The AI Adoption Podcast

Professor Ashley Braganza

The AI Adoption Podcast where cutting-edge artificial intelligence meets real-world relevance. The show offers an accessible, approachable take on some of the most complex topics in AI, making the effects of AI understandable and engaging for everyone, from curious beginners to tech-savvy professionals and business leaders. Each episode features in-depth conversations with leading AI policy makers, researchers, innovators, regulators, ethicists, and thought leaders. You will hear diverse voices, even sceptics, ensuring balanced and lively discussions, exploring the adoption of AI.

  1. 3d ago

    Leaders Who Do Not Value Data Slow AI Adoption

    Leadership teams can explain how they make money, but not their data. Ask about data architecture, and the reply my guest voices from the boardroom is a question: “Why do I need to know that?” This conversation makes suggests that this is a cultural problem rather than a technical one. Claire Thompson, Chief Data Analytics Officer for the Corporate Bank at Barclays, argues that AI is primarily a people and culture challenge. An organisation’s culture decides the extent to which AI opportunities becomes part of everyday work. Claire makes the case that generative AI is probabilistic rather than deterministic. It can give a different answer to the same question each time, and that answer may not be right, so it cannot be rolled out like a conventional digital programme. She argues that disconnected AI pilots deliver some value but not the value that matters, and that investments should be anchored to business or client outcomes. Governance, in her view, is much easier to put in at the outset rather than once agents are sprawling across the organisation. She contends that the more people around the leadership table who understand AI, data and technology, the more likely an organisation is to succeed. • Three routes bring AI into an organisation: AI tools in everyone’s hands, capability vendors build into their applications, and bespoke internal models. Each needs different governance. • The scaling checkpoint: personal use within clear data boundaries is fine; share a tool with 100 people and it goes through triage. • The hidden dependency: a locally built tool becomes critical to a process, then fails, with no backup plan. • Role modelling: leaders who say “it’s not a theory, this is how I’ve done it” move colleagues from curiosity to confidence. • Job anxiety is not new in data analytics; the value sits in “the valuable knowledge that people have in their heads”. Listen before your next AI pilot is approved. Chapters 00:00 AI as a People and Culture Challenge 05:06 Transforming Processes with AI 09:47 Successful AI Adoption Strategies 11:34 Addressing Job Security Concerns with AI 12:41 Human-Agentic Teams in the Workplace 13:35 Senior Management’s Role in AI Implementation 15:24 The Data Management Challenge 16:54 Moving from Local AI Use to Strategic Implementation 18:21 Governance in AI Adoption 23:45 Improving Data Maturity in Organisations 25:37 Cultural Implications of AI Adoption

    Leaders Who Do Not Value Data Slow AI Adoption

  2. Oct 15

    Geopolitics Makes AI Access to GIS Data a Board Priority

    Opening a hydrant map, as an example of vital infrastructure, to AI speeds up repairs and widens access. The data that tells a crew to fix the hydrant near the hospital first could also show an adversary where a city is weakest. Dino Ravnić, Co-founder and CEO of GIS Cloud, argues that AI is the final step in opening geographic information systems (GIS), the software organisations use to record and analyse their physical assets by location, to people who are not specialists, and that this works only with a layer of control between the data and the AI. Dino makes the case for AI as a virtual colleague in the workflow, handling triage such as comparing a field photo of a hydrant with what was reported, while a person signs off. Feeding an entire dataset into a large language model, in Dino's view, burns budgets on tokens and invites hallucination, so a GIS should sit in between as the system the AI queries. On sovereignty, Dino observes that customers often want data kept within the EU and that a vendor's origin matters more than before. The conversation closes on citizens, for whom public data on schools, kindergartens and outages already exists but sits in portals that are hard to use. • A solar plant built with each panel recorded as a polygon on a map, so decision makers see progress from frames to wiring in real time. • An example of GIS would be hydrant repairs ranked by proximity to schools, hospitals and homes for elderly people who are hard to evacuate in a fire. • A separate permission in GIS Cloud governing whether a user may read or edit shared data through AI. • "Once you start to share the data, then you lose control." Dino Ravnić • Shadow AI: staff dragging data into ChatGPT or Claude without the organisation being aware. Press play to understand the interplay between GIS that holds a map of assets, AI and geopolitics. Chapters 00:00 The Importance of Data Control in AI 04:41 Use Cases of GIS in Organisations 07:52 Integrating AI with GIS 09:50 AI vs. Internet of Things: Understanding the Difference 11:15 Democratisation of GIS: Making Data Accessible 13:54 Enhancing Decision-Making with GIS 17:09 Geopolitical Implications of GIS Data 18:16 Governance and Sovereignty in GIS Data 20:56 Balancing Democratisation and Control 24:40 The Impact of GIS on Citizens

