informed view of the AI landscape. We talk about how intelligent agents are evolving well beyond basic chatbots and starting to act as collaborative teammates inside real workflows. From customer support to software development, these agents are now reasoning, adapting, and in some cases, working alongside other agents to get things done.

We also explore the growing shift toward open source AI. Mike explains why some companies, especially in regulated sectors like financial services, are leaning into in-house or fine-tuned small models for better control, data security, and flexibility. We unpack what’s driving the rise of small language models and why in many cases, smaller, more nimble models are outperforming their larger counterparts in speed, privacy, and efficiency.

One of the most thought-provoking parts of our chat was about the diverging paths organizations are taking with GenAI. Mike shares insights from Thoughtworks’ upcoming global survey, which shows that while some are embedding bias detection and strong governance into their strategies, others are focused purely on quick wins and interpretability. That divide is shaping not just how projects are executed but how companies are thinking about long-term AI maturity.

If you're navigating the tension between speed and safety or trying to decide whether to build, fine-tune, or adopt off-the-shelf models, this conversation offers real perspective. We cover explainability, regulation, open ecosystems, and what tech leaders should be planning for next as AI becomes part of everyday business.

This isn’t about future hype. It’s about how AI is actually getting to work.

In this episode of AI at Work, I’m joined by Simon Ranyard, Managing Director for Northwest Europe at Orange Business, to challenge old assumptions about manufacturing and reveal how technology is rewriting the rules.

We discuss how AI, automation, augmented reality and 5G are giving manufacturers the tools to boost productivity, reduce downtime and create high-value careers instead of cutting jobs. Simon shares practical insights on where the UK stands, how to close the skills gap, and why apprenticeships and reskilling are more important than ever.

If you think factories are all grease and gears, this conversation will make you think again. Take a closer look at how Orange Business is helping manufacturers adapt and thrive, and what this means for workers, companies and the wider economy.

In this episode of AI at Work, I sit down with Dennis Woodside, CEO of Freshworks, to uncover how real companies are getting true value from AI.

Dennis shares how Freshworks has built AI tools that help businesses resolve routine questions automatically, boost agent productivity, and give managers clear performance insights without needing complex dashboards. He explains the company’s focus on making AI quick to deploy and simple to buy, so mid-sized companies can see immediate returns without endless consulting bills.

We explore customer stories like Total Expert, which saved thousands of agent hours and saw a 250 percent return on its AI investment. Dennis also talks about the lessons learned from integrating AI internally and how the company stays flexible enough to adopt the latest advances from across the industry.

This conversation is for anyone who wants to see beyond the AI hype and hear how smart companies are using it to save time, cut costs, and let people focus on more rewarding work.