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Data & AI Mastery

Cambridge Spark

In the age of rapid technological change, how can you harness the power of data and AI to transform your business?

Welcome to Data & AI Mastery, the podcast where cutting-edge insights meet practical strategies for success.

Hosted by Dr Raoul-Gabriel Urma, founder of Cambridge Spark, this show dives deep into how leading organisations across the globe are using data & AI to revolutionise operations, streamline efficiency, and drive innovation.

Each episode features conversations with senior leaders, revealing their career stories and real-world case studies and actionable takeaways that you can apply, whether you're climbing the career ladder or already in the C-suite.

From AI-driven solutions to practical tips for navigating your data transformation journey, Data & AI Mastery will equip you with the tools to thrive in the AI era.

Stay ahead, stay inspired, and unlock your potential with Data & AI Mastery: your ultimate guide to mastering data and AI for business.

This feed is also home to Inside the Algorithm, our sister show hosted by Chief AI Officer Dr Jeremy Bradley, featuring in-depth conversations with the researchers and technical experts working at the frontier of artificial intelligence. New episodes from both shows drop fortnightly, on alternate weeks.

Follow now so you never miss an episode from either show. 🎙️

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  • 25 episodes
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  • Avg 30 min
  • English

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  • S1 · E48
    October 28 · 42 min

    From Six Months to Three Hours: How AI Has Shrunk the Attack Window

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: [cambridgespark.com](https://www.cambridgespark.com/?utm_campaign=6955829-Podcast%202024%20-%20Onwards&utm_source=Podcast&utm_medium=RSS) Attackers now exploit a newly public vulnerability in under three hours. Six months used to be normal. Your developers are shipping code with Claude Code and Codex. Your security teams were trained to look for error messages, and in some companies the AI security budget now sits with a chief AI officer rather than the CISO. Ashish Rajan is CISO at TechRiot and author of AI Security Engineering, published by Wiley. He hosts the Cloud Security Podcast and the AI Security Podcast, and advises Fortune 500 companies on security. Ashish gives you a practical way to decide which AI risks to accept, where to focus first, and what skills your security teams need next. You'll hear why the confidentiality, integrity and availability principles still hold. You'll also hear why a vibe-coded tool needs the same security thinking as any application once it touches production data. This episode is for CISOs, CIOs, Chief Data and AI Officers, and the L&D leaders responsible for workforce upskilling during AI transformation. It suits anyone who wants an honest read on enterprise AI security from someone working inside it. If you want a vendor shortlist, this one isn't for you. ## Key Takeaways - An agent asking for a password throws no error code. Ashish explains why judging intent in plain English breaks the traditional security playbook. - A bank with 400+ applications can't secure them all on day one. Ashish shares how to pick the crown jewels and where compensating controls fit. - Building your own security tool in two hours with Claude Code is easy. Maintaining it after the builder leaves for a better offer is the cost few teams plan for. - A framework like NIST shows you have the basics covered. Ashish argues boards should ask their CISO a harder question: could you patch a critical flaw in under three hours? ## Chapter Markers 00:00 The three-hour exploit window 01:13 Ashish's start: a hacked Hotmail account 03:08 Why he wrote AI Security Engineering 05:28 Rewriting the book when agents arrived 07:02 Launching the AI Security Lab 08:08 First principles versus new AI risks 12:48 Who owns AI security 17:29 Advice for CEOs: protect the crown jewels 20:21 Workforce upskilling and build versus buy 23:52 Cross-functional security teams and harnesses 28:52 Personal apps versus enterprise tools 30:14 Offensive AI and the agentic SOC 33:39 Why frontier red-team models need a harness 35:57 From six months to three hours 37:00 Reasons for hope 37:34 The wake-up call that came and went 38:27 What boards should ask their CISO 41:28 Wrap-up ## Useful Links & Resources - AI Security Engineering by Ashish Rajan (Wiley) - Cloud Security Podcast and AI Security Podcast, hosted by Ashish Rajan - Cambridge Spark data and AI training: https://www.cambridgespark.com - Dr. Raoul-Gabriel Urma on LinkedIn: https://uk.linkedin.com/in/raoulurma ## Connect With the Show - LinkedIn: https://www.linkedin.com/school/cambridge-spark/ - Website: https://www.cambridgespark.com Who owns AI security in your organisation: the CISO, the CIO or a Chief AI Officer? Tell us in the comments. If your team has built its own security harness or tackled agent risk head on, we'd like to hear how it went. Visit cambridgespark.com to transform your workforce with cutting-edge data and AI training. Subscribe so you never miss an episode. #DataAndAIMastery #AISecurity #EnterpriseAI #CyberSecurity #CambridgeSpark

  • S2 · E7
    September 30 · 24 min

    From Prototype to Production: The Best of Inside the Algorithm

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com In this special compilation episode of Inside the Algorithm, host Jeremy, Chief AI Scientist at Cambridge Spark, brings together the best moments from a season of conversations with leading academics, researchers and practitioners. Guests from aviation forecasting, manufacturing, NHS health modelling and neuro-symbolic AI research take on the same core questions. Hearing them side by side shows how experts from very different fields approach the challenge of taking AI from theory to practice. The episode covers why so many AI projects stall at the prototype stage and where models break when they meet something they've never seen. It also covers how trust is built in systems that can feel like a black box, why the strongest results come from hybrid systems, and what experience still gives you as AI tools take on more of the work. Key Takeaways Production readiness means handling edge cases, unforeseen scenarios and adversarial attacks. It also means securing architecture sign-off and making sure outputs reach the people who need them. Projects succeed when teams understand how a solution will be used and what "done" looks like, and when they have champions inside the client's business. LLMs learn correlations, not the world. Their reliance on text patterns explains both their surprising capability and their strangest hallucinations. Historical data has limits. Events like the pandemic can break the relationships forecasting models depend on almost overnight. Stress testing, what-if scenario planning and human domain expertise are essential safeguards. Trust is built through involvement. Participative modelling earned deep trust with NHS clients in the Midlands, and transferring that trust to new regions proved harder than rebuilding the model itself. Hybrid systems deliver the strongest results. Pairing language models with solvers, simulations and small, fine-tuned specialist models lets each part do the job it's best at. Simplicity and production-first thinking win. A working model in production beats a more accurate one that takes months to build. Explainable approaches often serve clients better. Juniors need the chance to learn the trade. Guests warn about "rubber-stamping" AI-generated code and encourage newcomers to look beyond the hype to the wider history of AI. Useful Links & Resources Inside the Algorithm on the Data and AI Mastery podcast feed Inside the Algorithm episodes on the Cambridge Spark YouTube channel Cambridge Spark: cambridgespark.com Connect With the Show Cambridge Spark LinkedIn: https://www.linkedin.com/school/cambridge-spark/ Cambridge Spark Instagram: https://www.instagram.com/cambridgespark/ Cambridge Spark X: https://x.com/CambridgeSpark Host Jeremy Bradley on LinkedIn: https://www.linkedin.com/in/jeremy-bradley/ Which of these clips landed hardest for you: the prototype trap, the pandemic story, or the case for pairing LLMs with solvers? Tell us in the comments, and let us know which guest you'd like to hear a full episode with again. Visit cambridgespark.com to learn how Cambridge Spark can upskill your workforce in data and AI. #InsideTheAlgorithm #EnterpriseAI #DataEngineering #MachineLearning #AIDeployment