    Geopolitics Makes AI Access to GIS Data a Board Priority

  3. Oct 8

    AI Agents Overlaid On Fragmented Processes Create Digital Clutter, Not Value

    This is Part Two of a two-part conversation, and it moves from the global picture to adoption inside the organisation. Ericson Chan, Group Chief Information and Digital Officer at Zurich Insurance Group, makes the case that boards should judge AI by the value it delivers across a portfolio, not by a pipeline of individual projects, proofs of concept and use cases. Ericson explains that agents mark a shift from generating content to executing work, so an unreliable answer can become an unreliable action inside a business process. For now, Zurich orchestrates its agents through a deterministic workflow, with a predefined path from one agent to the next, and gives each agent an operating envelope. He argues that the transformation is gradual but not sequential, because technical capability is moving faster than organisations can absorb it. Boards, in his view, must govern AI as a portfolio that is an integral part of the enterprise strategy and capital allocation. • ROAI, Ericson's term for return on AI, judged across a portfolio rather than demanded from every experiment • An operating envelope for each agent: identity, permissions, data access, permitted actions, escalation thresholds and monitoring • Zurich's Hyper Challenge agentic AI hackathons, with hundreds of participants from 20 to 30 countries • "Half the team will never sleep": managers leading teams of people and agents • Junior tasks such as research, drafting and reconciliation, and an apprenticeship model Ericson says must be rethought Listen to the closing exchange, where Ericson turns the question on the host and students and new employees turn out to face the same test of judgement. Chapters 00:00 Holistic Value in AI Adoption 02:40 The Rise of Agentic AI 06:36 Understanding Deterministic vs Non-Deterministic Approaches 08:14 Gradual or Radical Transformation in AI Implementation 09:48 Governance and Strategic Planning for AI 11:43 Managing AI as a Portfolio 13:14 Collaboration Between Humans and AI Agents 16:07 Cultural Shifts in Organisations with AI 18:53 AI Literacy for the Next Generation 22:22 Balancing AI Automation and Learning Opportunities

    AI Agents Overlaid On Fragmented Processes Create Digital Clutter, Not Value

  4. Oct 1

    Zurich Insurance Is Building One AI Strategy Across Rival Regions (Part 1)

    Three regions are building AI for three different reasons. The US pushes frontier models towards AGI, China embeds applied AI into manufacturing and logistics, and Europe builds trust through risk based regulation. A global insurer has to operate inside the three at once. Ericson Chan, Group Chief Information and Digital Officer at Zurich Insurance Group, makes the case that a multinational needs one AI strategy and variations on implementation. Ericson argues that regulators in Europe, the US and China start from different places but converge on safety, transparency and accountability, which Zurich codifies as STAR. The harder divergence is cultural, and an insightful metaphor is customers raised on Astro Boy and Doraemon see technology as a helper, while Western science fiction offers Terminator and Skynet. He contends that AI owned by the technology function fails, because the operating business must own process redesign, adoption and realise value. Productivity gains, in his view, become a bar competitors will cross, so the larger opportunity lies in redesigning the value chain end to end and preventing risk before it happens. • STAR: Security, Transparency, Accountability and Reliability as Zurich's non-negotiable principles across jurisdictions • 75 to 80 percent of Zurich's workforce actively using AI tools, up from a quarter two years earlier • A global AI strategy as a constitution backed by a playbook, with local teams owning implementation and value • Zurich's AI360 strategy: AI for Everyone, AI at the Core and AI for the Future • Small language models based on decades of Zurich's own knowledge as a source of competitive advantage This is Part One of a two-part conversation. Listen for the regional picture before Part Two turns to adoption and implementation in detail next week. Chapters 00:00 The Importance of Transparency in Leadership 03:16 Global Perspectives on AI Development 07:53 AI as a Threat and Opportunity 08:39 Challenges and Opportunities in Scaling AI 12:39 Cultural Influences on AI Adoption 14:44 The Need for a Unified AI Strategy 17:56 Coordination in Decentralised Implementation 19:23 Leadership Priorities in AI Adoption 23:57 Transforming Business with AI 26:52 Zurich's AI Strategy: AI 360

