The Business of AI

AI adoption is soaring, so why are half of users saving no time at all? Deloitte’s new GenAI Workforce survey of 25,000 UK workers exposes a growing gap between access to generative AI and meaningful business impact. While 63% of workers have used AI at work, only 12% use it multiple times a day. The result is a workforce of AI “dabblers”, where headline adoption figures can mask limited changes to how work actual...

What happens to consulting when AI can do in days what teams of consultants used to spend months delivering? Neil argues that AI is rapidly collapsing the cost and time of traditional software and services, forcing businesses to rethink where value actually sits. Instead of starting with models, agents or productivity targets, companies should focus relentlessly on measurable outcomes: revenue growth, lower costs and reduced risk. ...

Are AI data centres really about to consume Britain’s power and water, or are the headline numbers hiding a very different story? Spencer argues that some of the biggest assumptions about AI infrastructure need challenging. While around 80GW of data-centre power requests have reportedly entered the UK connection queue, he estimates genuine AI-related demand could be closer to 10GW over the next five to ten years. He also expl...

This week Tim gets a ride at the buzzy Uber x Wayve robotaxi launch in London ("AI on wheels" and, "a real milestone for the British AI industry"), digs into the £100m government fund backing British AI startups, unpacks the Matt Clifford-to-Anthropic conflict-of-interest row, pushes back on calls for a data centre moratorium, and covers Nvidia's $12.9bn Hugging Face buy, new AI "kill switch" bills in the UK and US, and Matt Lucas ...

Why does AI-generated code so often look sloppy, even when it works? Kevin Coyle, founder of Drutek, joins the podcast to explain why the missing ingredient isn't better prompts, it's context. He breaks down how design systems give AI tools the grounding to produce consistent, on-brand results, and shares his "harvest" framework for getting every employee, not just developers, involved in building with AI.

The conversation t...

What if every small business could access the same financial expertise as a global enterprise? This conversation with Daniel Callis from Beyla AI explores how AI is making that possible. Beyla is building a conversational platform that acts like a virtual CFO. From cash flow forecasting to personalised business guidance, the discussion highlights how AI can help founders make smarter decisions without needing a large finance team.<...

AI transformation isn't a technology challenge, it's a leadership challenge. This conversation explores why the biggest barriers to AI adoption are human, not technical, from fear and shame to curiosity, humility, and the willingness to learn. Kirsty Mac from Illuminate AI shares how coaching leaders to embrace uncertainty creates the culture needed for meaningful AI transformation.

The discussion also looks at how organisat...

Social media has become exceptionally good at rewarding attention and dangerously poor at rewarding social value. This episode explores a different model: using AI to amplify credible voices, strengthen communities and direct recognition toward people creating real-world impact. The business opportunity is bigger than building another platform. It is about redesigning the incentives behind digital participation, citizen journalism ...

AI’s biggest opportunity is not simply cutting costs or accelerating output. It is improving people’s lives without leaving anyone behind. Jenny Garrett argues that leaders need to move beyond narrow productivity metrics and ask who benefits, who is excluded, and how AI can augment human potential. Drawing on examples in healthcare, education, refugee support and digital inclusion, she shows how equitable AI can shift t...

AI will not replace creativity, but it could reshape who gets to create, whose work is protected and which stories reach the market. Tanya argues that the real business challenge is not whether creative industries adopt AI, but who sets the rules. Used well, AI can lower production barriers, help independent creators build on their own terms and unlock new formats. Used without consent or representation, it risks concentrating powe...

AI adoption will not create value until organisations can answer three questions: where is it changing workflows, what measurable value is it producing, and who is accountable? Tram argues that finance must move beyond disconnected pilots towards shared use cases, practical capability-building and responsible implementation. Initiatives such as AI sandboxes, industry academies and an AI observatory can help firms learn faster, comp...

Most AI pilots do not fail because the technology is weak. They fail because nobody truly owns the operational problem, incentives reward the status quo, data is fragmented and insights never reach the people empowered to act. The critical lesson is to start with a measurable business problem, design the workflow around action and define the route to scale before the pilot begins. A demonstration proves that AI can work. A transfor...

Most AI programmes fail because companies teach tools instead of changing how work gets done. Real AI literacy is not “buttonology”; it is the confidence, judgement and curiosity to rethink problems, redesign workflows and experiment safely. Leaders must go further than frontline users. Their role is to challenge legacy processes, remove organisational blockers and create the conditions for adoption. Simply layering AI ...

AI should work like autopilot: powerful enough to accelerate progress, but never trusted so blindly that people lose the ability to take control. The biggest risk is not the technology itself. It is cognitive offloading, weak governance and leaders pursuing efficiency without a clear view of the value they may destroy. Successful AI adoption requires strong human judgement, reliable data and a strategy that connects every departmen...

AI spending is rising faster than the evidence that it works. The real problem is not lack of ambition, but lack of observability: companies see the cloud bills immediately, while productivity gains remain hypothetical. The episode argues that businesses need to treat AI less like a hype-led experiment and more like a value-tracked investment, with clear outcomes, governance, and ROI measures built in before pilots begin.

Sa...

Most AI adoption fails because businesses force employees into yet another system. The bigger opportunity is to bring trusted intelligence into familiar workflows, especially email, so people can access business data without learning new dashboards, navigating disconnected platforms or relying blindly on generative AI. The commercial value lies in reducing friction, widening access and turning siloed information into faster, more c...

AI transformation is the wrong goal. Businesses should be building for continuous adaptation. New tools will keep emerging, operating conditions will keep shifting, and there may never be a stable “after” state. The organisations that win will be those that understand how work actually gets done, identify where AI can create measurable value, and redesign their operating models without sacrificing effectiveness for spee...

AI could become the most powerful workplace adjustment ever created—or another system that quietly excludes millions. The difference comes down to design. Accessibility cannot be retrofitted after launch or treated as a compliance exercise. With one in four people in the UK living with a disability, inclusive AI is not a niche concern. It is a mainstream product, workforce and commercial issue.

Rachel, an independent A...

AI agents will not transform productivity if employees must supervise dozens of disconnected tools. The real opportunity is orchestration: one intelligent interface that coordinates tasks, people, systems and deadlines while keeping every action visible and auditable. That requires more than automation. It demands persistent reasoning across time, clear ownership and rigorous quality controls so agents know when to act, when to fol...