Driven by Data: The Podcast

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.

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

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.

In Episode 21 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Michael Ross, Data Agitator & Non-Executive Director at Domestic & General and Policy Expert. They discuss why so many consumer and retail businesses fail to turn data into commercial value, because they run on averages and siloed metrics that hide where money is actually being won and lost.

Drawing on a career spanning McKinsey, Figleaves, eCommera and DynamicAction, and years advising CEOs across the Abu Dhabi Investment Authority's portfolio, Michael makes the case for de-averaging: measuring distributions rather than averages, tracking failure modes rather than outcomes, and using driver trees to connect financial results back to the controllable inputs that leaders can actually change.

They also discuss:

• Why Michael's "viewed availability" epiphany at Figleaves exposed a 93% in-stock figure that was really 70%.
• How siloed teams create a hidden "coordination tax" that quietly erodes commercial performance.
• Why nobody owning the end-to-end customer journey is still the single biggest issue in most businesses.
• What separates the CEOs who thrive in a data world: strategic vision paired with "30,000 feet and two inches" of detail.
• Why "that's where the money is" is the real reason CEOs must get into the commercial engine.
• How the finance function quietly failed to become the owner of integrated, decision-driving data.
• Why averages are the enemy of commercial performance, and what to measure instead.
• How de-averaging an LTV:CAC ratio of 12 revealed that 70% of spend was acquiring customers above their lifetime value.
• Why bidding on your own brand terms on Google is often just a "navigation tax."
• What "spill and spoil" are, and how the airline and hotel industries measure their two failure modes.
• How Premier Inn's headline 80% occupancy hid hotels that were either 60% or 100% full.
• Why measuring failure metrics drives a far higher-quality conversation than chasing an average upwards.
• What driver trees are, and how a 1920s DuPont technique connects outcomes to controllable inputs.
• Why Amazon deliberately spends little time on financial outcomes and focuses on controllable inputs.
• Why conversion rate is "the CEO's metric" and a recipe for disaster when handed two levels down.
• How dashboards full of averages and filters create the "illusion of insight" rather than action.
• Why the best dashboards are the ones that "create a compulsion to act."
• How de-averaging package utilisation turns one meaningless number into clear churn risks and upsell opportunities.
• Why data teams so rarely get to this work, and how the "build the foundations" mandate traps them.
• Why so many data transformations end with "nothing's changed" and an eighteen-month reset.

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

In this week's Data Debrief, the companion show to Driven by Data: The Podcast — Davin Crowley-Sweet OBE, CDO at National Highways, steps into Kyle Winterbottom's seat and is joined by Catherine Dowden-King to unpack the week's main episode with Nick Zervoudis and range far beyond it into the human side of data leadership.

From burnout and its lesser-known cousin "rustout," to the serendipity we've lost to working from home, to why psychological safety matters more than technical mastery, Davin and Catherine make the case that the job of a modern data leader is less about building things and more about building the people and the environment in which those things get built.

They also discuss:

• Why burnout has an opposite — "rustout" — and how to tell which one you're actually facing.
• How working from home has stripped the chance encounters and serendipity out of professional life.
• Why so much success comes down to luck, and why being open to it is the real skill.
• What "the worst they can say is no" taught Catherine about taking a chance.
• Why so many people tie their identity to a job title, and what happens when the badge disappears.
• How psychological safety, not technical mastery, is the real job of a data leader.
• Why tension is healthy and shouldn't be mistaken for conflict.
• What Davin took from Nick Zervoudis's episode, and the subtle power of the words "value from."
• Why data is valuable for what you do with it, not for its inherent worth.
• How to get comfortable working in uncertainty rather than chasing a perfect data-driven story.
• Why "let's take that offline" is the phrase Davin hates most.
• How cognitive diversity matters as much as the visible kind.
• What a neurodiversity tribunal case reveals about being thoughtful, not careful, with language.
• Why mentorship matters at every stage of a career, not just the junior years.
• How the move from technical to managerial roles goes wrong when leaders revert to command-and-control.
• Why curiosity and openness to learning matter more than credentials when hiring junior talent.
• How Davin's role has evolved into developing 170+ people and lifting people out of poverty through data careers.
• Why "Jack of all trades, master of none" is only half the phrase.
• Why a GCSE maths resit needn't define anyone, and the danger of self-limiting beliefs.
• Why Driven by Data Live keeps drawing people who avoid the rest of the conference circuit.