    Zurich Insurance Is Building One AI Strategy Across Rival Regions (Part 1)

  5. Sep 24

    43 Police Forces, One AI Mission and Little Power to Mandate

    The Home Secretary has charged PoliceAI with making policing in England and Wales "the most AI enabled police force in the world", yet each force keeps its own technology, risk appetite and culture. Proving that an AI tool works is not the hard part; scaling it within one force and then from one force to the next is. Dylan Alldridge, Deputy Director for Strategy, Oversight and Coordination at PoliceAI, argues that national adoption will be won through evidence, education and unglamorous paperwork and bureaucracy reductions. Dylan makes the case that AI should first target the "unsexy", process heavy work of case files, disclosure schedules and transcription, freeing time officers can spend with victims. He sets the Home Office target of around six million hours, or 3,000 full time equivalent roles, against workforce fears about jobs, and is candid that the use of released time rests with each force's chief officers. He treats public trust as something earned by showing as well as telling, through an AI registry every force will be required to maintain. He also names the risk of moving early: vendor lock in and rising token costs, the metered charges for using AI models, which could leave the public sector dependent on AI it cannot afford. • Pathfinder forces trial and evidence a tool before the case is made to scale it across policing. • "Two, three hours a day" back in an officer's pocket is the prize from fewer bureaucratic tasks. • The target is not a brief "to find 3000 people's jobs", and there is a recognition that jobs will change. • A public AI registry lets any citizen scrutinise their force's use of AI and the testing behind it. • AI, in a phrase Dylan credits to PoliceAI's director Alex Murray, can "put the humanity back into policing". Press play for a frank account of adopting AI across 43 organisations that the public perceives as one. Chapters 00:00 Introducing PoliceAI and the case for a national centre 05:13 Scaling AI across 43 independent forces 07:03 Pathfinder forces and the unglamorous paperwork 10:00 Culture, ease of use and the six million hours target 14:05 Roles, jobs and time back for officers 16:39 Data, governance and consistent standards 21:51 Public trust and the AI registry 25:02 The impact on victims and society 27:12 Acting early and the risk of unaffordable AI 31:18 The victim comes first

    43 Police Forces, One AI Mission and Little Power to Mandate

  6. Sep 17

    Link Strategic AI Governance to Everyday Customer Trust

    NatWest holds every AI system to Europe's standard, wherever it runs. One rulebook across the UK, the EU and North America, chosen because governing to several standards at once multiplies the risk work rather than reducing it. Paul Dongha, Head of Responsible AI and AI Strategy at NatWest Group, makes the case that AI governance earns its keep only when it reaches the branch counter and the customer, not just the risk committee or the AI Ethics Panel. The conversation moves from the enterprise risk frameworks banks have run for decades to the ways those frameworks bend, and sometimes strain, under autonomous agents. Paul argues that trust, not compliance, is the real asset AI governance protects. He sets out the need for a diverse ethics panel that argues each use case beats any scoring formula, and the provision of role-based training reaches staff who never touch an AI model but still field customer questions about it. Highlights • NatWest applies the EU AI Act standard to its AI internationally, so a system built in the UK can move into the EU with no further compliance work. • The EU AI Act sorts systems into four tiers, from prohibited to low risk; a credit lending decision counts as high risk because it changes a person's life. • Guardrails block toxic output and stop confidential data leaving the building; hallucinations, factually wrong output, are treated as a managed risk. • Persona-based training runs across the bank, from data scientists testing for bias to branch colleagues who need to answer a customer's question about AI. • Ethics trade-offs are settled by reasoned debate against the bank's values, in Paul's words "not computational. You can't apply a formula." Press play, for a working blueprint of AI governance that reaches the customer, not just the committee. Chapters 00:00 The dual nature of AI: risks and benefits 02:00 Responsible AI in banking 03:09 Governance of agentic AI in financial services 08:29 Aligning with the EU AI Act 12:06 Risk assessment and categorisation in AI 15:16 Cross-jurisdictional governance challenges 17:30 The importance of trust in AI governance 20:19 AI literacy: building a knowledgeable workforce 22:33 Persona-based training for AI governance 24:46 The role of AI ethics councils 29:11 Balancing business needs and ethical governance

