Data & AI Mastery

<p>👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: <a href="https://cambridgespark.com/" target="_blank">cambridgespark.com</a></p> <p>In this special episode of Data &amp; 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.</p> <p>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?</p> <p>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.</p> <p>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.</p> <p>If you work in data, technology, or strategy inside a regulated industry, this is essential listening.</p> <p>Be sure to follow the show wherever you get your podcasts to never miss an episode.</p> <p>Enjoyed this conversation? Why not tune in to our episode with Edmund Towers, Head of Advanced Analytics &amp; Data Science Units at the FCA:</p> <p>Apple: https://podcasts.apple.com/gb/podcast/ai-regulation-and-trust-using-data-science-to-protect/id1779783413?i=1000750268699</p> <p>Spotify: https://open.spotify.com/episode/3JD7g1B47xI8JJ71ROF1Vp?si=00dc012933554b40</p> <p>YouTube: https://www.youtube.com/watch?v=379oyRKg-eE</p> <p><b>Chapter Markers</b></p> <p>(00:00) - Introduction and what responsible AI really means</p> <p>(02:26) - Jessica's career journey from Ford to the FCA</p> <p>(08:42) - Inside the FCA's regulatory and digital sandboxes</p> <p>(14:40) - Scaling AI responsibly and what doing it badly looks like</p> <p>(19:18) - Why the FCA chose not to write new AI-specific rules</p> <p>(24:57) - Accelerators and blockers of AI adoption in fintech</p> <p>(31:37) - The T-shaped skills framework for future careers</p> <p>(35:04) - Key takeaways</p> <p><b>Useful Links</b></p> <p>Follow Dr Raoul-Gabriel Urma on LinkedIn:<a href="https://uk.linkedin.com/in/raoulurma" target="_blank"> https://uk.linkedin.com/in/raoulurma</a></p> <p>Connect with Jessica Rusu on LinkedIn:<a href="https://uk.linkedin.com/in/jessica-rusu-189134a" target="_blank"> https://uk.linkedin.com/in/jessica-rusu-189134a</a> </p> <p>Visit the Cambridge Spark Website:<a href="https://cambridgespark.com/" target="_blank"> https://cambridgespark.com/</a></p>

<p>👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: <a href="https://cambridgespark.com/" target="_blank">cambridgespark.com</a></p> <p>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.</p> <p>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.</p> <p>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.</p> <p>Follow Data &amp; AI Mastery so you never miss a future episode of Inside The Algorithm.</p> <p>If you want to learn more about how AI is revolutionising demand forecasting and supply chain optimisation, listen to the Data &amp; AI Mastery episode between host Dr Raoul Gabriel Urma and Peter Laflin, Director of Data and Analytics at Morrisons: </p> <p>Apple: <a href="https://podcasts.apple.com/gb/podcast/bringing-art-to-science-using-ai-to-build-customer/id1779783413?i=1000677504555" target="_blank">https://podcasts.apple.com/gb/podcast/bringing-art-to-science-using-ai-to-build-customer/id1779783413?i=1000677504555</a> </p> <p>Spotify: <a href="https://open.spotify.com/episode/6HcwcdIGOguBZp1vADE1Z0" target="_blank">https://open.spotify.com/episode/6HcwcdIGOguBZp1vADE1Z0</a> </p> <p>YouTube: <a href="https://www.youtube.com/watch?v=NKdjiYVC9H8" target="_blank">https://www.youtube.com/watch?v=NKdjiYVC9H8</a> </p> <p><b>Glossary Terms:</b></p> <p><b>Inventory Management: </b>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.</p> <p><b>Stochastic Phenomena: </b>Events, processes, or systems that are inherently random, unpredictable, or probabilistic in nature, rather than deterministic.</p> <p><b>Demand Forecasting: </b>The process of predicting future customer demand for products or services using historical sales data, market trends, and analytical methods.  </p> <p><b>Out-of-sample bootstrapping: </b>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.</p> <p><b>Non-Newtonian Fluid: </b>A substance whose viscosity changes when subjected to stress, agitation, or shear force, rather than remaining constant.  </p> <p><b>IoT Device: </b>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.</p> <p><b>Chapter Markers</b></p> <p>(00:00) - Episode Introduction and opening insight from Dr Gueorgui Mihaylov</p> <p>(04:37) - Setting the scene: What is the AI Inventory Planner solving?</p> <p>(07:17) - The challenge of supply chain unpredictability and stochastic buffering</p> <p>(12:26) - Scenario simulation engine and constraint optimisation</p> <p>(17:00) - Scaling the tool across a global enterprise</p> <p>(19:17) - Introducing the Golden Batch project in pharmaceutical manufacturing</p> <p>(25:00) - System architecture: data historian, execution environment and visualisation</p> <p>(30:57) - From monitoring to closed-loop control: stages of adoption</p> <p>(34:28) - Open problems at the frontier: geometric deep learning and LLM orchestration</p> <p>(37:02) - Advice for practitioners entering industrial AI</p> <p><b>Useful Links</b></p> <p>Connect with Dr Gueorgui Mihaylov on LinkedIn:<a href="https://www.linkedin.com/in/gueorgui-mihaylov-891278197/" target="_blank"> https://www.linkedin.com/in/gueorgui-mihaylov-891278197/</a> </p> <p>For more AI insights follow Jeremy on LinkedIn: <a href="https://uk.linkedin.com/in/jeremy-bradley" target="_blank">https://uk.linkedin.com/in/jeremy-bradley</a></p> <p>Explore Cambridge Spark’s AI upskilling programmes at <a href="https://www.cambridgespark.com" target="_blank">https://www.cambridgespark.com</a></p>

