Driven by Data: The Podcast

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.

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/

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.

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/

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.

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.

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.

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/

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.

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.