Do You Need a Chief AI Officer? A Practical Guide to the Role and Its Value

AI systems are suddenly everywhere in organisations, yet value is often nowhere to be seen. Most firms can point to pilots and demos; far fewer can point to a cleaner P&L, faster cycle times, or clearly defined risk controls.

Experiments bubble up from every corner - IT, digital, innovation teams, individual business units—but no one quite owns the outcome. As one former Chief AI Officer at General Motors put it, “successful AI implementation requires someone in leadership to drive that change, as well as commitment from the top,” not just enthusiastic teams scattered across the organisation.

That fragmentation is not accidental. Responsibility for AI is usually split between CIO and CTO, the Chief Data Officer, risk and compliance, and the business lines that want results. Each has a piece of the puzzle, but no one is accountable for turning AI from scattered tools into safe, revenue‑relevant, cost‑cutting, or risk‑reducing change.

Regulators such as the UK FCA now explicitly expect firms to have clear senior accountability for AI within existing governance frameworks, while boards are beginning to ask whether a single executive owner is needed to avoid unmanaged model and operational risks.

This raises a concrete question: do you actually need a Chief AI Officer, or can existing roles cope? In other words, is it time for a single executive owner of both AI value and vigilance? Opinions differ: some practitioners argue there is “no clear gap” requiring a new C‑level post, while others, including analysts at Forbes and Finextra, see the CAIO as an increasingly common way to concentrate strategy, governance, and delivery of AI value in one place.

This guide is for boards, CEOs, CIO/CTO/CDOs and senior leaders in organisations where:

• AI is becoming strategically material, across several products or functions
• It still feels experimental, risky, or stuck in “pilot purgatory”

You will get a practical view of what a CAIO does, how the role differs from other C‑suite positions, when it is justified, and what to put in place whether you create the role or decide not to.

What a Chief AI Officer Actually Does

A Chief AI Officer is not a “head of experiments”. The role exists to turn AI from scattered pilots into safe, measurable value across the enterprise. As Mastercard’s Chief AI & Data Officer Greg Ulrich puts it, the challenge is “putting in the appropriate control processes while you keep your foot on the accelerator of innovation”.

Working definition

A CAIO is the executive owner of AI outcomes. They are accountable for:

• Value: cost reduction, revenue uplift, risk loss avoidance.
• Safety and trust: robustness, explainability, and responsible use.
• Compliance: meeting regulatory, data and conduct obligations.

Industry analyses describe the CAIO as a cross‑functional strategist and risk owner who sits alongside CIO, CTO and CDO roles rather than replacing them, with a remit that spans both value creation and governance.

Rather than a lone technical genius, the CAIO acts as an orchestrator. They align technology, data, risk, legal, HR and business lines so that AI decisions, controls and investments are coherent across the firm. Their authority is less about writing code, more about setting direction and enforcing standards, a pattern reflected in how regulators such as the UK FCA embed AI leadership into existing senior‑manager accountability frameworks in financial services.

Strategy and portfolio management

The CAIO translates board ambition into a practical, multi‑year AI roadmap. That means:

• Selecting a small number of material use cases that matter to the P&L, not a long tail of novelty projects.
• Sequencing these so early wins fund and inform later, more complex work.

They manage AI as a portfolio, with clear business KPIs rather than vanity metrics. Typical measures include cycle‑time reduction, cost‑to‑serve, incremental revenue and avoided losses. Initiatives are stopped, pivoted or scaled based on evidence from systems in production, not from impressive demos in labs.

This portfolio discipline matches what external benchmarks show separates AI “leaders” from laggards: higher deployment rates, quantified AI targets and tighter linkage to financial outcomes.

Process redesign and operating model

A core part of the role is shifting the organisation from “AI tools” to AI‑enabled processes. The CAIO pushes teams to redesign workflows end‑to‑end, using automation, decision support and new interaction models, instead of bolting chatbots onto legacy steps. This reflects the pattern highlighted by enterprise AI leaders who stress that value comes from reimagining services and processes around AI, not merely inserting models into existing tasks.

They also design the operating model:

• Where AI capabilities sit (central centre of excellence, federated teams, or a hybrid).
• Who is responsible, accountable, consulted and informed (RACI) across model owners, risk, audit and business lines.

This clarity prevents duplicated effort, gaps in ownership and confusion over who signs off what.