  • S1 · E47
    September 16 · 24 min

    No Get Out of Jail Free Card: Five Data Leaders on AI in Regulated Industries

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com In financial services, getting AI wrong doesn't just mean a bad headline. It means real customers losing real money at the moments that matter most. If you're trying to balance AI innovation with regulatory scrutiny, you already know the tension: move too fast and risk a compliance failure, move too slow and watch competitors who don't hesitate pull ahead. Edmund Towers leads Advanced Analytics & Data Science at the Financial Conduct Authority, shaping the regulator's approach to AI, financial crime detection and consumer protection. Before joining the FCA, he delivered financial services transformation programmes as a manager in Accenture's Financial Services Change Practice. In this compilation episode, he's joined by Jessica Rusu, the FCA's Chief Data, Information and Intelligence Officer, alongside senior data leaders from Santander UK, Aviva and TalkTalk. You'll hear how the FCA thinks about AI governance without resorting to tick-box regulation, and how banks and insurers turn that clarity into production systems. Sarah Self at Aviva explains how a summarisation tool cut claims hold times by more than 50%. Kevin Cassar walks through building an AI triage system in health insurance that improved both customer outcomes and operational efficiency. This episode covers the FCA's AI Live Testing programme, the Consumer Duty as the policy anchor for responsible AI, and how Santander UK's Luke Pearce secured C-suite sponsorship to move faster on generative AI. It's built for data and AI leaders in regulated industries who need to make the business case for AI while keeping compliance onside, and for anyone who assumes regulation and innovation have to be at odds. Key Takeaways Edmund Towers explains why the FCA's AI Live Testing lets firms trial live products under supervision, and why it's built on guidance rather than a tick-box checklist. The Consumer Duty already covers responsible AI, according to Towers, and blaming a "black box" model won't protect a firm from accountability under the Senior Managers Regime. Sarah Self's team at Aviva cut claims-handler hold times by more than 50% with a summarisation tool, and built a medical underwriting system running at 99% accuracy. Kevin Cassar reveals the co-sponsorship model, COO paired with CDO, that got a health insurance triage system built with risk and business teams involved from day one. Chapter Markers 00:00 Why regulated industries need a different AI conversation 02:07 The FCA's role in AI adoption across financial services 04:10 Inside the FCA's AI Lab and Live Testing programme 07:49 Why Consumer Duty rules already cover responsible AI 10:37 Santander's shift from risk-averse to AI-enabled 13:05 Aviva's claim summarisation tool cuts hold times in half 16:07 Building an AI triage system in health insurance 18:39 Getting COO and CDO co-sponsorship for AI projects 20:03 The FCA's fight against fraud, scams and money laundering 23:41 Closing thoughts: compliance and innovation as one agenda Useful Links Follow Dr Raoul-Gabriel Urma on LinkedIn: https://uk.linkedin.com/in/raoulurma Visit the Cambridge Spark Website: https://cambridgespark.com/

  • S2 · E6
    September 2 · 31 min

    DAIM: Inside The Algorithm | Divya Kesavan on AI Transformation in Banking: Beyond RPA and Automation

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com For years, digital transformation and AI transformation have been used almost interchangeably. Divya Kesavan, Head of Data Science for Business and Commercial Banking at Lloyds Banking Group, explains why that conflation breaks down in practice. In this episode, Divya walks Jeremy through the shift from rule-based RPA to reasoning-driven AI and why Lloyds' enterprise AI blueprint rests on three layers: reasoning, context and control. She unpacks what it actually takes to move an agentic system from a working proof of concept to a tool trusted with real banking decisions, including a striking example of an AI agent that went badly off script. The conversation also covers governance by design in a heavily regulated environment, confidence scoring as a practical risk tool, and why agile alone cannot carry the weight of AI-led change. A grounded, experience-based look at what transformation really requires. Follow Data & AI Mastery for more conversations with the people building AI inside major enterprises. If you enjoyed this conversation, check out this special episode of Data & AI Mastery where Raoul sits down with senior leaders from Lloyds to learn more about how the team is reimagining banking through AI-powered experiences: Apple: https://podcasts.apple.com/gb/podcast/reimagining-lloyds-banking-group-ai-transformation/id1779783413?i=1000701688423 Spotify: https://open.spotify.com/episode/06ZBLoyvemQEVftCuWytVe?si=726cf7a2d20b4b22 YouTube: https://www.youtube.com/watch?v=U2krteWX89Q Glossary Terms RPA (Robotic Process Automation): software that mimics human actions on a screen to automate repetitive, rules-based tasks. RAG (Retrieval-Augmented Generation): a method that grounds an AI model's responses in real, relevant data retrieved at the time of the request, rather than relying solely on what the model learned during training. Human-in-the-loop: a safeguard where a person reviews or approves an AI system's output before it's acted on, typically when confidence is low or the decision carries risk. Confidence scoring: a technique used to measure how certain an AI system is in its output. Outputs above a set threshold proceed automatically, while lower-confidence results are flagged for human review. Chapter Markers: (00:00) - Cold open: the case for AI governance (00:39) - Introduction: Divya Kesavan, Lloyds Banking Group (02:23) - Where automation stops and AI begins (03:39) - Why traditional RPA breaks under ambiguity (05:04) - From RPA blueprint to AI-first processes (06:47) - Building agentic reasoning: the real technical challenge (09:29) - The rogue agent story and why permissions matter (12:03) - Lloyds' enterprise AI blueprint: reasoning, context, control (14:45) - Reliability as the hardest problem to solve (16:34) - Designing and testing AI in a regulated environment (19:25) - Embedding responsible AI thinking across the team (22:54) - Confidence scoring and managing risk in practice (24:34) - Common misconceptions about AI versus automation (26:18) - Balancing probabilistic AI with deterministic workflows (27:39) - Advice for data science leaders starting out (30:04) - The one idea to take away Useful Links: Connect with Divya Kesavan on LinkedIn: https://uk.linkedin.com/in/divya-kesavan-54800b3 For more AI insights follow Jeremy on LinkedIn: https://uk.linkedin.com/in/jeremy-bradley Explore Cambridge Spark’s AI upskilling programmes at https://www.cambridgespark.com

