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Driven by Data: The Podcast

Orbition Group

Orbition Group is delighted to bring you this podcast series, which is designed for Data Enthusiasts, to hear from some of the most high-profile Data, Analytics and AI thought leaders from around the globe.

Each episode will detail the guests journey to the top while bringing unique insights, drawn from first-hand experience on the industry’s most trending topics.

This podcast was created as a way for our industry's most respected leadership figures from across the world to give back to the Data & Analytics community, by sharing; knowledge, experiences and ideas, to inspire, innovate and provide real-life use cases on the industries most pressing topics/challenges.

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S7 · E31
Yesterday · 54 min
S7 | Ep 31 | Data Foundations Only Get Funded When They’re Tied to a Business Outcome with Barry Panayi, Sarah Barr Miller, Sid Shah and Nina Bryant (recorded live at Driven by Data LIVE 2026)
In Episode 31 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Barry Panayi, Chief Data Officer at Howden, Sarah Barr Miller, Chief Data Officer at British Airways, Sid Shah, VP Data, International at Live Nation Entertainment, and Nina Bryant, Data, Analytics & AI Partner at Baringa, recorded live at Driven by Data LIVE 2026, where they discuss how data leaders balance foundational work with transformation, and why foundations only get funded when they are tied to a clear business outcome. The panel covers how to prioritise foundations, how to deliver them through the use cases the business actually wants, and the trade-offs between speed, scale, control and innovation. They also discuss: Why foundational data work gets funded when it is delivered “by stealth” through the use cases the business wants. How Howden’s “bullseye” approach shows the commercial prize, prices what it takes to unblock it, and lets the business choose. Why data leaders need to define when foundations are “good enough” and move on to building new capabilities. What it means to give the board a number for how far foundational work is complete, and how acquisitions change the denominator. Why AI is finally giving data leaders a way to put an ROI around data foundations. How the gap between past AI returns and C-suite expectations of a 20% return in two years creates pressure on data teams. Why transformation becomes noise when the data function treats it as a mission in itself. How to separate AI, automation and process reinvention when the business simply asks for “AI”. Why speed buys the right to take time on the next piece of work, and how to be honest about what quick wins cannot yet do. How a low-risk, high-value use case can be fast-tracked while higher-risk decisions are considered carefully. Why communicating wins matters as much as delivering them, and how perception shapes how a data team is judged. How bottom-up AI use cases risk duplication, and how to scale the highest-value ones across the organisation. Why most organisations are not yet managing AI spend effectively, and what happens when the early-adopter discount ends. How embedding commercial insurance experts in the data team gives a business context layer. What the future holds for the Chief Data Officer role, and whether AI needs a separate leader. Why “a 1% error rate equals a 100% issue rate” in the age of AI, and how that has changed board conversations about data quality. What new board questions, such as sustainability and sovereignty, may come next. Thanks to our sponsor, Data & AI Literacy Academy. Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today’s data-centric world, being data literate is no longer a luxury, it’s a necessity. If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first. At Data & AI Literacy Academy, they don’t just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They’re bridging the gap between business and data teams, so they can all work towards aligned outcomes. From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it’s not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme. They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve. Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com/driven
S7 · E30
Thursday · 32 min
Data Debrief: Halloween Special!
In this week's Data Debrief, Catherine Dowden-King & Kyle Winterbottom have a special Halloween episode! They discuss the tricks of the market, the horror stories that persist and the zombie challenges that won't seem to stay gone!
S7 · E30
October 27 · 33 min· Video
S7 | Ep 30 | Leader of the Year, Di Mayze, Chief Data & AI Officer, Marks and Spencer
In Episode 30 of Season 7 of Driven by Data: The Podcast, Catherine Dowden-King is joined by Di Mayze, Chief Data and AI Officer at Marks & Spencer, where they discuss why the CDO role has to prove its value, why clean data foundations still come before AI, and how to build a data culture people want to be part of. As featured in Driven by Data Magazine 2026. As the first person in post at M&S, and previously the first global head of data and AI at WPP, Di shares what she learned at dunnhumby, Boots and WPP, why curiosity matters more than jargon, and how she is balancing innovation with strong data foundations. They also discuss: What dunnhumby and Tesco taught Di about putting data at the heart of every decision. Why she is comfortable saying “I don’t know”, and how that curiosity shapes her teams. Why great data leaders make a subject accessible rather than hiding behind acronyms. How to build confidence in asking “what do you mean?” when vendors say a product is AI powered. How Di built a 3,000-strong data and AI community at WPP through influence rather than power. Why trust, credibility and championing your team’s work matter more than formal authority. Why Di believes there is a direct correlation between joy and productivity. Why the CDO role was in trouble, and why CEOs wanted proof of its value. Why Di wants her team to keep automating its own work and moving into new areas. How to avoid being distracted by shiny new tools while still tracking the market. What makes a data-and-AI mix of foundations and quick wins work when leaders need to show momentum. Why putting AI on bad data or bad processes is a disaster. How M&S is using Copilot licences and employee-built agents to lift individual productivity. How the Sparks loyalty scheme balances personalisation with trust. Why a data culture can’t just mean more reports, and how to motivate action instead. What responsible AI looks like in practice: explainability, and humans before, in and after the loop. Why asking the same question several ways can reveal whether a model is just trying to please you. What a story about an AI deleting people from a gym queue shows about unintended consequences. Why measuring impact is the career advice Di would give her younger self. Thanks to our sponsor, Data & AI Literacy Academy. Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today’s data-centric world, being data literate is no longer a luxury, it’s a necessity. If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first. At Data & AI Literacy Academy, they don’t just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They’re bridging the gap between business and data teams, so they can all work towards aligned outcomes. From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it’s not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme. They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve. Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com/driven Thanks to our sponsor FOIL AI FOIL are an AI consultancy, and one of the most exciting to watch right now. Fast-growing, genuinely ambitious, and refreshingly down to earth, with a leadership team who have been doing this for years and are well respected for it. FOIL push data leaders to claim a voice at the top table, to lead the business rather than trail behind it with a handful of AI productivity tools. They push the point that if every competitor has the same tools, productivity is not an advantage. The real prize with AI is bigger and FOIL have the expertise – both strategic and technical – to push the boundaries of what your organisation can achieve, in a practical way. FOIL are a lot bolder and braver that the traditional consultancy. That confidence comes from deep practitioner expertise, and it shows in how they engage with their partnerships. They are claiming the phrase: the autonomic business. A business that senses what is happening, decides within clear guardrails, adapts as things change, and keeps improving on its own. Intelligence built into how the company runs, and they’ve just been recognised at the British Data Awards for their work with Welsh Water on exactly this concept. Learn more at https://foilai.co.uk/
S7 · E29
October 22 · 49 min
Data Debrief: FOMO isn't a Strategy! Culture, Context & The Black Box Problem
Welcome to another episode of Data Debrief, the companion show to Driven by Data: The Podcast, hosted by Catherine Dowden-King and Kyle Winterbottom. This week, Catherine reports back from the Marketing Showcase in Bristol, her first event as a pure attendee in a long time. The talk was all data, analytics and AI. She and Kyle explore why marketing and data share the same struggle to show their impact. They also cover how B2B brands can stand out: know exactly who you’re speaking to, be authentic, and don’t jump on every bandwagon. They then turn to a viral LinkedIn post from a Meta software engineer who says he has never felt more uncomfortable in his job. He is shipping AI-generated code he doesn’t fully understand. Is this whistleblowing turning into red-flag waving, and where should the line sit? The conversation moves to Lee Fulmer’s recent Driven by Data episode. He is Head of Data at Canadian Life Reinsurance. They cover: Why clean data alone isn’t enough for AI, and why context is what separates winners from losers. What businesses lose when people leave, and the cultural “lore” that will never make it into a prompt. Why FOMO is not a strategy. Servant leadership, and why “everything else is waste” is a useful way to frame your role. Why businesses that hire for outcomes get outcomes, and what that means for your CV. They also share news of the final Orbition community event of the year in mid-November, and mentorship activity in late November. Listen to Lee’s full episode on Driven by Data: The Podcast.
