AI at Work

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 FirstThis 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 ProductionKyle 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 CultureWe 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.*********Visit the Sponsor of Tech Talks Network:Land your first job in tech in 6 months as a Software QA Engineering Bootcamp with Careeristhttps://crst.co/OGCLA

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 USThe dangers of ultra-processed food and what’s really inside your mealsWhy personalized advice and behavior change, not food tracking or perfection, are key to long-term healthWhat shotgun metagenomics can tell you about your gut and why that mattersThe ethical challenge of combating food industry misinformation at scaleFrom 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 noiseReal-world use cases from companies like Rivian saving hundreds of hours a yearWhy context switching kills productivity and what a unified experience can solveThe growing role of agentic AI in supporting, not replacing, teamsHow Atlassian is helping customers overcome change fatigue and adopt new workflowsWhy AI is no longer a luxury but a critical enabler of business velocityWhether 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/teamworkAsk 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.

In this episode of AI at Work, I catch up with Amanda Brock, CEO of OpenUK, for a wide-ranging conversation on the changing landscape of open technology, AI transparency, and international collaboration.We explore how OpenUK is working ahead of the market, helping shape policies and support for open source projects while responding to rising geopolitical tensions and funding pressures. Amanda explains how the UK occupies a unique position between the EU and the US and what that means for future AI standards and regulatory frameworks.We also discuss:The sustainability challenges facing open source communities and maintainersShifts in AI development, including legal and ethical questions around IP and model transparencyThe role of tools like Roost and initiatives like Current AI in creating practical solutions for AI governanceWhy "tools, not rules" may offer a more realistic path than top-down regulationThe importance of keeping open source accessible as a route into the tech industryAmanda shares her concerns about the rollback of EDI efforts and highlights how open communities can still offer a clear path into tech for people from underrepresented and underserved backgrounds. We discuss OpenUK's upcoming skills report and how it aims to highlight open source as a solution to address the ongoing talent shortage.Recorded ahead of International Women's Day, this episode also reflects on the slow progress around diversity and how leadership, policy, and community must come together to drive lasting change.If you're interested in how policy, law, and open technology intersect with AI development, this conversation offers thoughtful perspective, clear examples, and real-world action.🎧 Listen now and let us know where you think the future of open innovation is headed.

In this episode of AI at Work, I sit down with Juan Orlandini, CTO North America at Insight, to unpack the often-overlooked side of AI adoption: regulation, data strategy, and governance. While much of the recent conversation around AI has focused on speed, productivity, and experimentation, Juan brings the discussion back to fundamentals. Before you scale that shiny new AI tool across your business, have you classified your data? Have you considered your compliance obligations? And do you understand the different responsibilities that come with being an AI creator, adapter, or consumer?Juan walks us through Insight’s perspective on the current state of enterprise AI, including how they’ve used their own internal tools like InsightGPT to stress test both opportunities and risks. We discuss why internal use cases are often the best place to start, and how leaders can avoid repeating the mistakes of past tech waves, like the race to cloud or mobile apps without a clear strategy.We also explore the patchwork of US regulations, with California leading the way, and compare this to the EU’s more prescriptive approach. Juan explains how these emerging policies are shaping real business decisions right now, and what business leaders can do to stay ahead. Throughout our chat, his advice is grounded and practical, offering a steady counterpoint to the noise and hype.Whether deep in deployment or just starting to explore how AI fits into your business, Juan's insights offer a roadmap to thinking bigger while avoiding costly missteps. How do you keep your organization agile enough to adapt, but stable enough to deliver? And what does it really take to treat AI as an enterprise tool rather than a passing trend?Tune in to hear Juan’s advice on managing risk, reimagining processes, and building a culture that is ready for what comes next.

