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By Professor Ashley Braganza An organisation can add AI agents and end up with clutter instead of capability. An agent for underwriting, another for finance and a third for one team's knowledge work can each pass a proof of concept and leave the organisation no more capable than before. When those agents sit on top of fragmented processes and dysfunctional data, they add a further layer of digital clutter. Use cases, pilots and agents built by individuals or departments are a

By Professor Ashley Braganza Many boards can explain how they make money, but not their data quality. Ask a leadership team to map its revenue sources and it will have a good attempt. Ask it to sketch the organisation’s structure and the people who matter, and it will manage that too. Ask it to describe the organisation’s data architecture, and the reply is a question of its own: why do I need to know that? That reply says more about an organisation’s readiness for AI than an

By Professor Ashley Braganza A hydrant located on an electron map is old technology; asking AI about it is not. Utilities, farmers, city planners and energy companies have recorded their infrastructure assets such as pipes, valves and grid connections in mapping software for decades. The software is mature. The change is AI that lets a manager put a plain question to that map and receive a ranked answer in a chat window. That change widens access to critical infrastructure da

By Professor Ashley Braganza The Home Secretary wants policing in England and Wales to become "the most AI enabled police force in the world". Policing in England and Wales is not one organisation. It is 43 operationally independent forces, each with its own technology, its own appetite for risk, its own culture and its own way of working. Adopting AI inside a single organisation is demanding enough. Doing it across 43, while demand rises, crimes grow more complex and some ne

By Professor Ashley Braganza A bank can hold impeccable AI principles and still fail to convince the customer standing at the counter about the bank’s AI adoption. The distance between a governance framework written for regulators and a customer's simple question, is this safe, is where trust in AI is won or lost. In financial services that distance is the whole game, because a bank's stock in trade is not money, it is trust. Govern AI as a compliance document and you protect

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