The AI Adoption Podcast

Professor Ashley Braganza
  • 5.0 (1)
  • Business
  • Updated Weekly

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

    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)

  2. 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

  3. 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

  4. 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

  5. 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

  6. Aug 27

    AI Advisers in the Workplace Will Reshape Culture, Ready or Not

    Human in the loop and AI in the loop have become the reflex phrases of enterprise AI. Kerri O'Neill argues that both frame the relationship wrongly, and that the more we picture humans over here and AI over there, the more risk we take on. Kerri O'Neill, Chief People Officer at Ipsos UK and Ireland and its Global AI Workforce Transformation Lead, makes the case that culture, not technology, is where AI adoption succeeds or fails. Kerri argues that a lot of AI failures happen in the cultural layer rather than in the technology. She holds that agents can share a purpose only when leaders supply context, boundaries and a direction of travel, and that mindsets, being trainable, shift a culture faster than deeply held values. She warns that giving an agent too much certainty can accidentally narrow an organisation's ambition, since the unexpected is part of any change. On connection, she treats human dialogue as the seat of culture and cautions that heavy reliance on agents may thin it over two or three years. In this episode: ● Kerri sets out the "augmented Ipsos" vision led by CEO Kelly Beaver, built on trust, truth and transparency and four operating principles: security, simplicity, substance and speed. ● She reframes resistance to change as a signal worth listening to, not a problem to overcome. ● She names loneliness as, in medical terms, as much of a threat to health as obesity. ● She describes the human brain as the supercomputer behind your eyes, and serendipity between people as something AI struggles to replicate. Kerri draws on Rapid Reculturing, her book with Alex Bailey, to test whether a culture built for people can hold once agents share the work. Press play for a leadership account of AI adoption that starts with culture. Chapters 00:00 Introduction to AI and organisational purpose 02:18 Culture as a foundation for AI success 05:21 Culture and AI failures 07:22 Human abilities versus AI capabilities 10:32 Building shared mindsets in agentic organisations 12:37 Using mindsets to shift organisational culture 15:21 Human connection in an AI-driven world 16:03 The importance of human connection and serendipity 17:27 AI's limitations in replicating human creativity 18:52 Neuroscience and organisational design 19:26 Organisational transformation and AI 20:41 Uncertainty and AI in change management 22:33 Testing and exploring organisational uncertainty with AI 24:30 Dialogue and conversations in managing uncertainty 25:41 AI as a conversational partner and adviser 26:09 AI's role in shaping organisational culture 27:05 Complementary roles of humans and AI 28:45 AI adoption at Ipsos 30:13 Trust, transparency and principles in AI 32:34 Big questions for the future of AI in organisations