Governance, risk and ethics

The CAIO embeds AI into existing risk and governance frameworks rather than creating a parallel universe. They oversee policies for:

• Model lifecycle: development, validation, monitoring, retirement.
• Data use: what can be used for training or inference, and under which conditions.
• Access, third‑party and cloud dependencies: especially where concentration and data residency risks arise.

In higher‑risk areas, the CAIO sets specific guardrails. That includes layered controls against impersonation (e.g. deepfakes, voice cloning) and disciplined approaches to hallucination, such as grounding models in trusted data, retrieval‑augmented generation and human‑in‑the‑loop review for consequential decisions.

Regulators like the FCA explicitly expect firms to manage AI within existing conduct and operational‑resilience regimes, treating AI as amplifying familiar risks such as model, technology and third‑party risk rather than as an ungoverned novelty, and are moving towards live testing of AI systems in production under supervision.

Technology, data and talent foundations

The CAIO defines principles for build vs buy, model and vendor selection, and how to avoid model sprawl and lock‑in. They ensure the data foundations are fit for purpose: quality, lineage and policy enforcement are treated as non‑negotiables, not afterthoughts.

On talent, the CAIO both secures scarce ML and data engineering skills and defends investment in “plumbing” such as data engineering and MLOps. They work with HR to:

- Design upskilling programmes for the wider workforce.

- Set clear acceptable‑use guidelines covering IP, confidentiality and ethical use of AI assistants.

This emphasis on skills and governance aligns with survey evidence that only a small minority of firms currently realise substantial value from AI and many lack readiness for responsible deployment, despite high strategic ambition.

External engagement and board education

Externally, the CAIO represents the firm to regulators, industry sandboxes, partners and academia, using these channels to de‑risk deployment and access emerging capabilities. In sectors such as financial services, supervisory bodies explicitly encourage this kind of structured engagement through sandboxes, synthetic‑data environments and live‑testing regimes, expecting senior AI leaders to co‑design safe deployment patterns rather than wait passively for detailed rules.

Internally, they educate the board and executive team. This includes concise heatmaps of AI opportunity and risk, readiness assessments, and advice on risk appetite for automation and more agentic systems.

In effect, the CAIO gives the board a single, informed view of where AI helps, where it hurts, and what controls are in place - mirroring the “single point of responsibility” pattern now seen in large banks and payments firms that have appointed dedicated AI chiefs to sit alongside CIO, CTO and CDO roles.

How a CAIO Differs from Your CIO, CTO and CDO

Why this distinction matters

Treating AI as an add‑on to existing C‑suite roles usually fails. CIOs, CTOs and CDOs are already stretched running core systems, digital products and data governance. AI, however, cuts across every function, reshapes processes, and introduces new operational and ethical risk.

As Bernard Marr notes, the work involved is now “too large and specialised a subset of existing CDO and CTO workloads to be managed off the side of the desk”, which is why many firms are carving out a distinct Chief AI Officer role rather than simply rebadging existing posts.

Without a clear mandate:

• AI pilots multiply but do not reach production or deliver P&L impact
• Critical decisions on risk, model use and third‑party AI remain fragmented
• Boards struggle to see who is accountable when something goes wrong

A distinct CAIO role (or an explicitly extended mandate) clarifies ownership for both value and vigilance. In heavily regulated sectors, supervisors are already signalling that they expect named senior accountability for AI strategy, governance and outcomes, not just generic technology oversight.

The CIO vs the CAIO

A CIO typically optimises the technology estate: networks, enterprise applications, security, service levels and cost. Their success is measured in stability, reliability and efficiency.

A CAIO, in contrast, is accountable for business outcomes from AI, wherever they sit in the organisation. That includes:

• Prioritising high‑value AI use cases with business leaders and driving them into production
• Owning AI policy and governance – acceptable use, ethics, vendor standards and guardrails
• Managing AI‑specific risks such as sensitive data leakage through prompts, misuse of generative tools, and autonomous agent behaviour

Industry analysis of early CAIO appointments consistently highlights this split: CIOs concentrate on the “pipes and plumbing”, while CAIOs decide where AI should change products, services and operations, and how to evidence value and compliance.

The CIO often operates the infrastructure; the CAIO decides which AI bets to place, how they will be used, and what risk is acceptable.

The CTO vs the CAIO

CTOs focus on technology strategy, architecture and product engineering. They optimise how digital products and channels are built and scaled. AI is often seen as one feature set among many.

