In this episode of The AI Adoption Podcast, I speak with Sahar Danesh from the British Standards Institution about the role that standards play in the adoption and governance of artificial intelligence. Many organisations are racing to adopt AI. Productivity gains and competitive pressure are driving large and rapid investments. Yet this conversation revealed a striking gap between adoption and governance. Sahar shared findings from a BSI study showing that fewer than a quarter of organisations reported having a formal AI governance programme. Many employees are already using AI tools without formal oversight. Only a small proportion of organisations have clear processes governing confidential data used for AI training. This creates a growing governance gap. As Sahar says: Businesses are investing heavily in AI without always considering the risks they are opening themselves up to. Standards exist to address precisely this challenge. International AI standards such as ISO/IEC 42001 provide guidance for responsible use of AI and support transparency in decision making. These standards are already being adopted globally, yet many organisations still view standards as bureaucracy rather than operational tools. Sahar argued that this perception is mistaken. Standards are not barriers to innovation. They provide the common language that enables organisations to collaborate across borders and operate confidently within global supply chains and strong ecosystems. She also highlighted a deeper issue. AI governance is often treated as a technical responsibility, yet AI affects every department within an organisation. Governance frameworks therefore require engagement across the business together with training and AI literacy. One warning stood out clearly in our discussion. Organisations risk sleepwalking into a governance gap if enthusiasm for AI outpaces oversight. According to Sahar, standards provide a practical route for organisations to close that gap while building trust with customers, regulators and employees. This conversation offers a valuable perspective on AI governance at a time when organisations are investing heavily in AI but still searching for structures that ensure it is used responsibly and ethically. Chapters 00:00 The Role of AI in Business Productivity 03:33 BSI's Contribution to AI Standards 06:39 Global AI Regulations 09:27 Balancing Business Needs and Consumer Protection 11:52 The Importance of AI Governance 13:50 Standards vs. Innovation: A Misconception 16:02 The AI Standards Hub and Its Impact 18:39 Integrating Standards into Governance Frameworks 24:28 Practical Advice for AI Governance

This is for anyone recruiting or looking for a job or a new role. In this episode of The AI Adoption Podcast, I speak with Annabel Ashley from Indeed about AI adoption where it matters most, inside day-to-day operations at the sharp end of hiring. Indeed is already an AI-enabled organisation. Annabel shared that over 70% of Indeedians use AI at least weekly, and over 80% say it adds value to their working day. They have a ‘function first’ approach, as teams adopt tools that fit their work, rather than a single top-down rollout. In her role in government relations, AI helps Annabel track legislation and compare timelines quickly. Engineers use coding assistants and reported saving around four hours a week. Customer service teams use AI to build product mock-ups and create before and after performance summaries for clients, even though they do not see themselves as technical. We discussed the recruitment process itself. Annabel’s view is that recruiters needs to move away from CV-based hiring towards skills-led, capability-based recruiting. That shift tackles the growing problem of candidates using AI to polish applications while employers use AI to screen them. A skills-first model reduces the risk that good candidates are filtered out because of language, formatting, or miscommunication. On responsible AI, Annabel described Indeed’s principles, including jobseeker-first, fairness and equality, hiring as human, and innovating responsibly. The operational point was ‘human in the loop’, keeping judgement with people, supported by governance that adapts with ethics, regulation, and technology. We closed on future skills. Annabel emphasised communication, problem solving, and emotional intelligence, plus a mindset of lifelong learning. The argument was practical: AI may automate routine tasks, yet the advantage stays with people who can adapt and work well with others. Listen to the episode for an in-depth view of AI in hiring, with the labour market context Indeed sees in real time. Chapters 00:00 AI's Impact on Employment and Skills 03:11 Indeed's AI Integration and Employee Experience 07:08 AI Efficiency vs. Human Creativity 08:22 AI in Hiring: Streamlining the Process 10:17 Maintaining Human Connection in AI Hiring 11:57 Ethics and Transparency in AI Use 15:35 Transformations in Work Practices 18:25 Future Job Market Dynamics 21:58 The Role of Agentic AI 23:30 Shifting from CVs to Skills-Based Hiring 25:12 Essential Skills for Future Employment 27:52 The Value of a Diverse Workforce