S7 · E29
October 20 · 49 min
S7 | Ep 29 | $1bn Profit, 400 People, One Data Leader Who Isn’t an Actuary: Why Clean Data Still Isn’t AI-Ready with Lee Fulmer, Head of Data at Canada Life Reinsurance
In Episode 29 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Lee Fulmer, Head of Data at Canada Life Reinsurance, where they discuss why clean, well-governed data still isn’t AI-ready, and why context, a clear business outcome and curation matter more than any blueprint. Canada Life Reinsurance reported around a billion dollars in profit last year from roughly fifty deals, with a workforce of around 400 people in which almost everyone is an actuary. Lee reports to the CEO as the first non-actuary the business has hired, which makes it a high-stakes setting for questions about data, AI and accountability. He also explains why the “AI will replace the data team” belief has faded, how cost and the general ledger are forcing a rethink, and why agentic AI is not the right answer for most businesses yet. They also discuss: Why a data leader in a business with billion-dollar profits on a handful of deals can’t afford to treat data as raw numbers alone. Why the belief that AI makes the data team redundant took hold, and why it has unwound. How the shift to small language models and proprietary training data is putting data back at the centre. Why companies are hiring heads and directors of data rather than chasing the Chief Data Officer title. What matters more than a job title: responsibility, reporting line and empowerment. Why high-quality, well-governed data is not the same as being AI ready. What data governance has missed: the context behind a piece of information, not just its accuracy. How actuaries and reinsurance show the difference between raw data and the context that gives it meaning. Why context, not content, is king, and what the knowledge management craze of the 1990s teaches us. Why there is no universal blueprint for AI readiness, and why every organisation is a unique case. How organisational behaviour and change management matter more than technical skills in data transformation. Why this wave of AI is different: it is accessible to everybody, not just a few million specialists. How a generation that has grown up digital is approaching AI differently from older colleagues. How the general ledger is forcing leaders to question spiralling AI and data platform costs. Why many businesses have more AI pilots than they can measure, and what the RPA era should have taught them. Why the cost of compute is rising, and why success measures must be defined before AI is deployed. Why agentic AI risks repeating the RPA story in organisations with weak IT foundations. How fear of missing out drives AI decisions, and why starting with the outcome gives the right answer. What leaders should do first if their data sits in a data lake: curate it around the outcome, and keep the context. Thanks to our sponsor, Data & AI Literacy Academy. Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today’s data-centric world, being data literate is no longer a luxury, it’s a necessity. If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first. At Data & AI Literacy Academy, they don’t just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They’re bridging the gap between business and data teams, so they can all work towards aligned outcomes. From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it’s not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme. They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve. Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com/driven Thanks to our sponsor FOIL AI FOIL are an AI consultancy, and one of the most exciting to watch right now. Fast-growing, genuinely ambitious, and refreshingly down to earth, with a leadership team who have been doing this for years and are well respected for it. FOIL push data leaders to claim a voice at the top table, to lead the business rather than trail behind it with a handful of AI productivity tools. They push the point that if every competitor has the same tools, productivity is not an advantage. The real prize with AI is bigger and FOIL have the expertise – both strategic and technical – to push the boundaries of what your organisation can achieve, in a practical way. FOIL are a lot bolder and braver that the traditional consultancy. That confidence comes from deep practitioner expertise, and it shows in how they engage with their partnerships. They are claiming the phrase: the autonomic business. A business that senses what is happening, decides within clear guardrails, adapts as things change, and keeps improving on its own. Intelligence built into how the company runs, and they’ve just been recognised at the British Data Awards for their work with Welsh Water on exactly this concept. Learn more at https://foilai.co.uk/
S7 · E28
October 15 · 41 min
Data Debrief: Russian Bots, LIVE was hot & Kyle weeps as he's getting old
In Episode 28 of Season 7 of Data Debrief, Catherine Dowden-King and Kyle Winterbottom look back on a packed Driven by Data Live and the lessons from its roundtables. They then turn to what a reported attack on a Russian data centre might mean for data leaders, and debrief their conversation with German Faraoni, Global Director of Shared Services at Reckitt, on why data teams need to be in the right room. In this episode: Why Driven by Data Live was oversubscribed, and why roughly 45% of attendees being new to the events is good for the community. How Chatham House round tables help people share candidly, and why authenticity matters more than ever in an AI world. An HR agent use case: an anonymous assistant on top of an HR catalogue, with guardrails that signpost support and flag sensitive questions to the HR team. Why internal, people-led AI use cases are likely to come before customer-facing ones, and how they build confidence. How something as simple as understanding your maternity leave policy can influence employee loyalty, especially if non-competes are challenged in the UK. What the reported disruption to a Russian data centre, and the apparent drop in bot and crawler activity, suggests about how much online engagement is real. Both hosts are careful to flag that the reporting is still developing. Why geopolitical stability and data sovereignty are likely to be on every event agenda next year. How AI is giving data management, governance and quality, as well as cybersecurity, a new lease of life. How CISOs balance battening down the hatches against smothering the business, and what data leaders can learn from them. Why data leaders need to work closely with PR and understand their customers’ values, since decisions about where data lives could cost customers. Why doing business is more complex than ever, and what boards are weighing up on AI. A debrief of the interview with [Guest name], including the “if you’re in this room, you’re in the wrong room” idea. How a SKU-based use case showed the value of sitting with the executive team and working back to a use case that moves the P&L. Why no one will invite you into the room, and how to build the trust and credibility to get there. Why data teams working in isolation risk building things that go nowhere. How to write a CV for two audiences, those screening for keywords and those looking for outcomes, and why hiring managers say AI-written CVs now look the same. Community shout: the final event of the year, the quarterly roundtable, takes place on 17th November. Find the details at orbitiongroup.com.