The CAIO’s lens is broader and more risk‑aware. They:

• Span external products and internal operations – from fraud and claims to HR, finance and risk functions
• Balance experimentation with enterprise‑grade controls, so prototypes can survive contact with regulators and auditors
• Emphasise safe deployment, testing, monitoring and human‑in‑the‑loop controls, not just shipping AI‑powered features quickly

This is why firms such as Mastercard have given a single executive explicit responsibility for both AI innovation and the associated guardrails, rather than leaving these trade‑offs to product or engineering alone.

Where the CTO asks “can we build this?”, the CAIO asks “should we build this, where, and under what safeguards?”.

The CDO vs the CAIO

CDOs concentrate on data: governance, quality, lineage, analytics and BI. Their mandate is to democratise trustworthy data and reporting.

The CAIO consumes and shapes that data agenda specifically for AI. In practice this means:

• Defining data readiness for AI use cases – what data can be used, how it must be prepared, and what cannot leave secure boundaries
• Governing the full AI model lifecycle: evaluation, deployment, monitoring, retraining and retirement
• Tackling generative AI issues (grounding, hallucination, content safety) that go beyond traditional dashboards and models

Practitioners increasingly describe this as a “chef and ingredients” split: the CDO ensures the ingredients are safe and well managed; the CAIO decides how they are combined into AI‑enabled processes that create measurable value, and which recipes are too risky to serve at all.

When roles can be combined

In earlier‑stage organisations, or where AI is still limited to a small number of use cases, combining roles can be pragmatic – often CDO/CAIO or CIO/CAIO. This can work if:

• AI is not yet material across multiple P&Ls or heavily regulated activities
• The executive has explicit written accountability for AI value and AI risk
• They have direct board access and protected capacity to focus on AI, not just core IT or data demands

You should consder separating the CAIO role when:

• AI becomes central to several business lines and revenue streams
• The number of AI vendors, models and tools is rising fast, creating duplication and control gaps
• Regulatory and brand risks from AI intensify, and the board expects a single accountable owner

At that point, a dedicated CAIO becomes less about another title and more about ensuring someone is clearly responsible for turning AI into safe, scaled enterprise performance – a pattern already visible in sectors such as banking and payments, where boards are under pressure to show disciplined, named oversight of AI strategy, risk and return.

Do You Actually Need a Chief AI Officer?

Deciding whether to appoint a CAIO is less about hype and more about scale, risk and focus. Use the following questions to test your own situation.

Start with strategic materiality

Begin with your P&L, not your pilots. If AI now underpins how you compete or make money, it needs a clear owner.

AI is strategically material when:

• Multiple products or business lines rely on AI (for example, fraud detection, customer service, underwriting, trading, supply chain optimisation or personalisation).
• Automation through AI is baked into your efficiency targets or growth plans, not just a side experiment.

Analysts tracking the rise of the CAIO role point out that it tends to appear first in organisations where AI is embedded in core products and decisioning, and fragmented ownership is already creating duplicated spend and inconsistent outcomes (for example, across marketing, risk and operations in large financial institutions such as JPMorgan or Mastercard’s AI and data hub). In those conditions, a CAIO is less a “nice to have” and more a structural response to complexity.

When AI is both strategic and spread across many functions, “everyone owns it” quickly becomes “no one is accountable”. A CAIO gives you a single executive responsible for turning AI from scattered initiatives into measurable business outcomes.

Risk, regulation and brand exposure

In high‑risk settings, the question is often not “do we need a CAIO?” but “who is on the hook when things go wrong?”

You are in this territory if:

• You operate in regulated sectors and AI affects customers, citizens or markets.
• AI is used in credit, claims, eligibility, safety systems or other conduct‑sensitive decisions.

Regulators such as the UK’s Financial Conduct Authority have made it clear that firms remain fully responsible for AI‑enabled activities under existing regimes like the Senior Managers Regime and Consumer Duty, and are increasingly expecting named senior accountability for AI governance and live testing of high‑impact systems across markets and customers. In parallel, US federal agencies are now required to appoint a Chief AI Officer to own internal policy, risk and workforce change around AI.

Here, a CAIO can:

1. Act as the named executive accountable to the board and regulators for AI governance and evidence of control.
2. Embed AI into existing risk, conduct and compliance frameworks, so oversight is consistent rather than bolted on.

Complexity and fragmentation of AI activity

Many organisations stall in “pilot purgatory”: dozens of proofs of concept, few in production, no clear value story.

Warning signs include:

• Multiple uncoordinated pilots and genAI tools across functions.
• Overlapping vendors and cloud services, with no simple answer to “how many models do we run, and where?”.