  • S1 · E46
    August 19 · 30 min

    BlackRock CIO Simona Paravani-Mellinghoff on Data, AI and Financial Inclusion

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com In this episode of Data & AI Mastery, host Dr Raoul-Gabriel Urma is joined by Simona Paravani-Mellinghoff, Chief Investment Officer at BlackRock, to explore why data quality and accessibility sit at the heart of every serious AI strategy. Simona explains what good data infrastructure looks like when overseeing hundreds of billions in mandates and why relevant data, not just volume, drives better investment insights. She shares her Ferrari and Fiat Punto analogy for deciding when to use large generative AI models versus smaller, specialised ones and outlines a three-layer skill pyramid, prompting, critical thinking and creative thinking, that leaders and their teams will need to thrive in an AI-driven world. The conversation also covers AI's role in expanding financial inclusion, why Simona funds scholarships and teaches at Cambridge, and her advice for anyone starting a career in data or finance today. If you enjoyed this episode, follow Data & AI Mastery and share it with a colleague thinking through their own data and AI strategy. Chapter Markers (00:00) - Introduction: no data, no AI party (01:16) - Simona's journey from Italy to global CIO (03:40) - What academia and industry learn from each other (05:21) - Building good data infrastructure at scale (08:56) - Frontier models vs specialised models: the Ferrari and Fiat Punto analogy (11:42) - Why education and scholarships matter to Simona (15:03) - The three-layer skill pyramid for the AI era (17:19) - Will AI threaten jobs or create new ones (21:04) - AI's role in advancing financial inclusion (23:22) - Advice for the next generation (24:06) - Quickfire round (27:22) - Closing thoughts and key takeaways Useful Links Connect with Simona Paravani-Mellinghoff on LinkedIn: https://uk.linkedin.com/in/simona-paravani-cfa-5129524 Follow Dr Raoul-Gabriel Urma on LinkedIn: https://uk.linkedin.com/in/raoulurma Visit the Cambridge Spark Website: https://cambridgespark.com/

  • S2 · E5
    August 5 · 31 min

    DAIM: Inside The Algorithm | Alberto Romero on Engineering AI at scale at Aviva

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com What does it actually take to ship machine learning inside one of the UK's largest insurers? Jeremy Bradley sits down with Alberto Romero, director of AI engineering at Aviva, to trace his path from InsurTech founder to enterprise AI leader. Alberto explains why prototypes are so often mistaken for finished products and what production readiness really demands once edge cases, drift and adversarial behaviour enter the picture. The conversation covers how to get genuine explainability out of large language models rather than plausible-sounding justification, when fine-tuning earns its place in a regulated stack, and why Aviva built its own internal platform to govern AI use cases at scale. Alberto also shares his take on fraud detection as an adversarial ML problem and the one failure mode he sees engineering teams repeat most often. Follow Data & AI Mastery so you never miss an episode, and share it with a colleague working through similar production challenges. If you enjoyed this conversation, you might also like this episode featuring Sarah Self. She joined us on Data and AI Mastery to explore what most organisations get wrong when deploying AI. Apple: https://podcasts.apple.com/gb/podcast/from-cybersecurity-to-ai-director-sarah-self-on-leading/id1779783413?i=1000764247007 Spotify: https://open.spotify.com/episode/0BpSq5X1ZP8ctIYTxWVAJT?si=1264586e79f3446f YouTube: https://www.youtube.com/watch?v=2jgM095SYG0 Glossary Terms RAG: Retrieval-Augmented Generation is an AI methodology that enhances Large Language Models by pulling factual context from external knowledge bases. GAN: Generative Adversarial Network is a deep learning architecture in which two neural networks compete against each other to create highly realistic synthetic data from a training dataset Non-deterministic: describes a process, algorithm, or system whose outcome is inherently unpredictable and cannot be guaranteed to repeat exactly, even when it starts from the exact same initial conditions ReAct (Reasoning + Acting) approach: a prompting technique that enables AI models to solve complex problems by alternating between thinking and taking action Chapter Markers (00:00) - Cold open: why prototypes get mistaken for production (02:53) - Avoiding common AI adoption pitfalls in regulated sectors (05:44) - Real explainability versus post-hoc justification in LLMs (09:18) - From startup founder to enterprise: the mindset shift (11:38) - Managing AI across 70+ use cases at Aviva (13:31) - Standards first, technology second (17:19) - Where fine-tuning earns its place (20:33) - Building Aviva's own governed AI platform (23:51) - Fraud detection as an adversarial ML problem (28:34) - Quick fire: the most common AI failure mode (29:37) - What deserves more attention as AI scales Useful Links Connect with Alberto Romero on LinkedIn: https://uk.linkedin.com/in/albertoromero-uk For more AI insights follow Jeremy on LinkedIn: https://uk.linkedin.com/in/jeremy-bradley Explore Cambridge Spark’s AI upskilling programmes at https://www.cambridgespark.com

  • S1 · E45
    July 22 · 31 min

    Fast Enough to Matter, Careful Enough to Trust: How Leaders Are Governing AI

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com There's a real difference between an organisation experimenting with AI and one that has genuinely thought through what it means for its strategy, its accountability, and the people it serves. That gap is where a lot of the frustration lives. In this episode, we bring together some of the sharpest thinking from across the show on AI strategy and governance: the decisions that happen before AI gets deployed and the frameworks that make sure it keeps working once it does. You'll hear from Luke Pearce, Chief Data Officer at Santander UK, on presenting AI governance at the board level. From Miryem Salah at VodafoneThree, on building an AI strategy from first principles. And from senior leaders at Aviva, Oxford Saïd Business School, and Intact Insurance, all grappling with the same fundamental question: how do you move fast enough to stay relevant, but carefully enough to stay trusted? Follow Data and AI Mastery to stay ahead of the conversations shaping the future of data and AI. These are just the highlights. Find links to every full episode below. Miryem Salah: Apple - Spotify - YouTube Sarah Self: Apple - Spotify - YouTube Luke Pearce: Apple - Spotify - YouTube Mark Bramwell: Apple - Spotify - YouTube Indhira Mani: Apple - Spotify - YouTube Conny Ploth: Apple - Spotify - YouTube Nick Edwards: Apple - Spotify - YouTube Chapter Markers (00:00) Episode Introduction (02:01) Miryem Salah, VodafoneThree: building an AI strategy from first principles (05:40) Sarah Self, Aviva: why guardrails and customer outcomes are not a trade-off (09:47) Miryem Salah, VodafoneThree: a governed, agile approach to change (12:28) Luke Pearce, Santander UK: moving the board past fear of hallucination (16:22) Mark Bramwell, Oxford Saïd Business School: a portfolio approach to AI tools (20:13) Luke Pearce, Santander UK: a domain-led approach to use cases (23:44) Indhira Mani, Intact Insurance: what success looks like a year from now (27:24) Conny Ploth: why you can't manage what you don't measure (29:53) Nick Edwards, The AA: curated, not controlled (30:53) Closing reflections Useful Links Follow Dr Raoul-Gabriel Urma on LinkedIn: https://uk.linkedin.com/in/raoulurma Visit the Cambridge Spark Website: https://cambridgespark.com/