In this episode of the AI Adoption Podcast, I speak with Chelsea Chamberlain, Chief Technology Officer at @Roc Technologies, about to get an in-depth understanding of AI adoption across the UK public and private sectors. Chelsea sees a clear divergence in pace. Private sector firms in sectors such as banking, legal and pharmaceuticals are accelerating. Public sector organisations, on the other hand, remain constrained by cost, cyber risk and the need to demonstrate short term savings. Yet when public bodies do invest, they are often more disciplined and focused because they cannot afford experimentation or, as she says, throw AI spaghetti at a wall and hope something sticks. A recurring theme in organisation coming to AI is fear rather than resistance. CIOs don’t want to commit scarce funds to AI tools that could be redundant in months. Customers are cautious about long term managed service contracts because few believe the current answer will be the final answer. Service desks are evolving. AI is improving root cause analysis and first line troubleshooting, yet the lack of interoperability between vendor tools means human accountability still matters. As agentic AI and open standards mature, that balance may shift in favour of AI and the need for fewer people. Boards, in Chelsea’s view, are not yet treating AI as a board level mandate. Instead of reshaping operating models, many organisations are layering tools onto existing processes. That approach risks missing the structural shift AI is likely to drive. We also discuss sovereign AI, geopolitical risk, the UK’s position relative to global tech powerhouses, and the pressure on data centres and energy infrastructure. This is a grounded discussion about risk, cost, capability and leadership. Chapters 00:00 Roc Technologies and AI 07:04 The Role of IT Service Providers in AI Adoption 09:59 The Challenges of AI Implementation 11:18 The Future of Service Desks in an AI World 13:36 Are Boards Ready for AI Changes? 15:28 Preparing Boards for AI Transformation 17:51 Organisations' Ability to Absorb AI Technologies 21:54 Sovereign AI and Geopolitical Risks 25:17 Energy Consumption and Data Centres

AI “of the people, by the people, for the people”. I’ve taken the words from the closing lines of Lincoln’s Gettysburg Address to describe my discussion with Josh Tan from Public AI. Josh argues that AI should be treated as public infrastructure, closer to railways, highways, libraries, utilities, and the internet than to a premium service owned by a handful of companies. The context is stark. Two or three firms dominate the global consumer chatbot market. These systems are becoming critical for industry and social culture. The issue is power. A small number of companies have gained not only commercial concentration, they have significant cultural concentration. British culture, Swiss culture, or Swedish culture is increasingly mediated through models built and controlled elsewhere. This leaves local language, norms, and identity at risk: to be shaped by external defaults. Josh points to Switzerland’s Apertus model as an example of a sovereign model designed to reflect multilingual and culturally specific patterns, including Swiss German. Governments are investing heavily in data infrastructure, compute, and model development for public benefit. Public AI aims to provide a public alternative in what is rapidly becoming not simply an AI market but an AI oligopoly. Ethics, responsibility and accountability on the part of AI providers matter as much as model performance. Geopolitical tension is accelerating interest in sovereign and public AI. As dependencies become visible, country autonomy in critical technology becomes a strategic priority. This episode explores whether AI can genuinely be built and governed for the benefit of citizens rather than concentrated private power. Chapters 00:00 The Concept of Public AI 02:59 Global Models of Public AI 05:56 The Importance of Public AI 09:02 Public AI vs Open Source AI 11:49 The Flywheel Effect in AI Development 14:54 Sovereign AI and Public AI 17:23 The Role of Public AI in Cultural Preservation 20:27 Challenges of Sovereign AI 23:15 The Future of Public AI

AI adoption in financial and professional services is being sold as a technology story. Olivia Larkin makes the case that it is really a workforce transformation story. In her words, ‘AI transformation is change management and it needs to be managed in that way by a business.’ Olivia leads skills and workforce policy at the City of London Corporation. She works across the UK, not just the Square Mile. She makes an interesting distinction between AI talent, a narrow, deep group that builds, governs, assures, and improves AI. Whereas, AI skills are for the wider workforce, because AI use will spread across all functions in organisations. She argues that one of the biggest AI risks is not investing in upskilling. Only around one in five UK employees currently receive AI training. Time is the everyday blocker, for employers and employees. Protected learning time changes the picture. Regulation also slows momentum. Firms face uncertainty when existing regulatory requirements meet new AI use, with grey areas that create caution and delay. Olivia still lands on a positive position. With responsible governance, AI can lift productivity and improve customer experience. The prize comes with a condition. Focus on redeploying people, not reducing headcount. Build trust, so staff do not see learning as a route to replacement. London, in her view, can become a global reference point for trusted AI in financial and professional services, if workforce investment keeps pace with the technology. Chapters 00:00 The City of London Corporation 03:33 Skills and Workforce Policy in Financial Services 06:18 AI Skills vs. AI Talent 11:01 Challenges and Risks for Companies 17:09 Employee Challenges in AI Adoption 20:06 Regulatory Challenges in Financial Services 23:27 AI's Impact on London's Future 27:47 Successful AI Strategies in Organisations 30:42 Future Directions for AI Adoption

