For the past decade, UK boards have funded “digital transformation” with mixed results. Many initiatives improved efficiency, but few changed how organisations were actually led.
AI is different.
Enterprise AI has moved from pilots into core operations and decision-making. This raises a fundamental question: who in the C-suite owns value, risk and accountability in an AI-native organisation? And which leadership assumptions no longer hold?
Research from Deloitte, PwC, MIT Sloan and the World Economic Forum points to a consistent conclusion. Organisations extracting meaningful AI value do not centralise it under a single “tech owner”. They redesign decision rights across the C-suite, blending technology, finance, operations, risk and people leadership.
The UK sits slightly ahead of the global average on AI preparedness, helped by strong governance traditions and regulatory pressure. That does not guarantee better outcomes: enthusiasm is high, confidence uneven, and value realisation remains inconsistent, but it does change the sequencing. Organisations that embed decision rights, risk ownership and acceptable-use boundaries early tend to move faster later. Those that treat governance as a late-stage compliance exercise often move quickly at first, then slow down abruptly when trust, audit or regulatory questions surface.
In the first of two articles, we look closely at which C-Suite roles are being redesigned and rewired, and how rapidly the ground is moving beneath the feet of senior management.
AI in the C-Suite: Key Takeaways
AI in the C-suite is not another “digital transformation” programme.
It changes decision-making, accountability and leadership behaviour, not just tooling and efficiency.A C-suite AI strategy works best when ownership is shared, not delegated.
Organisations that pull ahead redesign decision rights across technology, finance, operations, risk, people and legal rather than parking AI with a single “tech owner”.The Chief AI Officer exists to convert AI capability into enterprise value at speed, without losing control of risk and trust.
The role succeeds when it accelerates cross-C-suite ownership, not when it becomes a convenient place to offload decisions.Traditional C-suite roles are being rewired, whether boards acknowledge it or not.
CEOs must draw the line between automation, advice and human judgment; CFOs bring value discipline; CIOs/CTOs build scalable capability; COOs make AI stick in the operating model; CHROs lead the workforce transition; CROs and legal move upstream to manage model risk, liability and compliance.AI concentrates accountability rather than dispersing it.
The organisations extracting meaningful value are those that align speed with governance early, so they move faster later, with fewer sudden stops.
Why AI Is Forcing a C-Suite Rethink
UK boards are no longer experimenting. AI is being deployed at scale, often faster than governance models can adapt.
Surveys show that over 60% of UK executives are actively investing in GenAI, while a majority admit they do not fully understand its implications. At the same time, boards are demanding proof. Where is the ROI? Where is the productivity gain? Where is the strategic edge?
The core issue is structural. AI decisions still too often sit within IT or data silos, disconnected from P&L ownership, risk appetite and workforce strategy.
“Surveys show that over 60% of UK executives are actively investing in GenAI, while a majority admit they do not fully understand its implications.”
AI also breaks traditional leadership models. When humans, algorithms and agents work side by side, leaders can no longer rely on command and control. Their role shifts to setting context: values, guardrails, decision rights and accountability.
That makes AI a leadership design issue, not just a technology one.
New Roles at the Top: The Rise of the Chief AI Officer
Nearly half of FTSE 100 companies now have a Chief AI Officer or equivalent role, with many appointed in the past 12 to 18 months. Two years ago, these roles were rare outside big tech. Today, they are becoming standard in financial services, insurance, telecoms and professional services.
The CAIO is not a CIO or a senior data scientist with a new title. Boards are creating the role because AI cuts across technology, operations, risk and commercial strategy, and existing structures cannot manage it effectively. The role exists to convert AI capability into enterprise value at speed, without losing control of risk, trust or accountability.
In practice, the CAIO typically:
Translates AI capability into commercial and operational outcomes
Defines enterprise-wide AI priorities and use-case discipline
Establishes governance, ethics and regulatory alignment
Bridges technology, business leaders, risk and the board
The strongest CAIOs are rarely pure technologists. They are drawn from data, analytics, digital, transformation or operating roles, often with board-level exposure and experience in regulated environments.
Two archetypes are emerging. Builders focus on innovation, growth and speed, pushing AI into products, operations and customer journeys. Stewards focus on governance, ethics and trust, managing model risk, explainability and regulatory compliance. UK boards increasingly want both mindsets, either in a single role or split across AI, data and risk leaders.
The CAIO only creates value when it accelerates shared ownership of AI decisions across the C-suite, rather than becoming a convenient place to park them.
How Traditional C-Suite Roles Are Being Rewired
We turn from acknowledging the new role of the Chief AI Officer, to more traditional positions where expectations, boundaries, skills and objectives are changing year on year.
Chief Executive Officer: From Sponsor to Owner
The CEO can no longer be a distant sponsor of AI. The ubiquity of AI forces the CEO to make explicit calls about where decisions can be automated, where AI advises, and where human judgment is non-negotiable.
CEOs now own the trade-offs between speed and trust, innovation and reputation, autonomy and control. They also own the narrative, internally and externally, about why AI is being used and where the organisation draws the line.
UK regulatory scrutiny helps. Leaders who embed governance early move faster later. Those who delegate AI entirely to “the tech team” often discover that strategic and cultural decisions have already been made without them.