What if AI could help us discover new medicines faster, more accurately, and with greater impact for patients?In this episode of AI at Work, I speak with Dr. Chris Austin, Head of Research Technologies at GSK, to explore how artificial intelligence is changing the way new treatments are developed. Chris brings a unique perspective shaped by decades of experience across academia, biotech, government, and now big pharma. His mission at GSK is clear: to bring science, technology, and talent together to radically improve human health.We unpack how AI, combined with massive clinical and genetic datasets, is enabling GSK to target disease with unprecedented precision. From identifying the right molecular pathways to simulating clinical trials using digital twins, Chris walks us through how technology is helping reduce development timelines and increase the chances of success. He shares powerful examples including a promising asthma treatment that moved from first-in-human testing to Phase 3 trials across four diseases in record time.We also explore how GSK uses AI to improve patient selection in clinical trials, design oligonucleotide-based therapies for hard-to-treat conditions like hepatitis B, and incorporate generative AI into everything from drug design to safety prediction. According to Chris, the key isn't just having better algorithms. It's about generating the right data, at scale, to make those algorithms meaningful.If you're curious about how AI is being applied to some of the most complex problems in healthcare, this episode offers a rare inside look. Chris also reflects on his journey from medicine to data science, and why this is the most exciting time he’s seen in drug development.

How do you transform a century-old creative institution into a future-ready force without losing sight of its roots?In this episode of AI at Work, we spotlight Dr. Joyce Brown, President of the Fashion Institute of Technology (FIT), who has spent 26 years leading a quiet revolution in fashion education. As the first woman and first African American to hold the role, Dr. Brown has reimagined what it means to prepare students for creative careers in a digital world.She shares how, when she took the helm in 1998, FIT was operating with outdated systems and a siloed approach to education. Through strategic planning, bold hiring decisions, and a commitment to change, she reshaped FIT into a collaborative, interdisciplinary, and forward-looking institution. Under her leadership, FIT quadrupled its use of technology in teaching and launched the DTech Lab, a hands-on innovation hub where students work directly with brands like Netflix, Adidas, Girl Scouts, and Tommy Hilfiger to solve real challenges using emerging tech like AI and advanced materials.This episode also explores how FIT is fostering a new wave of sustainable design. Students are using kombucha, mycelium, and pineapple fibers to rethink fashion from the ground up, while also cultivating a natural dye garden on campus. We unpack how the school integrates innovation, science, and sustainability without losing the soul of design.Dr. Brown reflects on how FIT has responded to social and global shifts, from the pandemic to social justice movements, and how students are using creative work to make sense of the world. Her insights offer a compelling look at what education can achieve when it embraces experimentation, diverse voices, and emerging technologies.Whether you're in fashion, tech, education, or simply interested in how institutions evolve, this conversation offers a masterclass in visionary leadership and what it takes to truly modernize without losing meaning.How are you preparing your team or organization for a future shaped by creativity, technology, and purpose? Join the discussion.

What if the limitations of search engines and conventional AI tools are holding back your ability to truly understand the world? In this episode of AI at Work, we welcome Mel Morris, founder and CEO of Corpora.AI, to explore how artificial intelligence is reshaping the way we research and consume information across industries and professions.Mel, known for his pivotal role in the early success of Candy Crush creator King, has now set his sights on transforming how individuals, businesses, and institutions discover knowledge. With Corpora.AI, he has created a powerful research engine that processes two million documents per second and delivers comprehensive reports containing up to 500 cited sources per query. The platform ingests over 100 petabytes of open-source intelligence in real time, offering unparalleled speed, scale, and accuracy.During our conversation, Mel explains why traditional search methods no longer scale for human users and how Corpora.AI addresses this by using real-time data ingestion, multilingual capabilities, and dynamic content summarization. We discuss how the platform is being used by academics, journalists, legal professionals, and even medical researchers to uncover deeper insights and verify claims quickly.Mel also breaks down how the platform avoids common AI pitfalls such as outdated information, source ambiguity, and bias. Every report produced through Corpora.AI is transparent, traceable, and backed by robust citations, allowing users to make informed decisions with confidence. We also touch on the impact this could have on democratizing access to advanced research, especially in underserved regions.With the future of work demanding faster, more credible, and more comprehensive access to information, can AI-powered research engines like Corpora.AI redefine how we learn and make decisions? Tune in to hear how this technology is setting a new benchmark for speed, transparency, and trust in research.

