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.

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 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

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 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

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 roll-outs, 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

Jonathon Palmer from LinkedIn and I spoke about creativity emerging as a serious competitive advantage in AI-driven business. Jonathon made a striking point: AI makes execution easier, so the advantage shifts to the human ability to originate ideas, reframe problems, and stay curious.

The discussion also challenged a common assumption in B2B ecosystems. Emotion carries more weight than logic in real decisions. People engage Brands that help people feel confident in their choice will stand out in a world shaped by AI abundance, endless options, and fast decisions.

We covered the changing landscape of work. A shift is underway from job title-based hiring to skills-based hiring. Jonathon shared LinkedIn data suggesting professionals entering work today will hold twice as many jobs as someone starting 15 years earlier. As AI adoption expands, careers adaptability and lifelong learning move from a nice-to-have to a baseline expectation.

A theme running through the conversation was shared responsibility. Creativity no longer sits in a single department. It becomes everyone’s work, supported by psychological safety, rigorous experimentation, and high standards.

This episode brings together creativity, emotional intelligence, confidence in brand decisions, skills-first careers, and the real tactics people can use to stay visible and employable as AI reshapes work and workplaces.

Chapters

00:00 The Importance of Creativity in Business

09:54 The New Marketing Landscape

20:05 The Evolution of Brand Identity

29:59 Adapting to the Future of Work

As 2025 ends, thanks to everyone who joined The AI Adoption Podcast. A recurring message is: AI adoption isn't a technology issue alone; it is a strategic transformation, leadership, and human challenge.

Regulation & Trust

• Regulation isn't the enemy of innovation Lord Tim Clement-Jones

• AI adoption only works at scale when regulation is coherent, trusted, and designed to enable innovation, not fight it Kate Jones

• Blindly adopting technology you don’t fully understand is dangerous Sunaina Aytan

• The danger is trying to legislate something you don’t fully understand Konrad Shek

• The UK’s light-touch approach supports innovation but risks leaving safety to those least equipped to manage it Dr Madeline Cheah (谢涵馨)

Leadership & Readiness

• Board members must think about productivity of their human workforce alongside their digital workforce Zahra Bahrololoumi CBE

• AI readiness depends on leadership, team culture, data and documented operations Andrew McNally

• AI tools are useless if not integrated into an effective business strategy Matthew Houlihan

• AI isn’t a tech problem: it’s a human problem ⚡ Ben Johnson

Work & Change

• You can optimise a process with AI, but you can't automate a relationship Neha Kabra

• People fear losing jobs, but many AI projects fail because companies ignore change management Heather Black

• The business case behind AI technology is human displacement Ian Smith

• Agentic workflows create space for humans to focus on meaningful, rewarding tasks Paul O'Sullivan

Data & Truth

• There’s no AI model that doesn’t hallucinate Hamzah Malik

• If our civilisation lets go of a notion of truth, we will be in fantasy land Lord Bishop Steven Croft

• We risk AI model integrity if we don’t protect human-created data Allison Gardner MP

• Everyone wants better AI outputs, no one wants to fund the inputs Alex Dalman

Adoption at Scale

• AI's transforming shipping, agriculture, and professional services, it’s happening now Marco Forgione MCIEx

• We spent £113m across the NHS, but pilots don’t always lead to adoption Alex Cole

• Some organisations are waiting to see what others do, the longer they wait, the further behind they get Jenna Shanks

• Whatever model you used last month is already outdated Nikolay Burlutskiy

Looking ahead

• We’re in the first wave of adoption. The next wave's transform businesses, then create new ones Lord Kulveer Ranger

• Managers need to manage agentic AI that’s in their team Sue Daley OBE

• If we can build machines that learn, adapt and act, we can learn to become more human Maggie Sarfo

• In an agentic economy, companies, not big tech, are in the driver’s seat Yves Bollinger

Thanks to guests, listeners, and colleagues at Brunel Business School and the Centre for AI Social and Digital Innovations Brunel University London, with special thanks to Vimal Dalal and Eliza Kania.

The conversation continues in 2026.

Wishing you joy at Christmas and every success in 2026.

Ashley