  • S2 · E4
    July 8 · 43 min

    DAIM: Inside The Algorithm | Why Neurosymbolic AI Solves What Scaling Alone Cannot with Dr Vaishak Belle

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com Large language models are surprisingly good at producing fluent, plausible text. So why do they still confidently get simple things wrong? In this episode, Dr Jeremy Bradley is joined by Dr Vaishak Belle, Reader at the University of Edinburgh's School of Informatics, Alan Turing Institute Faculty Fellow and Director of Research and Innovation at the Bayes Centre. Vaishak has spent 16 years working at the intersection of logic, probability and machine learning and brings that lens to one of AI's most persistent problems: hallucination. The conversation traces why scaling alone will not solve reliability, what neurosymbolic AI actually is and why tools like Claude Code quietly depend on it, how theory of mind is being engineered into language models, and where reinforcement learning fits into the future of AI reasoning. If you work at the frontier of AI research or engineering, this is a grounded, technically rich conversation worth your time. Follow Data & AI Mastery so you never miss an episode. If you enjoyed this conversation, you might also like this episode featuring Dr Petar Veličković. Petar joined us on Data and AI Mastery to explore how graph neural networks bring structured reasoning into systems like Google Maps and how AI is being used as a genuine discovery partner in mathematics. Apple: https://podcasts.apple.com/gb/podcast/bridging-ai-research-and-real-world-impact-dr-petar/id1779783413?i=1000734000576 Spotify: https://open.spotify.com/episode/7qA0AY9MlLS2L9PANqlXNi?si=37d8a8fb43c14cc9 YouTube: https://www.youtube.com/watch?v=GwMUSNidnvE Glossary Terms Neurosymbolic AI: an emerging field that merges the intuitive pattern recognition of neural networks with the logical, rule-based reasoning of symbolic AI. Theory of Mind: refers to an AI’s capacity to attribute mental states to humans or other agents and understand that these states may differ from its own. Confabulation: In AI, it is the generation of factually incorrect, distorted, or entirely fabricated information presented as absolute truth. Delegation Module: a software component that allows users or systems to assign tasks, roles, or access rights to others. Retrieval Augmented Graphs: an advanced AI framework that enhances large language models by grounding their responses in interconnected data networks, such as knowledge graphs. Reinforcement Learning: a machine learning method where an AI agent learns to make decisions through trial and error. Dynamic Pathway Analysis: a computational method used in systems biology and bioinformatics to model and simulate how biological processes change over time. Chapter Markers (00:00) - Why LLM hallucinations happen (02:12) - The biggest shift in AI over the last 16 years (07:42) - How logic and probability shaped Vaishak's path into AI (10:11) - Introducing neurosymbolic AI (11:27) - Claude Code, algebraic delegation and the theory of mind problem (20:01) - Theory of mind in robotics and human computer interaction (21:54) - Confabulation versus hallucination (26:42) - Why AI errors are not the same as human dishonesty (27:24) - Reinforcement learning, reward signals and learned behaviour (32:36) - Syntax checks and the engineering behind reliable code (37:52) - What happens to the software engineer's role (40:49) - Advice for early career AI thinkers Useful Links Connect with Dr Vaishak Belle on LinkedIn: https://uk.linkedin.com/in/vaishakbelle Learn more about Vaishak’s work here: https://www.vaishakbelle.org/ Read Vaishak's report on The Future of Neuro-Symbolic AI: https://ojs.aaai.org/index.php/AAAI/article/view/42130 For more AI insights follow Jeremy on LinkedIn: https://uk.linkedin.com/in/jeremy-bradley Explore Cambridge Spark’s AI upskilling programmes at https://www.cambridgespark.com

  • S1 · E44
    June 24 · 22 min

    The AI Skills Gap: What It Really Takes to Bring a Workforce on the Journey

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com The technology is the easy part. Getting people ready for it, that's the hard part. In this special compilation episode, Dr Raoul-Gabriel Urma steps back from the individual conversations and zooms out to one of the most persistent themes across the show in 2026: AI skills and literacy. Because no matter the industry, the company size, or the stage of the AI journey, the same challenge keeps surfacing. How do you get an entire workforce genuinely ready? You'll hear from Jessica Rusu, Chief Data, Information and Intelligence Officer at the FCA, on building T-shaped skills for an uncertain future. From Nick Edwards and Harj Johal at the AA on the combination of tooling, training, and space to practise that has driven rapid, organisation-wide progress. From Sarah Self at Aviva on why democratising AI is as much a cultural imperative as a commercial one. And from senior data and AI leaders at Santander UK, Oxford Saïd Business School, and TransUnion on curiosity, fear, and what genuinely high-performing teams look like in an era of AI. A compelling, cross-industry episode for anyone thinking about the people side of AI transformation. These, however, are just the highlights. Find links to every full episode below: Jessica Rusu: Apple - Spotify - YouTube Nick Edwards & Harj Johal: Apple - Spotify - YouTube Sarah Self: Apple - Spotify - YouTube Mark Bramwell: Apple - Spotify - YouTube Luke Pearce: Apple - Spotify - YouTube Kevin Cassar: Apple - Spotify - YouTube Conny Ploth: Apple - Spotify - YouTube Sudhish Mohan: Apple - Spotify - YouTube Follow Data and AI Mastery to stay ahead of the conversations shaping the future of data and AI. Chapter Markers (00:00) Introduction and welcome (01:31) Harj Johal, The AA: curiosity as the defining skill for AI adoption (03:14) Nick Edwards, The AA: tooling, upskilling and watching the water level rise (05:05) Sarah Self, Aviva: democratising AI and the case for broad access (08:01) Mark Bramwell, Oxford Saïd Business School: leading through uncertainty and creating a culture of FOMO (10:03) Luke Pearce, Santander UK: removing fear and augmenting expertise (11:14) Jessica Rusu, FCA: T-shaped skills and building flexibility for the future (13:31) Kevin Cassar, TalkTalk: the value of continued learning and the apprenticeship experience (16:13) Conny Ploth: reimagining upskilling for the 21st century (19:14) Sudhish Mohan, TransUnion: curiosity over credentials (21:47) Closing reflections Useful Links Follow Dr Raoul-Gabriel Urma on LinkedIn: https://uk.linkedin.com/in/raoulurma Visit the Cambridge Spark Website: https://cambridgespark.com/