S7 · E28
October 13 · 52 min
S7 | Ep 28 | The Data Translation Challenge with German Faraoni, Global Director of Shared Services for Data at Reckitt
In Episode 28 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by German Faraoni, Global Director of Shared Services for Data at Reckitt, where they discuss why data and AI investment still struggles to show measurable value, and why the answer lies in articulating it in business outcomes rather than data terms. German also shares how Reckitt cut the time to get a new SKU onto shelves by almost half, and how data leaders can earn their place in the right business conversations. They also discuss: Why data leaders are business professionals first and data specialists second. Why investment in data and AI is still articulated in data terms rather than business terms. How the same trap is repeating with AI, and what “hype credit” means for justifying investment. Why the metrics for data and AI should be the metrics the business already uses to measure success. What a data leader should ask a prospective employer before taking a role. How Reckitt reduced the time to commercialise an SKU from around 200 days to roughly half. Why the SKU release project started with process redesign rather than technology. How Gen AI emerged as the right tool for the job rather than the starting point. Why data practitioners must sit in the business conversations where results are discussed. Why starting small and avoiding “boiling the ocean” matters for scalability. How governance councils by data domain create a sustainable foundation for data quality. Why remediating legacy data issues remains hard, and how to choose which ones to fix. How AI lowers the cost of intelligence and speeds up the decisions that follow insight. What using AI as a “canary in the mine” reveals about weaknesses in your data and access controls. How to approach AI across internal efficiency, reimagining the existing business and creating new business models. Why agentic commerce could reshape how consumers and retailers interact. What AI means for the future of the CDO role, and who may be most exposed to change. Why competitive advantage will come from the right mix of premium and commodity AI, process understanding and orchestration. How large incumbents respond to disruption from smaller, faster AI-enabled businesses. Thanks to our sponsor, Data & AI Literacy Academy. Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today’s data-centric world, being data literate is no longer a luxury, it’s a necessity. If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first. At Data & AI Literacy Academy, they don’t just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They’re bridging the gap between business and data teams, so they can all work towards aligned outcomes. From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it’s not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme. They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve. Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com/driven Thanks to our sponsor FOIL AI FOIL are an AI consultancy, and one of the most exciting to watch right now. Fast-growing, genuinely ambitious, and refreshingly down to earth, with a leadership team who have been doing this for years and are well respected for it. FOIL push data leaders to claim a voice at the top table, to lead the business rather than trail behind it with a handful of AI productivity tools. They push the point that if every competitor has the same tools, productivity is not an advantage. The real prize with AI is bigger and FOIL have the expertise – both strategic and technical – to push the boundaries of what your organisation can achieve, in a practical way. FOIL are a lot bolder and braver that the traditional consultancy. That confidence comes from deep practitioner expertise, and it shows in how they engage with their partnerships. They are claiming the phrase: the autonomic business. A business that senses what is happening, decides within clear guardrails, adapts as things change, and keeps improving on its own. Intelligence built into how the company runs, and they’ve just been recognised at the British Data Awards for their work with Welsh Water on exactly this concept. Learn more at https://foilai.co.uk/
S7 · E27
October 8 · 41 min
Data Debrief: ASOS Breach, AI Index & Driven by Data LIVE
In this episode of the Data Debrief, Catherine Dowden-King and Kyle Winterbottom reflect on the week's news and the latest Driven by Data: The Podcast interview, with Tom Sadler of HP. Their central question is whether organisations are measuring AI activity (headcount, spend, job titles, tooling) instead of the productivity and financial outcomes that matter. They begin with the ASOS breach and the questions it raises about vendor risk. They then work through the latest Evident AI Index and what it rewards, before turning to Tom's candid views on hardware, cost and culture. They also discuss: Why the ASOS ransom note, delivered through the company's own app, marks a new and uncomfortable form of attack. How data leaders should work in lockstep with the CISO, or with the CIO where there is no CISO, as vendor ecosystems grow. Why the full story of a breach like this can take months to emerge, and why speculation helps no one. Why only 12% of reported AI use cases in the Evident AI Index demonstrate operational impact, and just 1% report financial impact. How the index's weightings (45% talent, 30% innovation, 10% transparency) shape what it rewards. Why organisations with the strongest financial returns from AI may not appear on the index at all. What the "too early to judge" argument gets right, and why 1,100 use cases over five years still demands better measurement. Why leaders chasing a higher ranking risk spending more money for the same 1% return. How adding "AI" to a job title, without mandate or budget, repeats the chief data officer pattern. Why the market is starting to reward titles over delivery when it hires. Why Tom Sadler argues hardware should be the last thing you think about in an AI strategy. How local compute can cut experimentation costs compared with spiralling cloud token costs. What running AI locally means for environmental impact. Why the CDO and CIO sometimes have never met, and how that drives sprawling costs and shadow AI. How a confined local "sandbox" lets teams experiment safely, with successful ideas scaled across the business. Why culture, not tooling, decides whether you can spot what has worked, and why a use case that cuts five hours of work to 30 minutes needs a place to be shared. What it takes to build the honesty to review mistakes regularly and correct them. Why a chief financial officer's advice to start running in your old trainers applies to AI spend.
