All Posts

By Professor Ashley Braganza Public sector AI has a productivity problem before it has a technology problem. Greater productivity can come from creating more value with existing resources or consuming fewer resources to maintain existing outputs. Those routes can produce very different public services. AI makes both possible. The risk is that the second is easier to measure. A reduction in expenditure appears in accounts relatively quickly. The value of redesigned services, g

By Professor Ashley Braganza Human in the loop and AI in the loop are the phrases leaders reach for to sound careful about AI. Both smuggle in an assumption worth challenging: that the human and the machine sit in separate places, and the only question is where to put the checkpoint between them. Kerri O'Neill puts it more sharply than most. The more we imagine humans over here and AI over there, she argues, the more risk we take on. On this account the loop is not a safeguar

By Professor Ashley Braganza An officer reaches a domestic abuse call in the early hours. The accounts do not agree. The incident may be isolated, or it may be the visible edge of something sustained. Deciding which, under pressure and with partial information, is among the harder judgements in policing, and the officer making it may not know which hidden crimes a single incident tends to signal. The claim I want to examine is that an AI agent can now surface that pattern and

By Professor Ashley Braganza A firm can now delegate the bulk of its production to software and keep only its senior people. The pyramid that defined professional services, a thin layer of partners resting on a broad base of juniors, can be inverted. The junior base becomes a fleet of agents. This is not a forecast. It is an operating model already running, and it exposes a constraint that no amount of compute removes. The limit on agentic work is not the machine. It is the p

By Professor Ashley Braganza Expertise is losing its scarcity value. For decades, careers in large organisations were built on depth: know one thing better than the people around you, and the organisation would find a place for you. That calculation has inverted. Knowledge has become a commodity, and an individual’s knowledge is uncapped by the agents they can mobilise. The professional advice that shaped a generation of managers, specialise early and go deep, may well be the

By Professor Ashley Braganza Sovereignty, trust and context are usually discussed as separate concerns. They are not. A bank cannot earn a customer’s trust without understanding that customer’s context, and it cannot own that context if the data and the intelligence layer beneath it are rented from someone else. Pull the three apart and each one weakens. Hold them together and they reinforce, which is the harder and more valuable discipline. This is the shift AI forces on fin

By Professor Ashley Braganza Cost cutting in customer service ended in 30% pay rises. That is not a projection. Ed Thompson, Senior Vice President for Market Strategy at Salesforce, has followed the first cohort of organisations that put AI agents live in customer service in spring 2025. By the autumn, those same organisations were paying the staff who remained 30% above the market rate to stop them walking. The business case promised headcount reduction. The outcome was a sm

The bot that keeps you trapped in a loop, refusing to pass you to a person, is not broken. It is doing its job. For twenty years, customer service has been run on a metric with an ugly name, deflection, and its single purpose is to stop you reaching a human. Adding AI to that goal has given us the experience many of us now dread. The failure is not technical. It is an incentive, and until leaders name it, more capable AI will simply deliver a more sophisticated way of pushing

The phrase has hardened into orthodoxy. AI with humans in the loop. It appears in strategy decks, vendor pitches and government white papers, and it sounds reassuring, as though the human has been safely accounted for. André Lacroix thinks it has the whole thing backwards. In his telling, putting AI at the centre and humans at the edge is precisely the error leaders are about to make at scale. The future he describes is the reverse: purpose-based, people-centric leadership wi

Organisations are spending more on artificial intelligence than at any point in history. At the same time, industry surveys consistently show that large-scale AI projects are failing to deliver the returns that were promised. In the payments industry, where the entire business model is built on keeping volume, reducing unit cost, and increasing revenue in careful balance, the gap between investment and outcome is not just a strategic embarrassment. It is an existential risk.

Taiwan assembles more than 95% of the world’s AI servers. Its businesses rank approximately 20th globally in AI adoption. More than 70% of Taiwanese companies have not integrated AI into their operations. Business leaders and policy makers need to note that proximity to AI infrastructure is not the same as AI readiness to adopt. I spoke recently with Sega Cheng, Co-Founder and Chairman of iKala, a Taiwan-based company that has been building AI solutions for enterprise clients

The future with AI was the theme. The setting was Brunel University of London's Research Festival 2026, the university's 60th anniversary, and the occasion was the first live, livestreamed episode of The AI Adoption Podcast. Five panellists joined me to discuss five topics: opportunities and risks, societal readiness, the agentic organisation, responsible and regulated AI, and the impact on people and jobs. Zahra Bahrololoumi CBE, President and CEO of Salesforce UK and Irelan

The number of AI pilots an organisation has run tells you almost nothing about whether it is transforming. Neither does the number of use cases deployed, the sophistication of the interface, or the size of the AI team. These are activity metrics. They feel like progress because they are visible, countable, and easy to report upward. The organisations that are genuinely ahead are not the ones with the most activity. They are the ones that have asked harder questions: does this

Every AI deployment decision your organisation makes should start from one uncomfortable fact: the AI system you are investing in cannot learn from its work inside your business. It is trained on data compiled before it was deployed. It is then fixed. It does not adjust its understanding as your context changes, as your market shifts, or as new information accumulates. A human employee hired today will be a different and more capable employee in two years. The AI system you d

The systems we build carry the history of the data we feed into them. That is not a design flaw waiting to be patched. It is a structural reality that every leader deploying AI in their organisation needs to understand. Facial recognition that fails to identify Black women accurately, a clinical tool derived from measurements of 2,000 white men, policing algorithms built on decades of racially skewed stop-and-search records: these are not isolated examp les of technical imper

Tom Parker: Journalist and Podcast host The most valuable AI resource in the United Kingdom is not a chip, a model, or a data centre. It is a database that took 75 years to build: cradle-to-grave health records on every person who has ever used the NHS. No other country in the world holds anything like it. And right now, Britain does not have a domestic AI company capable of using it responsibly. That is not a technology problem. It is a strategic emergency. In a recent conve

Eaun Blair, Founder and CEO of Multiverse Boards are approving AI budgets. Chief executives are citing AI in every earnings call. Productivity targets are being set. And yet, across most organisations, the gap between what AI can do and what the workforce can actually deliver with it keeps growing. The bottleneck is not the technology. It never was. I had a conversation recently on The AI Adoption Podcast with Euan Blair, Founder and CEO of Multiverse, an organisation that wo

The entertainment industry has operated on a linear logic for decades. You write the story. You make the film. You license the merchandise. You build the theme park. Each stage follows the last, and at every step, access is controlled by a small number of powerful institutions with large amounts of capital. That model is not under pressure but is it beginning to fail? I spoke recently with Samantha Tauber, founder of VNCCII, a science futures franchise she is building entirel

One of the most striking insights from my recent conversation with Janusz Marecki was deceptively simple. AI systems do not just make mistakes. They accumulate them. This is not a surface-level flaw. It sits at the core of how large language models operate. Each word, or token, is generated based on probability rather than certainty. That means every step introduces a small chance of error. On its own, that error is negligible. Over hundreds of tokens, it compounds. The resul

Artificial intelligence is moving quickly from experimentation to everyday business use. Boards are investing heavily in AI in pursuit of productivity gains, competitive advantage and new forms of automation. Yet a growing body of research suggests that governance is struggling to keep pace with adoption. In short, many organisations are sleepwalking into a governance gap. Recent industry research highlights the scale of the issue. A report from TechUK found that although man