  • S1 · E43
    June 10 · 31 min

    From FOMO to Strategy: How Haleon's CDAO Richard Moule Is Educating Leaders on AI

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com In this episode, Richard Moule, Chief Data and AI Officer at Haleon, joins Dr Raoul-Gabriel Urma to discuss one of the most pressing challenges facing large organisations today: how do you build genuine AI literacy across an entire workforce, starting at the very top? Richard shares how Haleon partnered with Cambridge Spark to deliver an in-depth AI education programme to its executive team, why the CEO set a clear ambition to lead the best-educated leadership team in the FTSE 100 on AI, and how that investment is reshaping the quality of business conversations across the organisation. The conversation covers the difference between agentic AI and generative AI, why Richard pushes back on teams that ask for specific technologies before defining their business need, and how Haleon is balancing foundational data infrastructure with the pressure to show early value. Richard also shares his approach to AI governance, the limits of automation when human negotiation sits at the heart of a process, and what he looks for in talent in an era of AI augmentation. A sharp, practical episode for senior data and AI leaders navigating enterprise-scale transformation. Follow Data and AI Mastery to stay ahead of the conversations shaping the future of data and AI. If this episode sparked your interest in how AI is being applied inside Haleon, then check out the conversation Dr Jeremy Bradley had with Dr Gueorgui Mihaylov, Principal Data Scientist at Haleon, on Inside the Algorithm: Apple: https://podcasts.apple.com/gb/podcast/daim-inside-the-algorithm-ai-in-industrial/id1779783413?i=1000768691684 Spotify: https://open.spotify.com/episode/3zJJxysXLBJcMi336RNmDZ?si=cca6acb90b7b4e0b YouTube: https://www.youtube.com/watch?v=Hz1OtBYMf0U Chapter Markers (00:00) - Introduction and welcome (04:30) - Agentic AI, generative AI and why they are part of the same continuum (08:27) - Building an enterprise-wide AI education programme with Cambridge Spark (11:17) - AI strategy, tech infrastructure and the foundational work most businesses underestimate (16:12) - Balancing governance with speed: raising the floor and raising the ceiling (20:32) - Process mapping, automation ROI and where agentic AI delivers real value (22:09) - Where AI is not yet ready: processes built on human negotiation (24:50) - Quickfire round: hiring for augmentation, staying current, career advice and personal interests (29:17) - Closing reflections Useful Links Follow Dr Raoul-Gabriel Urma on LinkedIn: https://uk.linkedin.com/in/raoulurma Connect with Richard Moule on LinkedIn: https://www.linkedin.com/in/richard-moule/ Visit the Cambridge Spark Website: https://cambridgespark.com/

  • S2 · E3
    June 3 · 34 min

    DAIM: Inside The Algorithm | Network Forecasting & Data Science Leadership with Dr Judit Guimera Busquets

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com This week, Dr Judit Guimera Busquets, Head of Data Science at Datasparq, joins Dr Jeremy Bradley to trace the journey from her PhD on air traffic network forecasting through to leading data science teams delivering real-world AI projects. Judit explains why forecasting inside a complex network is fundamentally different from standard demand prediction: when a single airport pair is removed, the cascade effect ripples across an entire system. She walks through the multi-stage modelling framework she developed, covering city pair demand generation, network evolution, itinerary assignment, and long-term scenario planning. The conversation then turns to what actually happens when structural shocks like a pandemic break a model's core assumptions and why human-in-the-loop design is not optional. Judit also sets out what she looks for in data scientists: pragmatism over perfection, simplicity over complexity, and a production-first mindset from day one. She closes with her view on where applied AI is heading, including the rise of small, fine-tuned specialist models and why AI governance remains the most overlooked challenge in the field. Follow Data & AI Mastery on Apple Podcasts, Spotify, or YouTube to stay ahead of the algorithm. If you enjoyed this episode, why not check out the Data & AI Mastery episode with Richard Masters, VP of Data and AI at Virgin Atlantic. You will learn more about how the airline leverages AI and data-driven strategies to enhance operations, optimise pricing, and deliver premium customer experiences: Apple: https://podcasts.apple.com/gb/podcast/mastering-data-ai-insights-from-virgin-atlantics-vp/id1779783413?i=1000697801419 Spotify: https://open.spotify.com/episode/1MY3AZCvDr5eS1HlfBudHy?si=ca5ec9b2fe6d44b1 YouTube: https://www.youtube.com/watch?v=DQ3mTwTzJvA Glossary Terms Hub-and-spoke Model: a centralised organisational architecture where a central core connects to multiple peripheral nodes. Traffic, communication, or inventory flows through the hub rather than directly between spokes. Network Theory: a multidisciplinary framework used to analyse complex systems by representing them as mathematical graphs Econometrics: the application of statistical and mathematical models to economic data Human-in-the-Loop: a collaborative AI approach where humans actively participate in an automated system's training, refinement, or operation. Linear Regression Model: a fundamental statistical and machine learning algorithm that models the relationship between a dependent variable and one or more independent variables by fitting a straight line to the data. Chapter Markers (00:00) - What makes network forecasting different from standard demand prediction (05:54) - How historical data fails when the network itself evolves (10:05) - Modelling link addition and removal as classification problems (13:26) - Designing for medium and long-term policy evaluation, not daily operations (17:57) - What happens to a model when a structural shock like a pandemic hits (22:22) - Human-in-the-loop: adjusting elasticities and running what-if scenarios (27:20) - What great data scientists actually look like in a consulting environment (30:03) - Getting stakeholders to use AI: champions, end users and change readiness (32:00) - Where applied AI is heading: small specialist models and the governance gap Useful Links Connect with Dr Judit Guimera Busquets on LinkedIn: https://uk.linkedin.com/in/judit-guimera-busquets-696ab74a Learn more about Judit’s PHD here: https://openaccess.city.ac.uk/id/eprint/24689/1/Busquets%2C%20Guimera.pdf For more AI insights follow Jeremy on LinkedIn: https://uk.linkedin.com/in/jeremy-bradley Explore Cambridge Spark’s AI upskilling programmes at https://www.cambridgespark.com

  • S1 · E42
    May 27 · 37 min

    Governing AI in Financial Services: Inside the FCA with Jessica Rusu, Chief Data, Information & Intelligence Officer