S7 · E27
October 6 · 52 min
S7 | Ep 27 | The Secret to Enterprise AI Success according to HP with Tom Sadler, Data Science and AI Solutions Lead, HP
In Episode 27 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Tom Sadler, Data Science and AI Business Solution Lead at HP, where they discuss why most AI and data strategies stall not because of the technology, but because different teams and divisions inside the same organisation simply don't talk to each other. They also explore why the best AI use cases are often built by accident, by employees solving their own problems rather than having a solution handed down from the top; how HP's compact AI workstations are bringing serious compute to the edge; and why data sovereignty and energy consumption are becoming unavoidable parts of every technology decision. They also discuss: Why most organisations buy technology before they understand the problem they're trying to solve. How workflow, governance and people should shape technology decisions, not the other way round. Why organisational silos lead divisions to pursue contradictory AI strategies without realising it. What "people, process, data, governance, technology" means as a decision-making order. Why the best AI solutions are often built by accident by employees solving their own problems. How HP's compact AI workstations bring trillion-parameter-scale compute to the edge. Why starting small and scaling out beats one large, upfront AI investment. How sandboxed, local compute lets teams experiment safely without runaway cloud costs. What the US Cloud Act and data sovereignty mean for where organisations choose to host their data. Why defence and government customers require fully offline, lockdown-capable AI environments. Why token pricing is more expensive in practice than headline prices suggest. How copilots and AI assistants need explicit rules and training to be genuinely useful. Why AI leadership is often fragmented across too many overlapping roles in large organisations. What it takes to give employees the freedom to experiment without losing control of security. Why storytelling and simplification matter when explaining AI to non-technical stakeholders. How rising AI energy and water use is becoming an unavoidable business consideration. Why prompt engineering training could meaningfully reduce unnecessary cloud AI usage. Thanks to our sponsor, Data & AI Literacy Academy. Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity. If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first. At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They're bridging the gap between business and data teams, so they can all work towards aligned outcomes. From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme. They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve. Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com. Thanks to our sponsor FOIL AI FOIL are an AI consultancy, and one of the most exciting to watch right now. Fast-growing, genuinely ambitious, and refreshingly down to earth, with a leadership team who have been doing this for years and are well respected for it. FOIL push data leaders to claim a voice at the top table, to lead the business rather than trail behind it with a handful of AI productivity tools. They push the point that if every competitor has the same tools, productivity is not an advantage. The real prize with AI is bigger and FOIL have the expertise – both strategic and technical – to push the boundaries of what your organisation can achieve, in a practical way. FOIL are a lot bolder and braver that the traditional consultancy. That confidence comes from deep practitioner expertise, and it shows in how they engage with their partnerships. They are claiming the phrase: the autonomic business. A business that senses what is happening, decides within clear guardrails, adapts as things change, and keeps improving on its own. Intelligence built into how the company runs, and they’ve just been recognised at the British Data Awards for their work with Welsh Water on exactly this concept. Learn more at https://foilai.co.uk/
S7 · E26
October 1 · 38 min
Data Debrief: Dark Hair, AI Nightmare & The Biggest Role in Data?
Catherine and Kyle are deep in event-season chaos this week — the printed Driven by Data magazine has just gone to print, the digital edition is about to launch, and Driven by Data Live is just eight days away. Catherine recaps her (very delayed) trip to Big Data London: reconnecting with the community, a noticeably different vendor landscape stacked with new AI-era players, and "context" as this cycle's buzzword of choice. It sparks a wider conversation with Kyle about why staying visible in the market, through events, LinkedIn or simply contributing to the conversation, matters more than ever in a leadership hiring market where six months out of work is fast becoming the norm. From there, the conversation turns to AI and human oversight, prompted by the now-infamous clip of a Canadian politician reading his AI-generated speech live, chatbot suggestions and all. Catherine and Kyle use it to unpick a bigger pattern: traditional checks and review are quietly disappearing as people lean on LLM output uncritically, whether that's AI-generated pitch decks stripped of intentional choices, or candidates' CVs that increasingly sound like the job description they were lifted from. They also debrief Tuesday's episode with Aimee Smith, UK Government Chief Data Officer, covering her culture-not-technology diagnosis of Whitehall's data-sharing problem, the surprising admission from Microsoft and Amazon that government's scale may be beyond current technology, and just how subjective "success" and value become once you're operating in public service rather than the private sector. They also discuss: Why Catherine's top tip about not moaning about trains backfired within hours of giving it. Why so many familiar faces were missing from Big Data London's vendor stands, and what that says about the market. Why "context" has become this season's buzzword, and why some vendors are stretching to say yes to whatever a prospect asks for. Why stepping out of the market for 18 months, even while genuinely busy doing the day job, can leave you without a way back in when you need one. Why the days of walking straight into a new role within a few weeks of redundancy are over. Why one CDO is advising his mentees to keep six months of financial runway. Why leadership hiring is busier than ever, but so is the competition for every role. What the viral clip of a politician reading his AI-generated speech, chatbot prompts and all, reveals about eroding human oversight. Why neither Catherine nor Kyle blame the politician himself, and where they think the real failure sits. Why AI-generated pitch decks and slides are losing the intentionality that came from every element being a deliberate choice. Why candidates rewriting their CVs to mirror a job description word-for-word is backfiring on them. Why six major banks are flagging the fraud and accountability risks of AI agents transacting on people's behalf. Why B2C brands are already leaning into "handmade" and human-made marketing, and why B2B is likely to follow. Why hiring a copywriter who sounds human could become a genuine differentiator again. Why Aimee Smith's move from 25 years in policing to UK Government Chief Data Officer meant relearning how power and language actually work. Why even Microsoft and Amazon weren't confident the technology exists to handle government's scale and complexity. Why culture and risk appetite, not technology, are the real blockers to data sharing across government. Why "value" and "success" mean something fundamentally different in public service than in the private sector. Why government's federated, siloed structure makes Aimee's mandate harder than the equivalent role in the private sector. A reminder that Aimee Smith will be at Driven by Data Live next week, so bring your questions.