Artificial intelligence is no longer a distant ambition, it is actively reshaping how businesses operate, innovate, and compete. But what does AI truly mean for the workplace of today and tomorrow? And as the pace of advancement accelerates, are organizations truly ready for what comes next?In this episode of AI at Work, we explore these questions with Louis Landry, newly appointed CTO of Teradata. With over two decades of experience in software architecture, engineering leadership, and technology innovation, Louis brings a grounded and insightful perspective on how businesses can harness AI responsibly and effectively.Together, we unpack some of the defining trends for 2025: the maturation of retrieval-augmented generation, the evolution of large-scale personalization, and the rise of agentic AI systems that blend generative AI with traditional software architectures. Louis explains how enterprises are moving beyond experimental AI projects to focus on outcome-driven deployments that deliver measurable business impact.Throughout our conversation, Louis stresses a recurring theme: trust. Building trusted AI, grounded in transparency, human accountability, and high-quality data, is essential for sustainable success. He shares practical strategies for managing emerging challenges such as vector data governance, navigating regulatory uncertainty, and balancing innovation with responsible risk management.We also explore the vital role of data harmonization in achieving faster, more confident decision-making, and how open-source technologies are enabling more accessible and customizable AI solutions across industries. Louis highlights why data quality, explainability, and clear business outcomes should be the North Star for any organization looking to thrive in an AI-driven future.As businesses face an increasingly complex digital environment, what strategic investments should they prioritize? How can they build AI systems that remain trustworthy, scalable, and truly transformational? And what leadership mindset is needed to unlock the next era of workplace innovation?Tune in to hear Louis Landry’s insights on the future of AI, and join the conversation: How do you see AI shaping the future of work in 2025 and beyond?

What happens when artificial intelligence moves beyond assisting individual developers and solves problems across thousands of codebases simultaneously?In this episode of AI at Work, we explore how AI is being used to tackle one of the most complex challenges in modern software development: large-scale code migrations. Justine Gehring, AI research engineer at Moderne and author of AI for Mass-Scale Code Refactoring and Analysis, joins the show to explain how she and her team are helping enterprises rethink how they approach code changes across massive environments.While many are familiar with tools like GitHub Copilot and ChatGPT that assist with writing or suggesting code snippets, Justine shares how mass-scale refactoring calls for a very different set of tools and methods. At Moderne, AI is applied with precision inside an open-source framework called OpenRewrite, which enables consistent and verifiable code changes while maintaining enterprise-level reliability and security.We discuss how Moderne's approach blends deterministic automation with targeted machine learning to make code migrations faster and more trustworthy. From onboarding new developers to simplifying upgrades across legacy systems, the real-world impact of this work is becoming increasingly visible in sectors like banking and insurance, where complexity and risk have historically slowed down innovation.This episode also dives into how AI enhances collaboration between developers and machines. Justine highlights the potential for AI to become a quiet partner in understanding, searching, and maintaining vast repositories of code and why this shift may help organizations reduce technical debt and increase maintainability over time.For business leaders evaluating how AI fits into their development strategy, this conversation offers a practical look at how to make meaningful progress without cutting corners. Whether you're leading a digital team or managing critical systems, Justine's insights reveal what it truly takes to put AI to work at scale.

In this debut episode of AI at Work, part of the Tech Talks Network, we sit down with Gautam Singh, Head of the Business Unit at WNS Analytics and co-founder of The Smart Cube, to uncover how analytics and AI are helping organisations navigate a rapidly evolving digital-first business world. With decades of experience in data strategy and management consulting, Gautam brings a grounded yet forward-looking view on integrating intelligence into the enterprise.We explore how WNS helps clients cut through the noise of AI hype by anchoring innovation in practical use cases and structured strategy. Gautam shares a compelling example of a global retail client achieving a 13.5x return on analytics investments, and unpacks why businesses should start small with “data ponds” rather than aim for comprehensive “data lakes” from day one.He also challenges popular misconceptions about AI, explaining why not everything needs a model and how Excel sometimes still does the job. We examine the importance of traceability, regulation, and a “maker-checker-consumer” framework that ensures human oversight remains central to AI implementation.Looking ahead, Gautam discusses how collaboration across industries, adaptability, and a clear North Star are key to staying resilient and competitive. This is a conversation for leaders who want to move beyond buzzwords and make meaningful progress with AI and analytics.How can your business approach AI in a way that delivers real outcomes instead of just more complexity? Tune in to hear Gautam’s adv