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com In this special episode of Data & AI Mastery Dr Raoul-Gabriel Urma goes inside the Financial Conduct Authority to sit down with Jessica Rusu, Chief Data, Information and Intelligence Officer at one of the most influential regulatory bodies in the world. The FCA oversees the conduct of more than 50,000 firms across UK financial services. Jessica sits at the centre of a question that every financial institution is grappling with right now: how do you move fast with AI while remaining genuinely accountable for outcomes? In this episode, Jessica shares how the FCA's digital and regulatory sandboxes are cutting fintech development timelines from a year to three months, why the FCA has deliberately avoided writing new AI-specific rules, and what firms consistently underestimate when deploying AI at scale. She also shares a practical framework for building a future-ready career in data and AI, drawing on her own journey from Ford Motor Company to eBay to the heart of UK financial regulation. If you work in data, technology, or strategy inside a regulated industry, this is essential listening. Be sure to follow the show wherever you get your podcasts to never miss an episode. Enjoyed this conversation? Why not tune in to our episode with Edmund Towers, Head of Advanced Analytics & Data Science Units at the FCA: Apple: https://podcasts.apple.com/gb/podcast/ai-regulation-and-trust-using-data-science-to-protect/id1779783413?i=1000750268699 Spotify: https://open.spotify.com/episode/3JD7g1B47xI8JJ71ROF1Vp?si=00dc012933554b40 YouTube: https://www.youtube.com/watch?v=379oyRKg-eE Chapter Markers (00:00) - Introduction and what responsible AI really means (02:26) - Jessica's career journey from Ford to the FCA (08:42) - Inside the FCA's regulatory and digital sandboxes (14:40) - Scaling AI responsibly and what doing it badly looks like (19:18) - Why the FCA chose not to write new AI-specific rules (24:57) - Accelerators and blockers of AI adoption in fintech (31:37) - The T-shaped skills framework for future careers (35:04) - Key takeaways Useful Links Follow Dr Raoul-Gabriel Urma on LinkedIn: https://uk.linkedin.com/in/raoulurma Connect with Jessica Rusu on LinkedIn: https://uk.linkedin.com/in/jessica-rusu-189134a Visit the Cambridge Spark Website: https://cambridgespark.com/

  • S2 · E2
    May 20 · 39 min

    DAIM: Inside The Algorithm | AI in Industrial Manufacturing: Inventory Optimisation and Real-Time Process Control

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com In this episode of Inside the Algorithm, Dr Jeremy Bradley sits down with Dr Gueorgui Mihaylov, Principal Data Scientist at Haleon and Visiting Research Fellow at King's College London, to explore what happens when rigorous mathematics meets the messy reality of a global consumer health supply chain. Gueorgui walks through two of the most technically ambitious AI projects running inside Haleon today. First, the AI Inventory Planner, an ensemble of machine learning models, survival analysis, and stochastic simulation designed to reduce inventory holdings by double digits while maintaining or improving service levels. Second, the Golden Batch project, an attempt to bring real-time process control to pharmaceutical manufacturing using data-driven corridor modelling of a highly non-stationary, non-Newtonian fluid system. The conversation covers global forecasting algorithms, Weibull-based delivery modelling, principal component analysis for process visualisation, and the long road from working prototype to enterprise adoption. Follow Data & AI Mastery so you never miss a future episode of Inside The Algorithm. If you want to learn more about how AI is revolutionising demand forecasting and supply chain optimisation, listen to the Data & AI Mastery episode between host Dr Raoul Gabriel Urma and Peter Laflin, Director of Data and Analytics at Morrisons: Apple: https://podcasts.apple.com/gb/podcast/bringing-art-to-science-using-ai-to-build-customer/id1779783413?i=1000677504555 Spotify: https://open.spotify.com/episode/6HcwcdIGOguBZp1vADE1Z0 YouTube: https://www.youtube.com/watch?v=NKdjiYVC9H8 Glossary Terms: Inventory Management: The systematic process of ordering, storing, tracking, and controlling a company’s goods to ensure the right items are available in the right quantity, place, and time. Stochastic Phenomena: Events, processes, or systems that are inherently random, unpredictable, or probabilistic in nature, rather than deterministic. Demand Forecasting: The process of predicting future customer demand for products or services using historical sales data, market trends, and analytical methods. Out-of-sample bootstrapping: A resampling technique used to estimate a model's performance on unseen data by generating multiple datasets through sampling with replacement from the original data. Non-Newtonian Fluid: A substance whose viscosity changes when subjected to stress, agitation, or shear force, rather than remaining constant. IoT Device: A physical object embedded with technology, software, and sensors that allow it to connect, collect, and exchange data over the internet or networks without human intervention. Chapter Markers (00:00) - Episode Introduction and opening insight from Dr Gueorgui Mihaylov (04:37) - Setting the scene: What is the AI Inventory Planner solving? (07:17) - The challenge of supply chain unpredictability and stochastic buffering (12:26) - Scenario simulation engine and constraint optimisation (17:00) - Scaling the tool across a global enterprise (19:17) - Introducing the Golden Batch project in pharmaceutical manufacturing (25:00) - System architecture: data historian, execution environment and visualisation (30:57) - From monitoring to closed-loop control: stages of adoption (34:28) - Open problems at the frontier: geometric deep learning and LLM orchestration (37:02) - Advice for practitioners entering industrial AI Useful Links Connect with Dr Gueorgui Mihaylov on LinkedIn: https://www.linkedin.com/in/gueorgui-mihaylov-891278197/ For more AI insights follow Jeremy on LinkedIn: https://uk.linkedin.com/in/jeremy-bradley Explore Cambridge Spark’s AI upskilling programmes at https://www.cambridgespark.com

  • S1 · E41
    May 13 · 37 min

    How the AA Balances AI Innovation and Human Care at Scale with Nick Edwards and Harj Johal