S7 · E26
September 29 · 52 min
S7 | Ep 26 | 300 days as the UK Governments CDO with Aimee Smith, UK Government Chief Data Officer, UK Civil Service
In Episode 26 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is rejoined by Aimee Smith, Chief Data Officer at the UK Government, where they discuss her move from 25 years in policing and law enforcement into government's top data role, and why she believes culture, risk appetite and misaligned investment incentives, not legislation or technology, are what's really blocking data sharing across Whitehall. They also touch on the UK's evolving position on data and AI sovereignty, the real purpose behind the National Data Library, and how Aimee is building a framework to track and prove the value of government data. They also discuss: Why Aimee left a 25-year career in policing to become the UK Government's Chief Data Officer. How she is building influence across government departments without having formal authority over them. Why she restructured the Chief Data Officer Council around a strategic data roadmap and sector-based leads. What makes data sharing across government harder than the equivalent challenge in the private sector. Why culture and risk appetite, not legislation or technology, are the biggest blockers to data sharing. How she plans to fix the incentive problem by changing how Treasury assesses technology and data investment bids. Why she wants government's most valuable data treated as critical national infrastructure. How linking investment to data assets rather than departments could unlock sharing. Why legacy technology is as much a data cataloguing problem as a modernisation one. What she believes better data sharing could unlock for citizens navigating public services. Why she thinks the data sovereignty debate currently overlooks the data itself. How she is working with cross-government commercial teams to embed sovereignty principles into procurement. What the National Data Library actually is, and the shifting priorities behind its original manifesto pledge. How the National Data Library differs from a centralised data lake. Why AI adoption depends on getting the underlying data foundations right first. How she is using AI as a way to keep data on the agenda inside government. Why she is cautious about how AI investment gets communicated to the public. What her three-part framework for tracking data value looks like (commercialisation, valuation, usage). How Treasury's new balance sheet requirement for data valuation came about. What she hopes to point to as evidence of progress in twelve months' time. Thanks to our sponsor, Data & AI Literacy Academy. Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity. If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first. At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They're bridging the gap between business and data teams, so they can all work towards aligned outcomes. From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme. They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve. Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com. Thanks to our sponsor FOIL AI FOIL are an AI consultancy, and one of the most exciting to watch right now. Fast-growing, genuinely ambitious, and refreshingly down to earth, with a leadership team who have been doing this for years and are well respected for it. FOIL push data leaders to claim a voice at the top table, to lead the business rather than trail behind it with a handful of AI productivity tools. They push the point that if every competitor has the same tools, productivity is not an advantage. The real prize with AI is bigger and FOIL have the expertise – both strategic and technical – to push the boundaries of what your organisation can achieve, in a practical way. FOIL are a lot bolder and braver that the traditional consultancy. That confidence comes from deep practitioner expertise, and it shows in how they engage with their partnerships. They are claiming the phrase: the autonomic business. A business that senses what is happening, decides within clear guardrails, adapts as things change, and keeps improving on its own. Intelligence built into how the company runs, and they’ve just been recognised at the British Data Awards for their work with Welsh Water on exactly this concept. Learn more at https://foilai.co.uk/
S7 · E25
September 24 · 40 min
Data Debrief: Networking, Intentionality & CVs that stand out
In this week's Data Debrief, Kyle Winterbottom and Catherine Dowden-King recap the Future of Data, AI & BI Summit with Starburst, share Catherine's top tips for networking at data events, and debrief this week's main episode with Simon Turner, Chief Technology Officer at Foil AI — including the Welsh Water case study that brought his "autonomic business" concept to life. They also discuss: Why the Future of Data, AI & BI Summit's core theme was the gap between giving people AI tools and reimagining how a business actually operates. What "codified business knowledge" — the context layer — means for scaling AI from local productivity hacks to full agentic systems. Catherine's top tips for networking at data events: doing your homework, messaging speakers in advance, and finding natural pauses for small talk. Why leading with curiosity, rather than negativity, makes conversations easier to build on. Why giving yourself permission to step away from an event is as important as showing up to it. How Simon Turner's Welsh Water case study used agentic AI to cut through false alarms during this summer's drought. Why the "autonomic business" concept compares agentic AI to a heartbeat that speeds up without conscious thought. Why having access to AI tools is no longer a competitive advantage, now that everyone has them. The debate between focusing on a handful of core business metrics versus giving people room to experiment and discover new use cases by accident. Why the best AI use cases sometimes emerge locally, before being scaled across a business. Why AI is making CVs look increasingly similar, and how outcomes-focused writing helps candidates stand out. Why intentionality — in networking and in career planning — matters more than ever in a fast-changing market. Why data and AI leaders should back the people already experimenting successfully with AI tools, not just the official roadmap. A preview of the "data dilemmas" segment returning for Driven by Data Live.
S7 · E25
September 22 · 47 min
S7 | Ep 25 | The Autonomic Business: Why AI Is a Leveller, Not an Advantage with Simon Turner, Chief Technology Officer at FOIL AI
In Episode 24 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Simon Turner, Chief Technology Officer at FOIL AI, where they discuss the "autonomic business" – organisations run by bounded, goal-driven digital entities that work alongside humans – and why, when every company has access to the same models and tools, competitive advantage has to come from somewhere else: your codified business knowledge, your data, and the creativity of your people. The conversation covers why the most common mistake is automating processes that shouldn't exist in their current form, a real-world water utility case where an autonomic entity cut alarm triage from 40 minutes to seconds during the drought, and why ownership of this agenda ultimately lands with a properly empowered CDO. They also discuss: Why generative AI was the catalyst for rethinking the traditional consulting model. Why "we want to do more AI" is the wrong request, and what businesses actually need. Why, if everyone says they're behind, someone has to be in the lead. Why the technology is easy, and landing it as systemic change is the hard part. What an autonomic business is, and why the term comes from biology. How an autonomic entity differs from RPA and traditional automation. Why autonomic entities pursue goals rather than execute fixed processes. How Gartner's digital-twin thinking seeded the idea years before ChatGPT. Why the autonomic business depends on knowledge management, not technology. How LLMs and knowledge graphs unlocked the 80% of business information that is unstructured. Why access to the same tools is a leveller, not a competitive advantage. Why reducing cognitive load matters more than raw speed. Why operating model and culture decide whether AI transformation succeeds. Why automating a broken process at scale creates no value. Why most business processes live in people's heads, and how to make them computable. How a water utility used an autonomic entity to cope with alarms rising from 800 to 3,800 a day. What the four human–AI partnership models look like, from "entity proposes, human decides" to "human retains authority". What needs to be true before entities can act fully autonomously. Why the CDO should own the AI agenda, and why that role can no longer sit inside IT. Why generative AI is becoming the Excel of the 90s. Why ROI rarely comes from a single AI project. Why headcount is a dangerous yardstick for ROI, and what successful organisations do instead. Thanks to our sponsor, Data & AI Literacy Academy. Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity. If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first. At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They're bridging the gap between business and data teams, so they can all work towards aligned outcomes. From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme. They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve. Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com.