<p>👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: <a href="https://cambridgespark.com/" target="_blank">cambridgespark.com</a></p> <p>The AA has been keeping British drivers moving for 120 years. But behind that familiar brand, a quiet and ambitious transformation is under way.</p> <p>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.</p> <p>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.</p> <p>This is an honest, practical conversation about transformation at scale and a compelling case study for any C-suite leader navigating the same journey.</p> <p>Be sure to follow Data and AI Mastery on Apple Podcasts, Spotify or YouTube so you never miss an episode.</p> <p><b>Chapter Markers</b></p> <p>(00:00) - AI at the Roadside</p> <p>(04:04) - How Nick and Harj Collaborate Across Technology and Operations</p> <p>(07:45) - Managing Internal Resistance and the AAs Can-Do Culture</p> <p>(11:32) - What Skills and Mindset the AA Looks for in People</p> <p>(16:53) - What Good Technology Foundation Means for Frontline Teams</p> <p>(21:36) - The Champions Network: A Bottom-Up Adoption Story</p> <p>(25:16) - Future Opportunities and Risks: The Midnight Breakdown Scenario</p> <p>(27:05) - Reimagining the Contact Centre: Haris Vision for the Next Decade</p> <p>(29:56) - Advice for Executives Trying to Demonstrate AI ROI</p> <p>(34:45) - Closing Reflections and What Makes the AAs' Transformation Distinctive</p> <p><b>Useful Links</b></p> <p>Follow Dr Raoul-Gabriel Urma on LinkedIn:<a href="https://uk.linkedin.com/in/raoulurma" target="_blank"> https://uk.linkedin.com/in/raoulurma</a></p> <p>Connect with Nick Edwards on LinkedIn:<a href="https://www.linkedin.com/in/nick-edwards-7a02b814/" target="_blank"> https://www.linkedin.com/in/nick-edwards-7a02b814/</a></p> <p>Learn more from Harj Johal on LinkedIn:<a href="https://www.linkedin.com/in/harj-johal-42053328/" target="_blank"> https://www.linkedin.com/in/harj-johal-42053328/</a></p> <p>Visit the Cambridge Spark Website:<a href="https://cambridgespark.com/" target="_blank"> https://cambridgespark.com/</a></p>