In Episode 20 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Nick Zervoudis, Founder at Value from Data & AI, where they discuss why so many data and AI teams struggle to demonstrate measurable business value — and why the real failure almost always happens upstream, long before anyone tries to articulate it.

Nick makes the case that "we can't prove our value" is usually a symptom, not the disease: teams solve the wrong problem, skip the value case, or hand off value realisation to no one. Along the way they get into his five-point diagnostic framework, how to build a credible back-of-the-envelope ROI estimate before a line of code is written, how to prioritise a portfolio of opportunities, and where AI productivity savings are real versus imaginary.

They also discuss:

• Why the inability to demonstrate value is usually an upstream failure, not a communication problem.
• What Nick's five-point framework reveals: wrong problem, wrong solution, poor execution, no measurement, weak communication.
• Why data teams keep solving the wrong problem by starting from technology instead of the problem itself.
• How to separate the "problem space" from the "solution space" before reaching for a tool.
• Why 70–80% of data teams operate as order takers rather than true collaborators.
• Why being ROI-positive is only the entry ticket, not a reason to do a project.
• What criteria actually decide prioritisation: return, payback speed, implementation readiness, and strategic relevance.
• Why nothing a data team builds has inherent value without an owner on the business side to realise it.
• How to build a credible back-of-the-envelope value case before anything gets built.
• Why estimating value is far easier to learn than the technical craft most data people already have.
• How to get stakeholders to correct a rough estimate rather than hand them a blank sheet.
• Why "how will we measure success?" is the most useful question you can ask when scoping work.
• What the bystander effect has to do with data teams quietly failing to create value.
• How framing work around outcomes turns engineers from code-writers into problem-solvers.
• Why hours saved rarely become money on the balance sheet.
• What the five-to-six buckets of productivity value are, and why you must never double-count them.
• Why some AI investment should deliberately have no business case at all.
• How Monday.com turned a five-week experimentation window into a $100M ARR product.
• Why blanket self-serve analytics or company-wide AI licences often set you up for failure.
• What first steps a CDO should take to re-prioritise a roadmap around measurable value.

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

Welcome to another episode of the Data Debrief, the companion show to Driven by Data: The Podcast, where hosts Catherine Dowden-King and Kyle Winterbottom unpack Tuesday's episode, share what's been on their minds, and explore the realities of leadership, culture, and capability across the data and AI landscape.

This week, Catherine and Kyle reflect on the conversation with Marion Shaw, Senior Director of Data, Analytics and Data Management at Cencora, and her new book on data culture, before digging into why the "data culture" debate keeps circling the same questions, and why the answer almost always comes back to people rather than technology.

They cover:

• The wildfires spreading across South Wales and Europe, and how AI and drones are being deployed in France to spot smoke earlier, distinguish dust from smoke, and cut down the false positives that waste emergency resource
• The bigger climate paradox facing the industry, from record-wet winters followed by hosepipe bans and water mismanagement, to the uncomfortable reality that the data centres powering AI advances are themselves enormous consumers of water
• Why absolutism helps no one, and how the healthiest position on AI, sustainability, and change sits somewhere in the messy middle rather than all-in or all-out
• How incentives quietly shape behaviour, illustrated by the fact that three flights across Europe can cost less than a single train from Manchester to London, and why people ultimately do what they're incentivised to do
• Catherine's latest build for the Orbition community: a NED Opportunity Finder, a live, daily-updating table of listed non-executive director roles showing remuneration, location, and whether the board is public or private, free to access for registered community members, and why she's so keen to see more data leaders move into board positions
• Why Marion's candour hit home, especially the reminder that you simply cannot force people to be interested in or care about data, and why that truth is uncomfortable but essential
• How technology becomes a distraction, the "shiny thing syndrome" that pulls focus away from the outcomes that actually matter, and why that's the real answer to "why now"
• The endlessly debated question of whether "data culture" even exists, why the industry loves arguing over semantics no one outside it cares about, and why every organisation already has a data culture, somewhere on the spectrum from barely-there to full tilt
• Why culture and outcomes feed each other rather than being an either/or, and how influencing behaviours and showing results almost always happen in tandem
• Why every business claims to be "data-driven," how the reality usually differs, and why a new CDO's first 90 to 100 days is really about working out where the organisation actually sits versus where leadership thinks it does
• The "slippery shoulders" problem, and why nothing improves or gets maintained unless someone genuinely owns it
• Why you can't see your own culture from the inside, and how stepping out to network, attend events, and compare notes with peers is often the only way to know whether you're ahead, behind, or better off than you thought
• A look ahead to the Orbition magazine landing in October, featuring a data leader who hasn't spoken publicly in over two years, alongside mentor and mentee stories and perspectives from beyond the CDO community
• The Director of Police AI role at the College of Policing, and how its rigid entry criteria expose the same old problem seen across data leadership: job descriptions that bear little resemblance to what organisations actually want from the role

Kyle's thought of the week: most job descriptions are disconnected from what the business actually needs. Organisations have learned to use the right language, asking for leaders who'll work with the board and use data to drive commercial performance, then listing purely technical requirements underneath. Until that gap closes, the mismatch between what's advertised and what's wanted will keep repeating itself. Fundamentally, every organisation already has a data culture; it simply sits somewhere on a spectrum, and the job is to understand where before trying to move it.

Catherine's thought of the week: you rarely recognise your own culture until you step outside it. Whether it's trust versus micromanagement, or how your data leaders are really perceived, the comparison only becomes clear when you go out, meet people, and see how others operate. And the honesty applies inward too, because no organisation describes itself as not caring about data, so the real work is uncovering where it genuinely stands.

This episode is a candid, wide-ranging conversation on data culture, ownership, and incentives, and a reminder that the hardest problems in data and AI leadership remain stubbornly human, no matter how much the technology moves on.

Housekeeping: The podcast is now broadcasting on LinkedIn Live. To watch along in real time, head to the Driven by Data Productions page on LinkedIn and follow it. We go live with each episode every Tuesday at 1pm BST, and our guest often joins the comments to answer your questions. Keep an eye out for upcoming events towards the end of the year, including Driven by Data Live, where Catherine will be handing out physical copies of the new magazine.

In Episode 19 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is rejoined by Marion Shaw, Senior Director, Data Analytics and Data Management at Cencora, where they discuss why building a genuine data culture is a people and process problem rather than a technology one, and what it actually takes to embed trust, curiosity and business value into how an organisation works with data.

The conversation centres on Marion's new book, Why Data Culture Matters, and digs into why trust is the single attribute with the biggest ripple effect on a data culture, plus the "AI paradox" — why people distrust their own data yet blindly trust the same data when an AI hands it back to them.

They also discuss:

• Why so many organisations pour money into technology yet still fail to see the returns.
• What Why Data Culture Matters covers, and who Marion wrote it for.
• How data culture should reflect and be built around a company's existing business culture.
• Why there is no universal blueprint for data culture — every organisation's version looks different.
• How giving people access to data without teaching them to interpret it undermines self-serve initiatives.
• How trust is built through recognisability, explainability, transparency and repeatability.
• Why CDOs should never promise something they can't actually deliver.
• What the "AI paradox" means for organisations rolling out generative AI tools.
• How younger generations risk losing critical thinking skills by taking AI output at face value.
• Why healthy scepticism should be a core attribute of every data culture, regardless of industry.
• How to measure progress in building a data culture beyond simple adoption metrics.
• Why dashboard adoption is a flawed proxy for a genuinely data-driven culture.
• Why data teams need to shift from being "order takers" to acting as business partners.
• What the biggest mistakes are that organisations make when trying to mandate a data culture from the top down.
• Why flexibility, not rigid planning, is the mindset shift data leaders need most.
• Why influence and communication skills are essential to embedding a data culture successfully.