S7 · E24
September 17 · 40 min
Data Debrief: We get Political
In this week's Data Debrief, Catherine Dowden-King and Kyle Winterbottom are recording a day early ahead of a busy stretch in London – a custom client event, the Future of Data, AI & BI Summit and Big Data London, with Driven by Data Live on 8th October. The headlines this week belong to Donald Trump, who has dismissed AI safeguards and calls for a "kill switch" as a hoax, which sets up the episode's central question: what happens when the people with the power to sign off on AI – presidents or CEOs – sit well above the technical detail and the risk? Catherine and Kyle draw the parallel between geopolitical "space race" thinking and the boardroom instinct to move first and mop it up later, before turning to the vetting questions every data leader should be asking of vendors and LLM providers: what are their values, why are they in the market, and what's in it for them? They then debrief Tuesday's episode with David Castro-Gavino and Boyan Angelov – merchants of complexity, friction versus maturity models, and a CDO role that is hired without an objective – and Kyle's thought of the week on the environmental cost of AI that nobody in the industry seems to be talking about. They also discuss: Why one of the world's most powerful people calling AI safeguards a hoax is a bigger statement than it sounds. Why the AI race is being driven by the fear of China catching up, and how that agenda filters into business. Why "just do it, we'll mop it up later" is the same decision whether it's made in the White House or the boardroom. Why the spectrum between doomsday and handbrake-on leaves everyone struggling to know who to trust. What questions to ask of any vendor or LLM provider before plugging them into your business. What the Careless People revelations about targeting insecure teenagers tell us about tech companies' incentives. Why tech companies handling health and genetic data aren't regulated like health companies. Why "just because you could doesn't mean you should" is the age-old debate, and why nobody boycotts anyway. How a tight-knit CDO community quietly blacklists vendors with poor ethics. Why the vendor community has to own the fact that every pitch deck now sounds the same. Why executives can be forgiven for not understanding the weeds of "AI-powered" everything. How the event agenda has flipped from getting executives to care about data to reining them in. Why data leaders have to learn to sell, and why selling is really just communication. Why influence at ExCo level comes down to trust, credibility and relationships. Why "merchants of complexity" and self-inflicted complexity resonated so strongly with listeners. Why friction isn't uniform, and why "the whole thing's a mess" is rarely true for every department. Why maturity models are theatre, and diagnosing friction through each role's lens is the practical alternative. Why frameworks can contain thinking, and why data people run to structure when they might need creativity. Why the CDO must be the only senior role in business hired without an objective, and what a 90-day plan is really for. Why nobody in the AI adoption conversation is asking whether we need to be using it at all. What 700ml of water per ChatGPT prompt says about the environmental cost of replacing Google. Why nothing in data is sociologically neutral, and why people only care about data centres once the bulldozers arrive. What the new Driven by Data Productions brand means for the community, the podcast, the events and the magazine. How to get 20% off Enabling Data with the code DRIVEN20.
S7 · E24
September 15 · 58 min
S7 | Ep 24 | Are you a Merchant of Self-Inflicted Complexity? David Castro-Gavino, Executive Director, Head of Data Deployment at AstraZeneca and Boyan Angelov, Principal Strategist at Exxeta
In Episode 24 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is rejoined by David Castro-Gavino, Executive Director, Head of Data Deployment at AstraZeneca, and joined by his co-author Boyan Angelov, Principal Strategist at Exxeta, where they discuss their new book, Enabling Data, and why the data industry is still stuck in Groundhog Day. The same three arguments – who owns that number, is it right, and why does it take so long – have been repeating for thirty years, and rather than fixing them, the industry keeps renaming the problem. The conversation covers why most complexity in data is self-inflicted, why maturity models are "data theatre" compared to diagnosing friction, and why AI hasn't solved any of this – it has poured fuel on the fire. They also discuss: Why the industry has a short collective memory and keeps rediscovering problems solved twenty years ago. What the three recurring arguments are that every data organisation keeps having. Why renaming the symptom – big data, data mesh, platforms – never fixes the underlying problem. How a simple pizza business becomes a data nightmare the moment it goes digital. Why most complexity in data is self-inflicted, and why that is good news. Why "technology is not the problem, you are" is deliberately provocative. What four questions to ask before going back to the market for a new tool. Why fixing the system, not the tool, is the maxim that matters. Who the "merchants of complexity" are, and why consultants are usually the culprits. Why making things simple is the hardest job in data. What's wrong with using maturity scores as the objective. Why measuring the wrong things promotes the wrong behaviours. How to practically find friction by refusing to accept the first answer. Why friction looks different for an analyst, an engineer and a business leader. Why not every foundational problem needs to be solved, and how to avoid spending forever in the basement. Why data teams that don't understand the business are missing the biggest opportunity. What the cargo cult is, and why copying Spotify's operating model won't make you Spotify. How the enabling model's four pillars – people, governance, technology and enablement – fit together. Why the fragility of senior data leadership is structural rather than personal. Why data doesn't create friction in an organisation, it reveals it – and gets blamed for it. Why a clear mandate matters more than who the CDO reports to. Why the industry needs to stop hiring data leaders on a shopping list of technical skills. How AI has exposed how little progress most companies have actually made on the fundamentals. Thanks to our sponsor, Data & AI Literacy Academy. Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity. If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first. At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They're bridging the gap between business and data teams, so they can all work towards aligned outcomes. From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme. They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve. Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com/driven Thanks to our sponsor FOIL AI FOIL are an AI consultancy, and one of the most exciting to watch right now. Fast-growing, genuinely ambitious, and refreshingly down to earth, with a leadership team who have been doing this for years and are well respected for it. FOIL push data leaders to claim a voice at the top table, to lead the business rather than trail behind it with a handful of AI productivity tools. They push the point that if every competitor has the same tools, productivity is not an advantage. The real prize with AI is bigger and FOIL have the expertise – both strategic and technical – to push the boundaries of what your organisation can achieve, in a practical way. FOIL are a lot bolder and braver that the traditional consultancy. That confidence comes from deep practitioner expertise, and it shows in how they engage with their partnerships. They are claiming the phrase: the autonomic business. A business that senses what is happening, decides within clear guardrails, adapts as things change, and keeps improving on its own. Intelligence built into how the company runs, and they’ve just been recognised at the British Data Awards for their work with Welsh Water on exactly this concept. Learn more at https://foilai.co.uk/
S7 · E23
September 10 · 48 min
Data Debrief: Schools Back, AI Attack & the future of paid employment
In Episode 23 of Season 7 of Data Debrief, Catherine Dowden-King and Kyle Winterbottom unpack Tuesday's conversation with Greg Freeman, CEO and Founder of Data & AI Literacy Academy, and the gap between organisations that have given everyone a Copilot licence and those that are actually using AI to change how the business operates. As Catherine puts it, a Sunday league player and a Premier League player are both "playing football" – but nobody's confusing the two. They also get into the week's headline that AI has a "greater than 10% chance" of wiping out humanity, why a growing anti-AI mood outside the data bubble matters for leaders trying to drive adoption, and why a landscaper turned content creator might be the best human-in-the-loop example going. They also discuss: Why September is the real new year for data leaders, with budget season and event chaos hitting at once. Why the "AI will kill us all" headlines are irresponsible without the evidence to back them up. How to tell the difference between a credible warning and a researcher looking for a headline on the way out the door. Why 70% of Facebook comments on an AI-generated event poster are people refusing to attend, and what that tells leaders about the mood outside the bubble. What a year five "meet the teacher" evening on WhatsApp groups has in common with the AI conversations happening in boardrooms. Why the toilet-door graffiti of the 1970s and today's comment sections are the same human behaviour at a scale our brains can't cope with. How Catherine explains agentic AI at the dinner table with trains and tracks, and why it still doesn't land. Why "AI" is used to mean automation, machine learning, LLMs and agents interchangeably, and why that confusion matters. What "buttonology" means, and why both hosts are stealing the term. Why training and education are two different interventions, and why most organisations only do the first one. What Greg's three personas – the asker, the conversationalist and the process redesigner – reveal about where most employees really are. Why AI maturity scales measure who can drive the machine rather than who's transformed their thinking. Why organisations want competitive advantage but are investing in local productivity, and why the two aren't the same thing. Why picking four or five core use cases beats a Venn diagram of everything you could possibly do. Why leaders must ask "have you actually understood this?" before accepting AI-assisted work. Why people treat LLMs like Google when Google gave you sources and LLMs give you a decision. Why the absence of sponsored results in LLMs makes people less likely to question what they're served. Why the context layer, not the tool, is where the real value in AI sits. What a founder's blanket ban on "Claude content" reveals about the perception problem holding back adoption. Why whether AI sits with the CIO or the CDO comes down to whether it's seen as a tool or a transformation. Why culture has to allow people to rip up a process and fail before any of the redesign talk becomes real. Why cutting graduate intake could leave businesses with a succession crisis in a few years' time. How the Dodgy Gardener quit his day job by pairing ChatGPT garden designs with advice from tradespeople in the comments. Why attention is the digital currency of the future, and why B2C businesses will create roles to work out how to win it.