    AI Advisers in the Workplace Will Reshape Culture, Ready or Not

  7. Aug 20

    Public sector AI faces a choice between innovation and cuts

    Higher productivity can come from creating more value or consuming fewer resources. For public sector leaders adopting AI, that distinction matters. Ash Thankey, Managing Director and General Manager for Amazon Web Services Public Sector across the UK, Germany and International Organisations, argues that AI is already generating productivity improvements and changing service delivery. Ash cites figures suggesting that 58 per cent of public sector organisations have adopted AI at some level, with 71 per cent reporting significant productivity gains. He points to the Department for Work and Pensions, where AI has been used to triage applications so vulnerable citizens can receive benefits more quickly. His argument is that productivity can mean freeing civil servants from repetitive work and allowing them to concentrate on citizens and higher value activities. But productivity has another side. Ash distinguishes direct pound savings from productivity savings whose effects may emerge over several years. That creates a significant question for public sector leaders. AI can support innovation in services, but measurable cost reductions can be easier to demonstrate. The conversation also moves into the infrastructure underneath adoption. Ash argues for multimodel environments in which organisations can select different AI models according to the task and cost rather than becoming locked into a single model. Highlights • 58 per cent of public sector organisations have employed AI at some level, according to figures cited by Ash. • 71 per cent report significant productivity gains. • The Department for Work and Pensions uses AI to support the triage of benefit applications. • Ash distinguishes direct pound savings from longer term productivity savings. • Multimodel AI can allow organisations to select models according to application and cost. The question for leaders is not simply whether AI increases productivity, but which form of productivity they choose to pursue. Chapters 00:00 Introduction to AI's evolving role in society 02:06 Ash Thankey's role at AWS and public sector focus 03:0 Public sector's adoption of AI and its impact 04:03 Historical perspective on technological change and job creation 05:03 AI's effect on jobs and new industry creation 06:16 The rise of solopreneurs and AI-driven startups 07:10 AI as an augmentation tool for skilled professionals 08:21 AI's role in boosting UK productivity 09:27 AI as a force multiplier for economic growth 11:06 Preparing the workforce for AI integration 12:41 Public sector's progress in AI adoption 14:10 Overcoming challenges in public sector AI implementation 18:47 The importance of multi-model AI systems 22:41 Strategic steps for public sector AI adoption 25:14 Addressing the 'bad and ugly' of AI projects 26:42 The role of higher education in AI readiness 28:36 The ongoing AI journey and continuous learning

    Public sector AI faces a choice between innovation and cuts

  8. Aug 13

    AI Tools Drive New Crimes and Social Behaviours, and Policing Is Behind

    Criminals and members of the public are learning and using AI faster than the police can respond. In the parts of policing that handle domestic abuse, non-consensual intimate images and spiking, that gap is not abstract. It shapes what happens when an officer reaches a victim at three in the morning. Claire Hammond, temporary Detective Chief Superintendent at the National Centre for Violence Against Women and Girls and Public Protection, argues that AI belongs in these cases precisely because they are the hardest, and that the human must keep every decision. The conversation sets out a closed AI agent, named M, embedded in the Centre’s digital toolkit and live across 43 forces. Claire makes the case that M’s value is not speed alone but pattern: an officer treating one crime in isolation can be prompted to consider coercive control, stalking, or a wider history of abuse. She is candid that policing was pushed into AI rather than choosing it, and that senior leaders carry more caution than frontline officers who adopt anything that saves time. She holds one line throughout: AI supports the officer, it does not replace the decision maker. Highlights: • M is a closed agent, built to answer only from national guidance rather than learn from open data, giving one national response at any hour. • Deepfake abuse images are now produced at the touch of a button, where they were once cut from a catalogue and pasted by hand. • AI drafts overnight handover summaries, so an oncoming officer inherits the case rather than a two-line email. • Disclosure that once meant reading 20 to 30 years of records is condensed for a human to check and decide. • “Criminals are using AI at such a speed as well. And they’re learning a lot quicker than we are.” For an account of AI adoption where the stakes are highest and the human stays in charge, this conversation is worth your time. 00:00 Introduction to AI's role in policing and public safety 02:17 Claire Hammond introduces the NCVPP and her role 04:41 The NCVPP's work in AI and police training 06:46 How AI is changing police work and crime detection 08:14 Detecting AI-enabled crimes like deepfakes and fraud 09:24 Examples of AI in case file management and disclosures 11:36 Cultural challenges and trust in AI within police 13:23 Impact of AI on public understanding and safety 17:52 Using AI to support decisions in violence against women and girls cases 20:21 AI's role in identifying coercive control and domestic abuse patterns 21:12 Future plans for expanding AI use in police disclosures and protective orders 24:26 Governance and ethical safeguards for AI in policing 26:50 Building public trust and engagement with AI tools

    AI Tools Drive New Crimes and Social Behaviours, and Policing Is Behind
See All (70)

Trailer

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
    70
  • Rating
    Clean
  • Copyright
    © Professor Ashley Braganza
  • Show Website
    The AI Adoption Podcast

You Might Also Like