The AI Adoption Podcast

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.

The AI Adoption Podcast is a business podcast hosted by Professor Ashley Braganza, with 63 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Professor Ashley Braganza hosts The AI Adoption Podcast, a business show with 63 episodes published.

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  Episode #63

  Public sector AI faces a choice between innovation and cuts

  Aug 20, 202631 minS3

  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

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  Episode #62

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

  Aug 13, 202630 minS3

  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

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  Episode #61

  Every professional is an AI manager, whatever their job title

  Aug 5, 202639 minS1

  Stillpoint employs only senior experts and hands the rest to agents. There are no junior staff. The productivity this promises still meets a limit set by human beings. Chris Donahoe, Co-founder of Stillpoint, a corporate affairs firm built to be AI native, argues that the binding constraint on agentic work is now biological rather than technical. Chris explains that building an AI native firm is an exercise in destroying assumptions, not a technology project, and that many companies use AI merely to speed up an operating model that may no longer deserve to exist. He describes inverting the professional services pyramid: senior human talent only, an entirely agentic support layer, and delegation to teams of agents rather than junior colleagues. The gain has a ceiling, because human reading speed, attention, and judgment have not risen since 2022, so the volume agents produce backs up at the last mile of review. Chris also names a management shift, arguing that even a solo operator now runs a team of agents while no orchestration tool yet exists to coordinate them. • "It's really an exercise in destroying assumptions": the starting point is questioning why the business works the way it does. • The talent pyramid inverted: partner and managing director level humans only, no junior staff, an entirely agentic support layer. • The biological bottleneck: agents take work to ninety per cent done, but customer ready output depends on a person's finite hours and attention. • "Even solo operators are managers now": scoping, delegation, feedback, and judgment become baseline skills for every professional. • A candid tools verdict: Claude is strong at formatting Word, Excel, and PowerPoint, yet in Chris's view a poor copywriter, so he routes drafts through ChatGPT and back. Press play for an account of running a firm where the agents never tire and the people set the pace. Chapters 00:00 Building an AI native company: destroying assumptions 04:30 Encouraging experimentation with AI tools 07:07 Characteristics of an AI native firm versus traditional 08:13 Talent structure in AI native firms 10:28 Challenges of managing a human-AI hybrid organisation 12:54 The need for orchestration tools in AI workflows 13:54 Skills for managing in an AI native environment 17:03 Management and coordination of AI agents 19:35 Managing interdependencies and orchestration 23:43 Human biological bottleneck and work volume 27:40 Implications of human speed limits in AI workflows 34:04 Recruiting and developing talent for AI native organisations 37:28 AI native firms and future challenges

