The AI Adoption Podcast

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 rethoughtListen 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.Chapters00:00 Holistic Value in AI Adoption02:40 The Rise of Agentic AI06:36 Understanding Deterministic vs Non-Deterministic Approaches08:14 Gradual or Radical Transformation in AI Implementation09:48 Governance and Strategic Planning for AI11:43 Managing AI as a Portfolio13:14 Collaboration Between Humans and AI Agents16:07 Cultural Shifts in Organisations with AI18:53 AI Literacy for the Next Generation22:22 Balancing AI Automation and Learning Opportunities

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 advantageThis 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.Chapters00:00 The Importance of Transparency in Leadership03:16 Global Perspectives on AI Development07:53 AI as a Threat and Opportunity08:39 Challenges and Opportunities in Scaling AI12:39 Cultural Influences on AI Adoption14:44 The Need for a Unified AI Strategy17:56 Coordination in Decentralised Implementation19:23 Leadership Priorities in AI Adoption23:57 Transforming Business with AI26:52 Zurich's AI Strategy: AI 360

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.Chapters00:00 Introducing PoliceAI and the case for a national centre05:13 Scaling AI across 43 independent forces07:03 Pathfinder forces and the unglamorous paperwork10:00 Culture, ease of use and the six million hours target14:05 Roles, jobs and time back for officers16:39 Data, governance and consistent standards21:51 Public trust and the AI registry25:02 The impact on victims and society27:12 Acting early and the risk of unaffordable AI31:18 The victim comes first

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.Chapters00:00 The dual nature of AI: risks and benefits02:00 Responsible AI in banking03:09 Governance of agentic AI in financial services08:29 Aligning with the EU AI Act12:06 Risk assessment and categorisation in AI15:16 Cross-jurisdictional governance challenges17:30 The importance of trust in AI governance20:19 AI literacy: building a knowledgeable workforce22:33 Persona-based training for AI governance24:46 The role of AI ethics councils29:11 Balancing business needs and ethical governance

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