S7 · E23
September 8 · 50 min
S7 | Ep 23 | Redesigning Organisations for AI-Driven Competitive Advantage with Greg Freeman, CEO & Founder, Data & AI Literacy Academy
In Episode 23 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is rejoined by Greg Freeman, CEO & Founder at Data & AI Literacy Academy, where they discuss why most organisations have mistaken tool training for AI literacy – and why a data-literate workforce, critical thinking and the willingness to rebuild processes from the ground up matter far more than knowing which buttons to press in Copilot. They also explore why data quality and governance are finally having their day in the sun, how leaders must role-model the discipline to challenge AI-generated work, and why the businesses winning with AI focused on four or five core processes rather than spinning up 95 pilots. They also discuss: Why the line between data literacy and AI literacy is blurry, and why Greg wants it to stay that way. Why a data-literate workforce is the enabler of an AI-ready workforce. Why data quality, governance and risk management have gone from "not that sexy" to the most important topics in the business. Why there isn't a Copilot buttonology programme in the world that can teach critical thinking. How AI slop is bleeding from LinkedIn into the work employees put in front of internal and external audiences. Why leaders must ask pointed questions of AI-enabled work to test whether the human in the loop has actually done their job. Why people treat LLMs like Google, and why that makes them less able to challenge the answers. What separates the asker, the conversationalist and the process redesigner, and why 98% of employees are still stuck at the first stage. Why leaders need the mindset to burn processes down and rebuild them with AI at the core, rather than layering it on top. Why enterprise learning conflates training with education, and why that is the root of the tool-centric problem. How hyperscalers and training partners are incentivised to teach the tool rather than transferable principles. Why a workforce using Copilot instead of Google is an expensive thing, not a useful thing. Why generative AI gives executives a hands-on "aha moment" that dashboards never did. Why nine out of ten AI conversations mean generative AI, and what that costs organisations in forecasting, decisioning and recommendation opportunities. Who should own the operating model conversation with the board, and why it depends on having the right kind of data and AI leader. Why cutting headcount and graduate intake is the wrong reason to do AI, and why AI-native graduates are the hires to make. What the US market's shift from 95 pilots to four or five core use cases teaches UK businesses. How democratising AI capability into local teams frees the central team to focus on the big wins. Why customer-facing AI use cases remain a minority, and what Lloyds Bank gets right. How to measure AI literacy by whether people see and solve business problems differently.
S7 · E22
September 3 · 39 min
Data Debrief: Creepy AI Teddy, Running App Renters & The beginning of the Real AI Use cases
In this week's Data Debrief, Kyle Winterbottom and Catherine Dowden-King unpack a ChatGPT-powered "smart learning" teddy bear aimed at three-year-olds, and use it as a way into a bigger question: when we let technology deliver the output, what happens to the learning journey that used to produce it? That thread runs from toddlers and university degrees all the way into the enterprise. They also discuss Tuesday's main episode with Chris Pearce, Chief Data Officer at Ageas UK, why AI use cases are finally moving from the sandbox into production, and why the value of that work is still invisible to most customers. They also discuss: Why an AI companion that validates a child's every feeling removes the friction that teaches them how to share, wait and apologise. Why "screen-free" is a weak selling point when the device still talks back, listens and adapts. How closed-circuit toys like a Toniebox or Yoto player carry a fundamentally different risk profile to a Wi-Fi-connected, always-listening teddy. What happens when parental controls protect one side of the conversation but not what the child says. Why universities banned AI not to stop augmentation, but to stop replacement — and why that distinction matters everywhere else too. How one Strava user overlaid running-route data with rent and income data to find up-and-coming New York neighbourhoods before prices caught up. Why personal, intuitive data use cases like that one are a better route into data literacy than heavy-handed formal training. Why psychological safety keeps surfacing as the precondition for genuine experimentation with AI. How the AI hype cycle has bought data leaders more freedom to test and fail than the analytics era ever did. Why podcast guests are suddenly willing to name specific, productionised use cases when a year ago they wouldn't talk on the record. What the shift from internally-focused efficiency gains to customer-facing AI means for how organisations talk about their investment. Why a business can cut processing times from 100 days to five and still have customers asking what changed for them. How the gap between the AI narrative and the actual customer experience is becoming a reputational problem, not just a comms one. Why Kyle still had to request a paper form by post to update his details with a pension provider in 2026. How Octopus Energy empowering agents to send flowers or waive costs resets customer expectations for every other provider. Why data teams need a feedback loop with customers without becoming a ticket office that builds whatever the last complaint asked for. What Chris Pearce's point about hallucinations — that nobody ever measured how often tired, stressed humans got it wrong — says about the standard we hold AI to. Why the structural and operating model problems inside organisations, not the technology, are what keep use cases stuck in the sandbox. How the AI risk conversation has finally given data governance, quality and management their moment of investment. Why CDOs should take that funding while it's on the table, whatever vehicle got it there.