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com The AA has been keeping British drivers moving for 120 years. But behind that familiar brand, a quiet and ambitious transformation is under way. In this episode, Dr Raoul-Gabriel Urma is joined by Nick Edwards, Group Chief Technology Officer, and Harj Johal, Managing Director of Customer Operations, to explore what the AA's guiding principle of digital convenience with human care actually means when applied to real operational decisions. They cover how the AA deployed 1,500 ChatGPT Enterprise licences, built a 75-person champions network from the bottom up, launched an internal Dragons' Den innovation fund, and how they are now training their senior leadership in AI through a structured programme with Cambridge Spark. They also get specific about ROI measurement, managing internal AI demand, and why the most passionate technology adopters were not in the technology function. This is an honest, practical conversation about transformation at scale and a compelling case study for any C-suite leader navigating the same journey. Be sure to follow Data and AI Mastery on Apple Podcasts, Spotify or YouTube so you never miss an episode. Chapter Markers (00:00) - AI at the Roadside (04:04) - How Nick and Harj Collaborate Across Technology and Operations (07:45) - Managing Internal Resistance and the AAs Can-Do Culture (11:32) - What Skills and Mindset the AA Looks for in People (16:53) - What Good Technology Foundation Means for Frontline Teams (21:36) - The Champions Network: A Bottom-Up Adoption Story (25:16) - Future Opportunities and Risks: The Midnight Breakdown Scenario (27:05) - Reimagining the Contact Centre: Haris Vision for the Next Decade (29:56) - Advice for Executives Trying to Demonstrate AI ROI (34:45) - Closing Reflections and What Makes the AAs' Transformation Distinctive Useful Links Follow Dr Raoul-Gabriel Urma on LinkedIn: https://uk.linkedin.com/in/raoulurma Connect with Nick Edwards on LinkedIn: https://www.linkedin.com/in/nick-edwards-7a02b814/ Learn more from Harj Johal on LinkedIn: https://www.linkedin.com/in/harj-johal-42053328/ Visit the Cambridge Spark Website: https://cambridgespark.com/

  • S2 · E1
    May 6 · 28 min

    DAIM: Inside The Algorithm | Hybrid Simulation, Model Reuse and AI in NHS Planning

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com Planning healthcare at scale is one of the most consequential challenges facing the NHS. In this inaugural episode of our new show, Inside The Algorithm, Cambridge Spark’s Chief AI Officer, Dr Jeremy Bradley, is joined by Dr Lucy Morgan, Head of Simulation at the NHS Strategy Unit, to explore how simulation modelling and AI are being used to tackle that challenge head-on. Lucy led the development of a hybrid simulation model for renal replacement therapy, which began with the Midlands Kidney Network and is now being rolled out nationally. She breaks down how the model works, combining system dynamics with discrete event simulation to project future demand and model patient pathways through dialysis and transplant. The conversation moves into the harder questions: how do you validate a model in a live healthcare environment, how do you rebuild trust with new organisations who weren't part of the original build, and where does AI fit into making models more portable and reproducible? Lucy also shares her current research interest in using AI agents to help NHS staff query and interrogate analytical models directly, without needing deep modelling expertise themselves. A rich, technically substantive episode for anyone working at the intersection of AI, simulation and real-world deployment. Follow Data & AI Mastery so you never miss a future episode of Inside The Algorithm. Liked this episode? Why not listen to the conversation Dr Raoul Gabriel Urma had with Ming Tang from the NHS on the Data & AI Mastery podcast: Apple: https://podcasts.apple.com/gb/podcast/transforming-healthcare-through-data-ai-human-centred/id1779783413?i=1000736381325 Spotify: https://open.spotify.com/episode/27wqHT0dnRMwKMedj2uoTw?si=5aabd4015dfa425d YouTube: https://www.youtube.com/watch?v=JERGmZUumvA Glossary Terms Model reproducibility: the ability to obtain consistent computational results using the same input data, code, and environment Neural Network: a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain Sequential Hybrid Simulation Model: a modeling approach that combines two or more distinct simulation paradigms (such as System Dynamics, Discrete-Event Simulation, or Agent-Based Simulation) by running them in a specific order, where the output of the first model acts as the input for the next System Dynamics: a computer-aided modeling methodology used to understand, analyse, and manage complex, dynamic systems over time Stock-flow System: a foundational system dynamics modelling concept mapping how quantities (stocks) accumulate over time, influenced by rates of change (flows) Chapter Markers (00:00) - Opening: The case for AI-assisted simulation in the NHS (02:11) - About the NHS Strategy Unit and the backwards consultancy model (05:04) - Tackling NHS waiting lists with queuing theory (10:26) - Complexity of scheduling: theatre capacity, rotas and prioritisation (13:42) - Introducing the renal replacement therapy planning model (18:32) - Data sources, model calibration and validation (23:00) - Rolling the model out nationally: trust, workshops and participative modelling (26:47) - A call to the community: sharing best practices in model reproducibility Useful Links Connect with Dr Lucy Morgan on LinkedIn: https://www.linkedin.com/in/lucy-morgan-97072041/ For more AI insights follow Jeremy on LinkedIn: https://uk.linkedin.com/in/jeremy-bradley Explore Cambridge Spark’s AI upskilling programmes at https://www.cambridgespark.com

  • S1 · E40
    April 29 · 27 min

    From Cybersecurity to AI Director: Sarah Self on Leading Transformation at Aviva

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com On this week's episode of Data & AI Mastery, Dr Raoul-Gabriel Urma is joined by Sarah Self, AI Director at Aviva. Sarah brings a rare dual perspective to the conversation of data and AI, having spent nearly a decade in cybersecurity before moving into AI leadership. In this episode, she shares how those two disciplines share the same foundations: people, process and technology, and why that framework is central to delivering responsible, scalable AI. Sarah discusses how Aviva built its own internal AI platform to embed guardrails from day one and why she views risk management and customer value not as a trade-off but as a single, unified objective. She also makes a compelling case for democratising AI across the workforce, using the term "organ rejection" to describe what happens when people are left behind in a transformation. The episode closes with two standout production use cases: a claims summarisation solution that cut customer hold times by over 50% and a medical underwriting tool delivering 99% accuracy, both of which drive efficiency and customer satisfaction simultaneously. Follow Data and AI Mastery on Apple Podcasts, Spotify or YouTube so you never miss an episode. Chapter Markers (00:00) - Opening Insight: Experimentation With Purpose (02:44) - The Parallels Between Cybersecurity and AI (05:46) - Balancing Risk and Customer Outcomes at Aviva (10:29) - Democratising AI: Why Workforce Inclusion Matters (14:28) - Identifying High-Value AI Use Cases (17:27) - Balancing Experimentation With Production: The Portfolio Approach (20:16) - Remaining Challenges and Keeping Perspective on the Hype (23:48) - Quickfire Round: Sarah's AI Aha Moment (25:21) - Raoul’s Reflections Useful Links Connect with Sarah Self on LinkedIn: https://www.linkedin.com/in/sarah-self-ba0b0642/ Follow Raoul for more AI insights on LinkedIn: https://www.linkedin.com/in/raoulurma/ Explore Cambridge Spark’s AI upskilling programmes at https://www.cambridgespark.com

  • April 27 · 52 sec

    Data & AI Mastery Presents: Inside the Algorithm | A New Show from Cambridge Spark