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

Welcome to another episode of The Data Debrief, the companion show to Driven by Data: The Podcast, where hosts Catherine Dowden-King and Kyle Winterbottom unpack Tuesday's episode, share what's been on their minds, and explore the realities of leadership, culture, and capability across the data and AI landscape.

This week, Catherine and Kyle reflect on Kyle's conversation with Laura Fiacco, Founder of Adaptive Assets, exploring why communication, influence and commerciality remain some of the most overlooked skills in data leadership. Alongside the episode discussion, they dive into LinkedIn's apparent AI U-turn, the growing challenge of AI-generated content, and why the future of data leadership may be less about technology and more about transformation.

They cover:

• Why LinkedIn, Snapchat and Substack are all taking steps to tackle AI-generated content, and what it says about organisations pushing AI adoption before understanding its long-term consequences
• Whether we're heading towards a world where overusing AI becomes just as much of a performance concern as not using it at all
• The ethical grey areas surrounding AI-generated communication, from political statements to funeral tributes, and why context matters far more than blanket rules
• Laura's perspective on communication as a skill that compounds over time, and why nobody starts by presenting to a thousand people
• Why communication isn't synonymous with public speaking, but instead about translating technical concepts into language that business leaders understand and care about
• The importance of influencing without authority, building relationships, and developing the commercial mindset needed to turn technical work into business value
• How organisations continue to undervalue communication, relationship building and commerciality because they're harder to measure than technical capability
• Practical ways data professionals can develop these skills themselves, including writing, voice conversations with AI, and using large language models as personalised coaching tools
• Why curiosity, listening and asking better questions are just as important as being able to communicate confidently
• Kyle's reflections on why transformation is becoming the next major destination for senior data leaders as technology becomes less of the challenge and organisational change becomes the real differentiator
• Catherine's comparison between today's AI leadership roles and the "e-business" and "Chief Internet Officer" titles of the dot-com era, and why AI leadership may ultimately become absorbed into every business function as the technology matures

Programming note: If you've been following Catherine's recent LinkedIn series on the evolution of executive technology roles, this episode expands on that conversation, exploring why today's AI job titles may eventually follow the same path as digital and internet leadership before them.

This episode is a reminder that while AI continues to dominate headlines, long-term success still depends on the fundamentals: communicating clearly, influencing effectively, building relationships, and helping organisations change, not just implement new technology.

In Episode 18 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is joined by Laura Fiacco, Founder of Adaptive Assets, where they discuss why communication, influence and commercial thinking have become the competitive advantage for modern data professionals, and how learning to communicate like an executive is now just as important as technical expertise.

They also discuss:

• Why influence without authority is the defining skill for modern data leaders.
• The 4 quadrant brain framework for tailoring communication to different thinking styles.
• Why successful influence starts by understanding other people's goals before presenting your own ideas.
• Why relationship building happens outside the meeting room, not inside it.
• How to communicate technical work in a way executives immediately care about.
• Why every presentation should start with the business outcome, not the analysis.
• Why data professionals often answer questions that were never asked.
• What data leaders can learn from sales discovery.
• The questions every data professional should ask before proposing a solution.
• Why understanding failed attempts and hidden assumptions leads to better stakeholder conversations.
• How to influence when different executives have conflicting priorities.
• Why peer success stories influence behaviour more effectively than data alone.
• Why communication, influence and leadership should become measurable career progression criteria.
• Practical habits that help build influence, trust and credibility every day.
• Why AI will make communication, influence and judgement more valuable than technical expertise.
• Why every person needs to understand their specific role in delivering the wider business objective.
• Why every business strategy is built on assumptions that data should validate or challenge.
• How to align stakeholders when everyone interprets the same strategy differently.

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.