S7 · E22
September 1 · 51 min
S7 | Ep 22 | Getting AI out the Sandbox and into Production with Chris Pearce, Chief Data & AI Officer, Ageas
In Episode 22 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Chris Pearce, Chief Data and AI Officer at Ageas, where they discuss why so few organisations manage to get AI out of proof-of-concept and into live production. Chris makes the case that this is a structural and operating model problem rather than a technical one, and that the businesses which crack it are the ones that understand their own commercial engine intimately enough to know exactly which decision they are trying to change. Drawing on a 250-person function spanning data engineering, data science, AI engineering, infrastructure and governance, Chris walks through real deployments into Ageas's contact centres, how the value of those deployments is measured and attributed to the P&L, and why the risk conversation with a board is far more winnable than most data leaders assume. They also discuss: Why rolling out Copilot licences bears no resemblance to putting LLMs into front-end production systems touching customers in real time. What the full cross-functional cast actually looks like, from SRE and infrastructure to UX, middleware developers, AI engineers, business SMEs, risk, legal and compliance. Why AI delivery is fundamentally a structural problem, with the necessary skill sets scattered across different leaders, agendas and backlogs. How building AI capability in isolated pockets of the ecosystem guarantees you never leave POC land. Why the first question on any piece of data science work should be how you intend to measure it, and why nothing starts until that's answered. How Ageas used LLM summarisation at the chatbot-to-agent handover to remove friction for customers already losing patience. Why after-call work was worth attacking, and what shaving minutes off every call does to backlogs, concurrency and demand. How A/B testing capability across 50 agents against another 50, de-biased for tenure and experience, produces evidence a board can't argue with. What it takes to build a genuine culture of experimentation in an environment as dynamic as a contact centre. Why "my job is to help people" is where most value conversations begin, and how to move past it. How to trace the decision chain that follows once the phone goes down, and why that's where the financial link is found. Why brilliant technical analytics is squandered without the work of presenting it visually and narratively. What has to be true for a change in decision-making to be logged, monitored and made someone's accountability. Why any organisation asking for an AI strategy should be asked about its business strategy first. How to uncover a business strategy that isn't written on a wall or neatly captured in a PDF anywhere. Why starting with low-hanging fruit builds the patterns, the track record and the appetite for bigger bets later. What the doom loop of perpetual proof-of-concept does to credibility, investment and the perception of ROI. Why perfect temples of data platforms get built over four or five years and then fail to land. How the risk conversation changes when you demonstrate the operational, technical and information security controls that already exist. Why hallucination rates deserve to be compared with how often humans under pressure get things slightly wrong. What is missing from every AI maturity framework Chris has encountered, and why counting models in production is activity rather than maturity. Why software development skills are becoming essential for data scientists, and how AI engineering mirrors the data science unicorn boom of fifteen years ago. Why the technical barrier to entry has never been lower, and why adaptability is now the trait Chris values most. Why every practitioner needs a degree of commercial nous, and what happens to retention when people can't see the impact of their work. Thanks to our sponsor, Data & AI Literacy Academy. Data & AI Literacy Academy is leading the way in transforming enterprise workforces with data literacy across the organisation, through a combination of change management and education. In today's data-centric world, being data literate is no longer a luxury, it's a necessity. If you want successful data product adoption, and to keep driving innovation within your business, you need to start with data & AI literacy first. At Data & AI Literacy Academy, they don't just teach data skills. They empower individuals and teams to think critically, analyse effectively, and make decisions confidently based on data. They're bridging the gap between business and data teams, so they can all work towards aligned outcomes. From those taking their first steps in data & AI literacy to seasoned experts looking to fine-tune their skills, our data experts provide tailored classes for every stage. But it's not just learning tracks that they offer. They embed a deep data culture shift through a transformative change management programme. They take a people-first approach, working closely with your executive team to win the hearts and minds. We know this will drive the company-wide impact that data teams want to achieve. Get in touch and find out how you can unlock the full potential of data in your organisation. Learn more at www.dl-academy.com/driven
S7 · E21
August 27 · 36 min
Data Debrief: Blame AI! Averages Lie! and Kyle's throat sounds like he's going to... Cough.
In this week's Data Debrief, the companion show to Driven by Data: The Podcast, Kyle Winterbottom and Catherine Dowden-King unpack the week's main episode with Michael Ross and range far beyond it into the collapse in graduate hiring, the succession planning nobody is doing, and what's really happening at both ends of the data job market. From a record 45% drop in advertised graduate roles, to the experienced leaders who've been out of work for two years, to Michael's case that every average hides an opportunity, Kyle and Catherine make the argument that AI is taking the blame for decisions plenty of businesses already wanted to make, and that the bill for not developing people will land in about five years' time. They also discuss: Why a 45% drop in advertised graduate jobs is the lowest figure ever recorded, and why AI can't be held responsible for all of it. How record university enrolment colliding with a shrinking entry-level market creates a problem unfolding in real time. Why "entry-level" data roles asking for two years of Python or SQL were never really entry-level. What happens to the pipeline when the admin-heavy tasks juniors cut their teeth on get absorbed by agents. Why the real risk isn't AI replacing juniors, but having nobody ready when the current workforce retires. How data roles are shifting towards QA, product management and facing back into the business. Why succession planning has only ever been pointed at the top of the house, and why that has to change. What skills matrices and career pathways expose the moment you ask "and when this bottom layer moves up, then what?" Why some organisations announced AI-driven headcount cuts when the business was simply performing badly. How "we're cutting because of AI" got turned into a PR positive rather than a negative. Why a retailer, a telco and an airline sat at the same table are nowhere near the same stage of the journey. What the senior end of the market actually looks like, and why it gets discussed far less than the graduate end. Why there are more head of, director and VP roles than at any point in fifteen years, even as true CDO roles decline. How being overqualified has become as much of a barrier as being underqualified. Why an entire cohort of data leaders has been tarred with the same brush through no fault of their own. How the failure to prove value from data and analytics now has a direct, downstream human cost. What Michael Ross's epiphany moment says about technical specialists becoming commercial operators. Why de-averaging matters more than any dashboard, and how averages quietly mislead entire teams. How an 80% average occupancy hid the fact that no hotel was anywhere near 80%. Why 100% occupancy might be a pricing failure rather than a success story. What it takes for a CEO to get close enough to the commercial detail of their own business to win. Why putting your head above the parapet takes bravery, and why the cost of not doing it is the situation the industry is now in. Why Dolly Parton's Imagination Library may be the most important thing she ever built. What's left of the Future of Data, AI & BI event, Driven by Data Live on 8 October at Tobacco Dock, and the new roles on the NED Appointment Finder.

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