In this episode of The AI Adoption Podcast, I speak with Sydney MacGregor from HSBC Innovation Banking about a critically important aspect of AI adoption: funding, risk, and scale. Sydney works with more than 250 enterprise software companies, most of them building AI into their core products. She shares a clear view of the UK AI market, from the surge in early-stage activity to the harder reality of scaling beyond Series A. We discuss the rise of AI agents over chatbots, investor expectations shaped by rapid revenue growth, and the pressure founders face when competing with businesses scaling from zero to tens of millions in months. Sydney explains why later stage funding remains a structural weakness in the UK, even with strong research, talent, and early capital. The conversation moved onto venture debt, an area that often feels opaque to founders. Sydney explained how banks assess risk in AI businesses that can be cash-burning, the importance of investor syndicates, and the need to avoid over-leveraging while still enabling growth. We explored the role banks play supporting their AI businesses beyond capital, including global expansion, sector expertise, and trusted relationships. We close with a forward look at workflow automation, AI governance, and the conditions needed for UK AI companies to scale and stay in the UK. This episode is a rich discussion about growth, discipline, and realism in the AI economy. Chapters 00:00 The Future of AI in Corporate Workflows 03:05 Current State of AI Companies in the UK 07:08 Government Support and Investor Appetite 09:46 Investment Trends in AI Sub-sectors 12:10 Regional Investment Expansion Beyond London 14:16 Ensuring the UK Remains an AI Hub 15:58 Supporting AI Startups Beyond Capital 22:18 The Lifecycle of AI Startups 27:32 Key Trends and Future Opportunities in AI

Cash transactions are disappearing fast in the UK, but five million people still rely on cash payments. That tension sits at the heart of this episode. In my conversation with John Howells, Chief Executive of LINK, we explore the sharp fall in cash use, from six in ten payments a decade ago to fewer than one in ten today, and the consequences that follow. For most people, digital payments bring convenience and choice. For others, the elderly and those less comfortable with technology for instance, the pace of change creates real risk. John explains that cash infrastructure declines faster than people adapt. As usage falls by around ten per cent a year, ATMs, branches and cash accepting shops disappear unevenly. Banking hubs emerge as a practical response, shared branches on the high street that protect face to face access when the last local bank branch closes. We also look ahead to digital money. Stablecoins, tokenised bank deposits and central bank digital currencies point to a future where payments carry richer data and support smarter services. That future brings opportunity, including reduced fraud and more flexible support for vulnerable users, but only if inclusion is designed in. A striking argument in this episode is about responsibility. John suggests that banks should not carry the cost alone. Large technology firms such as Amazon, Meta and Google also benefit from the shift to digital payments and should contribute to sustaining the system that underpins it. This episode is a reminder that payment systems are part of the social infrastructure and community fabric, not just technology. Chapters 00:00 The Cash Dilemma: Understanding the Need for Cash in a Digital World 03:00 The Evolution of Payments: From Cash to Digital 06:07 The Vulnerable Population: Who Are the 5 Million Cash Users? 08:45 The Role of Technology: Bridging the Gap for Cash Users 11:58 Banking Hubs: A Solution for Access to Cash 14:52 The Future of Payments: Trends and Predictions 17:55 The Impact of Digital Payments on Society 20:49 The Role of AI in Financial Inclusion 23:50 The Multi-Money Approach: Stablecoins and Digital Currency 26:42 Preparing for the Future: What Companies Should Do

