Three shifts UK leaders should make in the next twelve months.
Agentic AI presents UK enterprises with a new challenge as it introduces actors inside the business that need managers, rather than engineers. The deciding factor for success over the next twelve months will therefore not be solely technical capability, but whether the organisation is ready to supervise an actor it did not hire.
Agentic AI, the category of system that plans and executes multi-step actions with limited human intervention, removes that decision point, resulting in the same drift playing out inside an autonomous workflow rather than at its boundary. Whereas a drifting prediction shows up as a wrong number on a report, a drifting agent shows up as a wrong action taken at speed, potentially repeated across thousands of interactions and all before anyone realises the workflow has changed.
Introducing the readiness model: evaluation, ownership, control
Ownership is who manages the actor and who is accountable for what it does.
Control is what authority the actor has and how that authority is removed.
Existing governance frameworks treat these as separate disciplines, but for agentic AI systems they collectively describe how an organisation governs a worker it did not hire.
Shift 1: evaluation moves from procurement to continuous, and from engineering to product
Most enterprises benchmark suppliers and components at procurement, then never again. Fortunately, when it comes to agents, continuous production evaluation patterns are already available, such as graders and LLM-as-a-judge, and deterministic guardrails can wrap non-deterministic decisions.
The harder question is who owns them. For a customer support agent, accuracy is important, but tone, compliance posture, and the escalation decision all matter equally, and none are technical judgements.
The board-level question is: who in your organisation signs off the escalation threshold for an agent that handles regulated customer interactions?
If the answer is the technology function, you are likely still treating the agent as a tool. Product should define what good looks like. Risk should define what acceptable failure looks like. And Engineering should instrument each workflow step.
Shift 2: ownership moves from functional silos to cross-functional accountability
Agents collapse the boundaries between functions, so ownership must collapse too. An agent touches data, application logic, downstream services and the business workflow it sits inside, and the team that owns it must oversee all four.
This is an operating model decision, not an engineering restructure. UK leaders should stand up cross-functional squads with end-to-end accountability for the agent.
The board-level question is: who in your organisation is the agent's manager?
If the answer is more than one name, or no name, ownership is incomplete.
Shift 3: control moves from approval gate to runtime authority
Traditional technology change management assumes the change is the code. For agents, change occurs every time the model is updated, the prompt is updated, a question is posed by the user, or an upstream data source drifts. Controls must therefore extend into operating at runtime:
Detection - something is changing.
Containment - stop the change from spreading.
Reversal - return the system to a known good state.
Cost control - keep the runtime change inside the original business case.
Together these are the equivalent of how an organisation supervises any other worker: it watches what they do, limits where they can act, can stand them down, and controls how much of the budget they spend.
The board-level question is: if a new hire started changing your customer-facing language without telling anyone, you would notice immediately. Would you notice if an agent did?
These three questions aggregate into a single readiness test. If organisations can answer them with a threshold, a name, and a runtime control, they are ready to put agents into production. If they cannot, the gap is organisational, rather than technical.
What the regulators are saying
Cross-sector UK regulators are converging on the same diagnosis. The Digital Regulation Cooperation Forum, comprising the CMA, FCA, ICO and Ofcom, identified fragmented accountability as one of the top emerging concerns for agentic systems across financial services, consumer markets, communications and data protection.
The regulators are asking the supervision question because the technology has produced a worker that existing supervision regimes were never designed to govern.
What readiness actually looks like
It’s clear the task is harder than buying and deploying technology. Evaluation owned by product and risk, ownership held by a single accountable squad, runtime controls authorised by the board – these are the structures of supervision, not of technology deployment.
If your organisation cannot define the threshold, name the manager, and enforce runtime controls, it is not ready for agentic AI. The UK organisations that lead the next twelve months will not be the ones with the best models or engineers; they will be the ones that learned how to manage a worker they did not hire.
David Sugden
Head of AI & Engineering, Axiologik
David Sugden
Head of AI & Engineering, Axiologik
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Derek Thompson, Senior Vice President and GM, EMEA at Workato, shares his insights on how agentic AI is at risk of becoming a 'side project' for many, without intelligent and ambitious enterprise integration.
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Kir Nuthi is the Head of AI and Data at techUK.
She holds over seven years of Government Affairs and Tech Policy experience in the US and UK. Kir previously headed up the regulatory portfolio at a UK advocacy group for tech startups and held various public affairs in US tech policy. All involved policy research and campaigns on competition, artificial intelligence, access to data, and pro-innovation regulation.
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Usman joined techUK in January 2024 as Programme Manager for Artificial Intelligence.
He leads techUK’s AI Adoption programme, supporting members of all sizes and sectors in adopting AI at scale. His work involves identifying barriers to adoption, exploring solutions, and helping to unlock AI’s transformative potential, particularly its benefits for people, the economy, society, and the planet. He is also committed to advancing the UK’s AI sector and ensuring the UK remains a global leader in AI by working closely with techUK members, the UK Government, regulators, and devolved and local authorities.
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Sue leads techUK's Technology and Innovation work. This includes work programmes on AI, Cloud, Data, Quantum, Semiconductors, Digital ID and Digital ethics as well as emerging and transformative technologies and innovation policy. In 2025, Sue was honoured with an Order of the British Empire (OBE) for services to the Technology Industry in the New Year Honours List. She has also been recognised as one of the most influential people in UK tech by Computer Weekly's UKtech50 Longlist and was inducted into the Computer Weekly Most Influential Women in UK Tech Hall of Fame.
A key influencer in driving forward the tech agenda in the UK, in December 2025 Sue was appointed to the UK Government’s Women in Tech Taskforce by the Technology Secretary of State. She also sits on the UK Government’s Smart Data Council, Satellite Applications Catapult Advisory Group, Bank of England’s AI Consortium and BSI’s Digital Strategic Advisory Group. Previously, Sue was a member of the Independent Future of Compute Review and co-chaired the National Data Strategy Forum. As well as being recognised in the UK's Big Data 100 and the Global Top 100 Data Visionaries in 2020, Sue has been shortlisted for the Milton Keynes Women Leaders Awards and has been a judge for the Loebner Prize in AI, the UK Tech 50 and annual UK Cloud Awards. She is a regular industry speaker on issues including AI ethics, data protection and cyber security.
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