    Link Strategic AI Governance to Everyday Customer Trust

  7. Sep 10

    Organisation Spaghettification is the Barrier to Enterprise AI Adoption

    Enterprise AI is not working at the speed the market promised. The reason is not the models. It is the complexity that decades of growth leave inside any company. Dhiraj Rajaram, founder and CEO of Mu Sigma, argues that databases record actions and not the decisions and perceptions that produced the actions. He makes the case that many business cases fail because leaders solve problems in the vertical silos, as separate nodes rather than a connected constellation or network. He sets out a field of context built from three traces, actions, decisions and perceptions. He argues that the unit that matters is the decision, not the data point. He offers a physics analogy that treats data as mass and context as energy, and holds that a great deal of an organisation's context is still dark, as influences of human judgement and organisation culture remain hidden. Some highlights from the conversation. ● A store's records show 32 door frames sold in a month, not the discounts approved or the weather that drove the sales. ● The big D is decisions, not data; leaders optimise truth against time on a continuous basis. ● Spaghettification: decades of markets, geographies, products and acquisitions leave complexity that blocks the business case. ● A home improvement retailer reframes contractors from a segment to own into a distribution channel to collaborate with. ● Context behaves like a field: a small amount of content creates a large amount of context, and much of it remains dark. For any board funding agentic enterprise AI, this conversation reframes the work that has to happen first. Chapters ● 00:00 Challenges in building enterprise-level AI business cases ● 04:45 Complexity in large organisations and its impact on AI ● 07:03 Problems as constellations, not silos ● 10:42 From databases to a field of context ● 12:05 Making decisions more visible with AI ● 18:24 Unpacking action, perception, and decision traces ● 20:57 The three I's: intention, intelligence, initiative ● 24:31 Real-world example: problem redefinition in organisations ● 32:28 Key takeaways and future steps for enterprise AI

    Organisation Spaghettification is the Barrier to Enterprise AI Adoption

  8. Sep 3

    Rethinking, Reimagining is The AI Leadership Test

    Many organisations begin with what AI technology can do. Vilas Dhar, President of the Patrick J. McGovern Foundation, argues that is the wrong place to start, and that leaders treating AI as a IT product purchase are ceding ground to those willing to reimagine their purpose, strategy and processes. Vilas makes the case that AI adoption is a question of leadership and organisational courage, not a technology checklist. Moreover, leaders need to prepare for wider economic changes as AI technologies become ubiquitous. Counterintuitively, he describes a red-teaming discipline that asks a team to argue the opposite of the adoption case. He resists the reflex to equate AI with headcount reduction and locates the quarterly-earnings pressure not with chief executives alone but with the public investors who reward maintenance over transformation. He points to a blood-supply organisation that tied donor mobilisation directly to AI demand projections and rewrote assumptions held for over 25 years. • Public markets still run on heuristics roughly 50 to 60 years old, while some organisations stay private at 50 to 100 billion dollars to fund transformation. • Vilas: "one of the opportunities of the AI moment is not really about AI." • Leaders hold an AI mental model for about six weeks before reaching for the next. Press play for reimagining the organisation, not automating it, is the decision boards face. Chapters 00:00 AI and Leadership Principles 05:29 Are Boards Ready for AI? Practices and Outcomes 08:05 Organisational Readiness and Cultural Shifts 10:24 Reimagining Supply Chains with AI 14:08 Reimagining Business and Questioning Assumptions 16:53 Empathy and Data Skills for Leaders 19:13 Non-Technical Leaders and AI Authority 22:07 Scenario Planning and Red Teaming AI 23:27 Reconciling AI Speed with Human Decision-Making 25:54 Changing Organisations for AI Succes

    Rethinking, Reimagining is The AI Leadership Test
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About

The AI Adoption Podcast where cutting-edge artificial intelligence meets real-world relevance. The show offers an accessible, approachable take on some of the most complex topics in AI, making the effects of AI understandable and engaging for everyone, from curious beginners to tech-savvy professionals and business leaders. Each episode features in-depth conversations with leading AI policy makers, researchers, innovators, regulators, ethicists, and thought leaders. You will hear diverse voices, even sceptics, ensuring balanced and lively discussions, exploring the adoption of AI.

Information

  • Creator
    Professor Ashley Braganza
  • Years Active
    2025 - 2026
  • Episodes
    73
  • Rating
    Clean
  • Copyright
    © Professor Ashley Braganza
  • Show Website
    The AI Adoption Podcast

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