Chief Financial Officer: From Scorekeeper to Value Discipline
As AI automates reporting, forecasting and analysis, the CFO’s centre of gravity shifts. The value is no longer in producing numbers, but in deciding what those numbers mean and how they shape capital allocation.
CFOs now play a central role in:
Shaping AI investment cases before money is committed
Stress-testing AI-driven scenarios, not just annual plans
Ensuring productivity gains show up in margins, not hidden complexity
AI programmes deliver stronger returns when CFOs are involved from the start. This ensures speed translates into value rather than wasted effort.
Chief Information Officer / Chief Technology Officer: From IT Leader to Enterprise Architect
The CIO or CTO role has changed materially. This is no longer about owning systems. It is about building shared AI capability that scales across the enterprise.
In the UK, that means delivering GenAI use cases at pace while managing cost, security and compliance. It also means resisting fragmentation: multiple tools, multiple models, no common standards.
The strongest CIOs and CTOs act as integrators across the C-suite. When they operate in isolation, organisations end up with technically impressive solutions that struggle to scale or earn trust.
Chief Operating Officer: Owner of the AI Operating Model
AI only creates value when it changes how work actually gets done. COOs are increasingly responsible for:
Redesigning end-to-end processes around AI, not bolting it on
Orchestrating human teams, automation and AI agents at scale
Ensuring efficiency gains do not erode service, safety or culture
Where the COO owns the operating model, AI sticks. Where ownership is unclear, it remains stuck in pilots.
Chief Human Resources Officer: From People Steward to Transition Leader
The CHRO role is often underplayed in AI discussions, and that is a mistake. AI reshapes roles, skills, incentives and trust simultaneously. Without active HR leadership, organisations encounter resistance, attrition or disengagement.
Effective CHROs focus on:
Planning role evolution and reskilling, not just workforce reduction
Redesigning performance and reward for human and AI collaboration
Acting as guardians of fairness, transparency and employee trust
Chief Risk Officer: From Control Function to Integrator
AI introduces new risks, including model risk, bias, explainability and third-party dependency. CROs increasingly treat AI models as risk-rated assets and embed them into enterprise risk and stress-testing processes. Absence from AI decisions often results in organisations moving fast and then stopping abruptly.
General Counsel / Chief Legal Officer: From Adviser to Gatekeeper
Legal teams are moving upstream. AI raises unresolved questions around liability, accountability, intellectual property and regulatory interpretation. Forward-leaning GCs help define acceptable use, escalation thresholds and decision accountability early. Where legal is asked too late, the risk is amplified.
The Pattern That Matters
AI concentrates accountability rather than dispersing it. Organisations that pull ahead are not those with the most advanced models, but those where CEOs, CFOs, CIOs, COOs, CHROs, CROs and legal leaders share ownership of AI decisions, each through their own lens. That is what being AI-native looks like.
Rewiring the C-Suite: FAQs
What does “AI in the C-suite” actually mean?
AI in the C-suite means AI is no longer a technical initiative managed at arm’s length. It becomes a leadership and accountability issue: who approves automation, who owns outcomes, and who carries risk when models influence decisions? In AI-native organisations, decision rights are redistributed across the top team rather than delegated to a single function.
What is a C-suite AI strategy?
A C-suite AI strategy is not a list of tools or pilots. It is a set of choices about where AI creates value, where it creates risk, and how decision-making changes as AI moves into core operations. It defines priorities, governance, operating model, investment discipline and boundaries, so AI scales with trust rather than stalling under scrutiny.
Who owns AI in the C-suite: the CEO, CIO, or Chief AI Officer?
Ownership is shared, but not vague. The CEO owns the trade-offs and the narrative; the CIO/CTO owns scalable capability and integration; the CFO owns value discipline; the COO owns the operating model; the CRO and legal own risk and acceptable-use boundaries; the CHRO owns workforce transition. The Chief AI Officer coordinates, accelerates and connects these responsibilities.
What are the key Chief AI Officer responsibilities?
Chief AI Officer responsibilities typically include translating AI capability into commercial and operational outcomes, setting enterprise priorities and use-case discipline, establishing governance and regulatory alignment, and bridging business leaders, risk and the board. The CAIO creates value when it enables shared decision-making across functions—not when it becomes the place decisions go to die.
Do we need a Chief AI Officer, or can existing roles cover it?
Some organisations can succeed without a CAIO if decision rights are clear and the C-suite has a strong cadence for prioritisation, governance and delivery. Boards create the role when those conditions are missing, when AI cuts across functions faster than existing structures can manage. The risk is appointing a CAIO and still leaving ownership ambiguous elsewhere.
How are C-suite roles changing in practice?
C-suite roles are being rewired around new expectations. CEOs move from sponsor to owner of boundaries and accountability. CFOs shift from reporting to value discipline and ROI realisation. CIOs/CTOs become enterprise architects, resisting fragmentation. COOs redesign processes around AI. CHROs lead trust and reskilling. CROs and legal move upstream.
What should boards measure to know AI is delivering value?
Boards should look beyond pilot activity to enterprise outcomes: adoption in core workflows, measurable productivity or revenue impact, and whether benefits appear in margins rather than hidden complexity. They should also track risk and trust indicators: model governance maturity, incident rates, explainability expectations, third-party dependencies, and whether decision accountability is clear across the C-suite.
Part 2 of this article series will be published in March. To speak to Meera about topics raised in this article, contact her here. Meera.joshi@freshminds.co.uk
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