In this episode of The AI Adoption Podcast, I speak with Sumeet Bhatia from Cisco about the changing role of AI in defence and security. The discussion stays close to operational reality rather than abstract theory. We explore how AI now operates at a speed that humans cannot match. That speed allows threats to be detected earlier and systems to respond before damage spreads. At the same time, it creates new risks around trust and control. Sumeet explains that data purity, provenance, and governance are not technical hygiene issues but strategic and operational warfare necessities. Data integrity runs through the entire conversation. Poisoned data can quietly distort AI behaviour, while misinformation can undermine confidence in the information presented to human decision makers. In defence environments, that loss of confidence can be as damaging as the attack itself. Explainability becomes critical when decisions carry serious consequences. Opaque models make it difficult to understand how conclusions are reached. Without clear decision trails, accountability weakens and trust erodes under pressure. We also discuss the rise of multi-vector attacks. Disruption no longer arrives through a single route. Infrastructure, cyber systems, and information channels can all be targeted together. AI helps by recognising patterns across these fronts before instability escalates. The episode also looks at AI in operational contexts. Defence logistics faces challenges similar to large commercial supply chains. AI can support planning, predict failure points, and improve crisis response when conditions change quickly. Lessons from defence extend well beyond the military domain. The need for speed must be balanced with verification and human judgement. Policy continues to struggle to keep pace with technical capability and developments. Human oversight remains essential where lethal decisions are involved. Looking ahead, effective human–AI teaming will depend on new training approaches and a serious investment in skills. Chapters 00:00 The Rise of Autonomous Cyber Warfare 03:00 AI in Defence and Security 05:58 AI's Role in Threat Detection 08:57 Human-AI Collaboration in National Security 11:59 Data Integrity and Misinformation Challenges 14:50 The Explainability Crisis in AI 17:40 Multi-Vector Attacks and Their Implications 20:54 Operational Enhancements Through AI 23:59 Lessons from Defence for the Private Sector 27:02 Policy Considerations for AI in Defence 30:02 The Future of Human-AI Teaming

This episode with Chris Blatchford, CTO at Kingfisher, quickly became a conversation about leadership mindset and AI technology. Kingfisher is best known through its brands such as B&Q and Screwfix. Chris described an organisation that treats AI as a board-level capability. Foundations first, so teams can build safely and repeatedly rather than improvising. Kingfisher’s data lake, Nucleus, and its AI framework, Athena, sit underneath everything. That choice signals discipline and intent. AI becomes an operating model, not a sequence of use case pilots. From there, the focus shifts to customer focused innovations, personalisation and value, not superficial novelty. Customer-facing AI stays anchored in a simple outcome: customers reach the right product fast. Chris spoke about moving beyond keyword search towards conversational and semantic search, then using context such as prior searches, purchases and loyalty behaviour to sharpen relevance. He shared a hard-edged commercial marker too. Recommender systems contributed around 10% of e-commerce sales last year, with ambition to grow that through 2026. For boards, the supply chain segment will resonate. Chris described near real-time visibility at SKU level, including fulfilment timing and shipment progress, supporting faster decisions on replenishment and stock gaps. It is operational clarity in a world full of noise. Chris declared that ensuring Kingfisher rolled-out AI responsibly as one of the areas, alongside cybersecurity, that genuinely keeps him awake. Again, this reflects the depth of commitment to good AI governance, bias checks, explainability, controlled customer rollouts, plus tech-literate executives who press on risk as well as upside. Innovation is encouraged, within a bounded context with rules. His view on AI’s impact on jobs and skills was measured and human-centric. AI tools support colleagues rather than remove them, freeing time for higher value work. Roles evolve through better prompting, stronger curation of information, and sharper critical thinking, with data-savviness becoming essential. This episode is for boards and senior teams who want to scale AI adoption that holds up under scrutiny, delivers value, and earns trust. Chapters 00:00 AI in Retail 02:57 Kingfisher's AI Strategy and Innovations 06:08 Enhancing Customer Experience with AI 08:47 Dynamic Pricing and Its Implications 11:49 Supply Chain Visibility through AI 14:45 Responsible AI Implementation 17:55 Impact of AI on Jobs and Workforce 20:47 The Effects of AI on Retail Jobs 23:49 Skills for the Future Workforce 26:56 Supporting SMEs in AI Adoption 29:41 Advice for Large Organisations on AI Adoption