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com Most AI commentary stops at the headline. This show starts where that ends. Welcome to Inside the Algorithm, a brand new podcast from Cambridge Spark, hosted by Chief AI Officer Dr Jeremy Bradley. Each episode, Jeremy sits down with leading researchers, data scientists and technical experts to get into the science, the breakthroughs and the ideas shaping the future of AI. No trend commentary. No surface-level takes. Just rigorous, substantive thinking made accessible to the people who need it most. Inside the Algorithm publishes fortnightly in this feed, alternating with Data & AI Mastery, the show for AI strategy, leadership and business transformation. If you want to go deeper into the science driving artificial intelligence forward, hit follow now. Episode one drops 6th May. Stay curious. Stay rigorous. Stay ahead of the algorithm. Useful Links For more AI insights follow Jeremy on LinkedIn: https://uk.linkedin.com/in/jeremy-bradley Explore Cambridge Spark’s AI upskilling programmes at https://www.cambridgespark.com

  • S1 · E39
    April 15 · 31 min

    The Future of Education: Mark Bramwell, CDIO at Oxford Said Business School

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com On this week's episode of Data & AI Mastery, Mark Bramwell, Chief Digital and Information Officer at Oxford Said Business School, joins Dr Raoul-Gabriel Urma to share how one of the world's oldest universities is navigating one of its biggest challenges: deploying AI across a complex, multi-persona institution while simultaneously preparing students for an AI-driven workforce. Mark unpacks Oxford's deliberate portfolio approach to AI tools, why they moved from per-user licensing to consumption-based models, and how they are measuring real business outcomes rather than treating AI as something categorically different from the rest of the tech stack. From a faculty-led AI digital avatar teaching courses in any language, 24/7, to an AI assistant enhancing the MBA student experience, this conversation is grounded in genuine implementation rather than theory. If you lead a complex organisation grappling with AI adoption, governance, and change management at scale, this episode is essential listening. Follow Data and AI Mastery on Apple Podcasts, Spotify or YouTube so you never miss an episode. Chapter Markers (00:00) - Opening Insight: Treating AI Like Any Other Tool (04:02) - Driving Innovation in a Centuries-Old Institution (05:24) - Oxford's AI Strategy: The Portfolio Approach (09:52) - Risks and Lessons Learned from the Consumption Model (12:03) - Preparing Students for an AI-Driven Workforce (17:44) - Governing AI Adoption at Scale (22:58) - Change Adoption and Getting Everyone on the Journey (25:51) - The Future of AI in Higher Education (27:44) - Rapid-Fire Personal Questions (30:06) - Raoul's Key Takeaways Useful Links Connect with Mark Bramwell on LinkedIn: https://www.linkedin.com/in/mark-bramwell-fbcs-308b4913/ Follow Raoul for more AI insights on LinkedIn: https://www.linkedin.com/in/raoulurma/ Explore Cambridge Spark’s AI upskilling programmes at https://www.cambridgespark.com

  • S1 · E38
    April 1 · 32 min

    Redefining Data ROI in Banking: Luke Pearce, Chief Data and AI Officer, Santander UK

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com On this week's episode of Data & AI Mastery, Luke Pearce, Chief Data and AI Officer at Santander UK, joins Dr. Raoul-Gabriel Urma to discuss what it actually takes to drive meaningful AI adoption inside one of the UK's largest banks. Luke shares how Santander UK moved beyond a governance-heavy data culture to build genuine C-suite and board-level sponsorship for AI. He challenges the conventional cost-saving narrative around AI ROI, making the case for customer and colleague delight as the more powerful North Star. The conversation covers how Santander UK structured its AI rollout using a domain-based approach, why cross-functional teams are critical to success, and how to have productive governance conversations with a board that is both excited and cautious about AI. Luke also offers a genuinely contrarian view on the future of the CDO role and what he would change about the industry if he could. A practical and candid episode for any senior data or AI leader navigating organisational complexity. Follow the podcast on Apple Podcasts, Spotify or YouTube so you never miss an episode. Chapter Markers (00:00) - Introduction and Luke Pearce's Career Journey (04:51) - How Industry Experience Shapes Data Expectations (07:24) - Organisational Structures That Support Data and AI (10:19) - Redefining AI ROI Beyond Cost Reduction (14:13) - Board and C-Suite Expectations: Navigating the Difference (18:25) - Lessons for Organisations Just Starting Their AI Journey (22:29) - Picking the Right Domains and Managing AI Momentum (26:21) - Quickfire Round: Contrarian Views and Personal Insights (30:13) - Episode Wrap-Up and Key Takeaways Useful Links Connect with Luke Pearce on LinkedIn: https://uk.linkedin.com/in/luke-pearce-23121522 Follow Raoul for more AI insights on LinkedIn: https://www.linkedin.com/in/raoulurma/ Explore Cambridge Spark’s AI upskilling programmes at https://www.cambridgespark.com

  • S1 · E37
    March 18 · 30 min

    Building an AI Strategy at Scale with Miryem Salah, Director Digital Data & Transformation at VodafoneThree

    👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: cambridgespark.com On this week's episode of Data & AI Mastery, Miryem Salah, Director of Digital, Data and Transformation at VodafoneThree, joins Raoul Gabriel-Urma to share the story behind one of the most ambitious AI strategies in UK telecoms. Miryem breaks down how her team approached building a forward-looking AI strategy from first principles, why most organisations fall into the FOMO trap, and how VodafoneThree is thinking about the balance between humans and AI in a post-merger business. She also shares her thinking on governed agility, the importance of data literacy at every level of leadership, and why getting foundations right matters more than chasing the next shiny tool. The conversation also touches on diversity in data and tech, Miryem's Women in Data network spanning 100,000 employees across VodafoneThree; and her genuine excitement about the rise of physical AI. A frank, experience-led conversation for senior data and AI leaders navigating large-scale transformation. Be sure to follow Data & AI Mastery wherever you listen to your podcasts to never miss an episode. Chapter Markers (00:00) - Introduction and Miryem's role at VodafoneThree (02:00) - Inside the Vodafone and Three UK merger (06:00) - Misconceptions about data teams and data ownership (10:00) - Operating model, human versus AI, and the sell-build-run framework (15:00) - Measuring success in early-stage AI deployment (19:00) - Risk management and when to put AI in front of customers (24:00) - Quickfire round: diversity, STEM, music and the magic wand question (27:00) - Raoul on AI and the future of education (28:30) - Key takeaways Useful Links Connect with Miryem Salah on LinkedIn: https://uk.linkedin.com/in/miryem-salah-7a3ba633 Follow Raoul for more AI insights on LinkedIn: https://www.linkedin.com/in/raoulurma/ Explore Cambridge Spark’s AI upskilling programmes at https://www.cambridgespark.com

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