<p>👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: <a href="https://cambridgespark.com/" target="_blank">cambridgespark.com</a></p> <p>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.</p> <p>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.</p> <p>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?</p> <p>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.</p> <p>A rich, technically substantive episode for anyone working at the intersection of AI, simulation and real-world deployment.</p> <p>Follow Data &amp; AI Mastery so you never miss a future episode of Inside The Algorithm.</p> <p>Liked this episode? Why not listen to the conversation Dr Raoul Gabriel Urma had with Ming Tang from the NHS on the Data &amp; AI Mastery podcast: </p> <p>Apple: https://podcasts.apple.com/gb/podcast/transforming-healthcare-through-data-ai-human-centred/id1779783413?i=1000736381325</p> <p>Spotify: https://open.spotify.com/episode/27wqHT0dnRMwKMedj2uoTw?si=5aabd4015dfa425d</p> <p>YouTube: https://www.youtube.com/watch?v=JERGmZUumvA</p> <p><b>Glossary Terms</b></p> <p><b>Model reproducibility:</b> the ability to obtain consistent computational results using the same input data, code, and environment</p> <p><b>Neural Network:</b> a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain</p> <p><b>Sequential Hybrid Simulation Model:</b> 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</p> <p><b>System Dynamics:</b> a computer-aided modeling methodology used to understand, analyse, and manage complex, dynamic systems over time</p> <p><b>Stock-flow System: </b>a foundational system dynamics modelling concept mapping how quantities (stocks) accumulate over time, influenced by rates of change (flows)</p> <p><b>Chapter Markers</b></p> <p>(00:00) - Opening: The case for AI-assisted simulation in the NHS</p> <p>(02:11) - About the NHS Strategy Unit and the backwards consultancy model</p> <p>(05:04) - Tackling NHS waiting lists with queuing theory</p> <p>(10:26) - Complexity of scheduling: theatre capacity, rotas and prioritisation</p> <p>(13:42) - Introducing the renal replacement therapy planning model</p> <p>(18:32) - Data sources, model calibration and validation</p> <p>(23:00) - Rolling the model out nationally: trust, workshops and participative modelling</p> <p>(26:47) - A call to the community: sharing best practices in model reproducibility</p> <p><b>Useful Links</b></p> <p>Connect with Dr Lucy Morgan on LinkedIn: <a href="https://www.linkedin.com/in/lucy-morgan-97072041/" target="_blank">https://www.linkedin.com/in/lucy-morgan-97072041/</a></p> <p>For more AI insights follow Jeremy on LinkedIn: <a href="https://uk.linkedin.com/in/jeremy-bradley" target="_blank">https://uk.linkedin.com/in/jeremy-bradley</a></p> <p>Explore Cambridge Spark’s AI upskilling programmes at <a href="https://www.cambridgespark.com" target="_blank">https://www.cambridgespark.com</a></p>

<p>👉 Discover how Cambridge Spark helps organisations build the data and AI capabilities needed to turn strategy into measurable impact: <a href="https://cambridgespark.com/" target="_blank">cambridgespark.com</a></p> <p>On this week's episode of Data &amp; 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.</p> <p>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.</p> <p>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.</p> <p>Follow Data and AI Mastery on Apple Podcasts, Spotify or YouTube so you never miss an episode.</p> <p><b>Chapter Markers</b></p> <p>(00:00) - Opening Insight: Experimentation With Purpose</p> <p>(02:44) - The Parallels Between Cybersecurity and AI</p> <p>(05:46) - Balancing Risk and Customer Outcomes at Aviva</p> <p>(10:29) - Democratising AI: Why Workforce Inclusion Matters</p> <p>(14:28) - Identifying High-Value AI Use Cases</p> <p>(17:27) - Balancing Experimentation With Production: The Portfolio Approach</p> <p>(20:16) - Remaining Challenges and Keeping Perspective on the Hype</p> <p>(23:48) - Quickfire Round: Sarah's AI Aha Moment</p> <p>(25:21) - Raoul’s Reflections</p> <p><b>Useful Links</b></p> <p>Connect with Sarah Self on LinkedIn:<a href="https://www.linkedin.com/in/sarah-self-ba0b0642/" target="_blank"> https://www.linkedin.com/in/sarah-self-ba0b0642/</a></p> <p>Follow Raoul for more AI insights on LinkedIn: <a href="https://www.linkedin.com/in/raoulurma/" target="_blank">https://www.linkedin.com/in/raoulurma/</a></p> <p>Explore Cambridge Spark’s AI upskilling programmes at <a href="https://www.cambridgespark.com" target="_blank">https://www.cambridgespark.com</a></p>