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  Episode #60

  Middle Management Has No Place in the Agentic AI Economy

  Jul 30, 202632 minS3

  Ten years in the same sales role now predicts AI resistance. Leo Rogers has seen the pattern often enough to name the mechanism: representatives who hit their number quarter after quarter were never questioned on their method, so the method was never examined. His verdict is that AI adoption is a people problem as much as a technology problem. Leo Rogers, co-founder and CEO of Curvo, which builds a real-time execution layer for sales teams, argues that knowledge has become a commodity and that the need for generalists is on the rise. He makes the case for skill stacking, holding roughly 30 per cent of several disciplines, enough to set strategy and to judge an output, with agents supplying the depth. He sets a limit on his own argument: a non-designer who commissions a prototype from an agent still cannot say whether the design system is robust. This means prior exposure remains the gate on quality. He describes orchestration as a generalised agent sitting above specialised agents, deciding which to mobilise and reconciling what they report back, and treats it as the change that makes materially more complex work possible. He also predicts an inversion of the labour market in which agents appoint humans for the last mile, on the grounds that an agent in a sandbox cannot shake a hand or fix a chair. ● The laggard profile: decades of steady growth on the SaaS wave, leadership motivation moving from growth to preservation of the founders’ / managements’ wealth, then private equity arrive with two or three years ‘to squeeze the lemon’. ● Resistance mapped to tenure: one year, three years, five years, and at ten years AI adoption becomes much more challenging, with reps coasting on their black books rather than the CRM. ● Robotics without the humanoid: a bricklaying machine with no legs working at the rate of five bricklayers, set against a humanoid taking half an hour to place a mug in a dishwasher. ● On jobs: Meta and other large technology firms cutting headcount, Leo’s view that organisations carry cost centres agents make redundant, and his warning that universal income becomes a real possibility if AI adaptation fails. ● On executives: checks and balances survive because agents lack empathy and their judgement is geared to be entirely commercial. Middle management, on his account, does not survive. Leo’s central claim is testable inside a week. Pick one function, ask whether the people in it hold 30 per cent of the disciplines their work touches, and the answer will tell you how ready the organisation is. Chapters 00:00 AI's Impact on Headcount and Organisational Fat 00:30 Skill Stacking and Cross-Functional Value 00:59 Training Programmes and Upskilling in Large Organisations 02:26 Orchestration of Specialised AI Agents 05:00 AI Adoption in the UK and Change Management 06:10 Resistance to AI Adoption and Entrenched Roles 08:00 Restructuring and Talent Shifts for AI Integration 09:20 AI's Effect on Jobs and Cost Rationalisation 10:46 Future Skills: Broader, Generalist Roles 12:16 Becoming a Human-AI Hybrid: Skills and Exposure 14:23 Critical Thinking and Human Oversight of Agents 16:56 Acquiring Experience in Large Organisations 17:56 Self-Directed Learning and Side Projects 20:12 Characteristics of Lagging Organisations 23:23 Robots and Robotics in Value Creation 25:45 Orchestration of Agentic AI and Marketplaces 28:02 The Human Role in a Future AI-Driven Workplace 30:44 Organisational Transformation and Change Management

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  Episode #59

  Sovereignty, Trust and Context Are Not Three Separate AI Problems

  Jul 23, 202631 minS3

  Sovereignty, trust and context are often addressed separately, yet there is an underlying interplay between them. Send the same prompt to the same large language model and two institutions receive the same answer, so the advantage that remains is the context a firm owns. That context is arguably the ground on which trust has to be earned, and it holds only if the data and intelligence layer beneath it are not outsourced. Peeyush Aggarwal, senior partner at Deloitte, makes the case that banks, and organisations across the public, private and third sectors, have to solve all three at once. He argues that the book of record captures what happened, while the intelligence lives in the why, the how and the who, much of which has never been written down. He shows that agentic AI, software that pursues goals and takes actions rather than answering a single query, breaks the settled architecture of transaction, engagement and analytics, while latency, predictable outcomes and cost remain unsolved. He warns that the real risk in a multi-agent system is not one agent failing but the interaction between them, where intent drifts and no one can trace who decided what. He treats the disintermediation of banks as an open question, with trust the deciding factor. • "You do not outsource your intelligence layer," a principle Peeyush applies to energy, compute, models and data alike. • Trust cannot be deployed like software; it is earned transaction by transaction, and a single agent taking a wrong turn or inappropriate action can lose it. • A customer who says "I lost my wallet" forces four actions at once: detect fraud, issue a new card, block the old one, and offer further protection. • Four pillars of a durable AI strategy: a choice of tools, a business metric, instrumented trust, and data and context at the heart. The episode hands leaders a test to apply before approving another agent: name the business metric it is meant to move, and the context it draws on. Press play. Chapters: 00:00 National sovereignty and AI infrastructure 05:54 Controlling the intelligence layer and infrastructure design 07:51 Data management and the data moat 09:26 Business model evolution with AI 11:37 Impact of AI on value creation in industries 13:17 AI readiness of software architecture 15:41 Challenges of deploying AI at scale 18:39 Consumer trust and AI agents 22:42 Disintermediation of banking and trust 24:06 Ecosystem emergence and innovation 25:50 Key ingredients for a successful AI strategy 29:30 Summary: Trust, context, and strategic pillars

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