Consulting and industry research repeatedly show that only a small minority of firms convert AI pilots into scaled value, with the rest stuck in fragmented experiments and duplicated tooling. Former Chief AI Officers at large incumbents, from automotive to payments, describe the role as “directing the traffic” of AI across functions - standardising platforms, aligning roadmaps and forcing trade‑offs on where to build versus buy - precisely to break this pattern.

In these conditions, a CAIO can:

1. Rationalise spend, vendors and platforms, and introduce shared patterns and guardrails.
2. Prioritise a small number of high‑value use cases and drive them through integration, hardening and support.

Organisational readiness and executive bandwidth

Even if AI is material, your existing leaders may simply not have the capacity to own it properly.

Common constraints:

- CIO, CTO and CDO already absorbed by modernisation, cyber, cloud, regulatory and transformation agendas.

- AI left as “innovation theatre” without a clear owner for delivery and risk.

External analyses of failed AI portfolios consistently highlight this bandwidth problem: CIOs and CTOs are expected to keep the lights on, modernise infrastructure and deliver digital programmes, while simultaneously absorbing AI‑specific strategy, ethics, model risk and workforce change.

As one former GM CAIO put it, “successful AI implementation requires someone in leadership to drive that change, as well as commitment from the top - not just a rebadged data or IT lead.

A CAIO becomes justified when you need:

• Board‑level influence and cross‑functional authority over AI value and vigilance.
• Someone who can design operating models and workforce change, not just choose technology.
• Enterprise‑wide guardrails for agentic or autonomous AI that can take actions, not just give recommendations.

When you probably do not need one yet

Some organisations are better served by strengthening existing roles before creating a new one.

You probably do not need a CAIO if:

• AI use is narrow, low‑risk and well controlled (for example, contained analytics or marketing optimisation tools).
• Regulatory and brand exposure from AI is currently modest.

In this case, a pragmatic approach is to:

• Assign explicit AI accountability to an existing executive (often the CDO or CIO) with a clear mandate, small steering group and lightweight governance.
• Define transition triggers - such as AI becoming material to multiple P&Ls, or the introduction of agentic systems - at which point a dedicated CAIO role, or an expanded hybrid (CDO/CAIO), may become necessary.

Research from industry bodies and practitioners stresses there is no one‑size‑fits‑all answer here: in some firms, AI remains an extension of data and IT responsibilities, while in others - where AI is central to products, heavily regulated, or deeply embedded in how money and safety‑critical decisions flow - a separate CAIO is increasingly treated as a minimum condition for credible governance.

Making a CAIO Deliver Value, Not Hype

A CAIO earns their place by turning AI from scattered pilots into hard numbers on the P&L. That demands ruthless focus, solid foundations, and disciplined change management - not another wave of experiments.

As Mastercard’s Chief AI & Data Officer Greg Ulrich puts it, “putting in the appropriate control processes while you keep your foot on the accelerator of innovation is the real challenge” for any serious AI leader.

Focus on a few material use cases

The quickest way to waste money is chasing every new model or headline feature. A CAIO should:

• Choose 1–3 high‑value, enterprise‑relevant problems (for example, contact centre efficiency, claims automation, fraud loss reduction, developer productivity). Analysis of AI “leaders” in banking by BCG shows the bulk of value sits in a small number of well‑chosen journeys such as service, marketing, sales and IT development, not in hundreds of minor pilots
• Quantify the economics upfront: current cost, cycle time, error rates, leakage. Firms that do this consistently are far more likely to report positive ROI rather than “AI theatre”
• Commit to production, not proof‑of‑concept theatre: integration, support, monitoring, change to processes.

This focus keeps the portfolio aligned to strategy, avoids a zoo of demos, and creates reference wins the rest of the business can copy.

Invest in foundations (“plumbing”) early

Most of the work sits beneath the model.

• Data and MLOps: Expect 70–80% of effort in data pipelines, integration, feature stores, testing, monitoring and retraining. Banking case studies consistently show that underinvesting in “data plumbing” is one of the fastest ways to poison otherwise good algorithms. The CAIO must defend this investment or every use case stalls at prototype.
• Guardrails and controls: Put in place policies on prompt security, sensitive data handling, and when public models are allowed. Require model risk assessments, independent challenge, and clear approval paths for high‑impact deployments, in line with emerging supervisory expectations on “output‑driven” validation of AI systems.

The aim is a shared platform and control set that every new use case can reuse, rather than bespoke plumbing each time.

Drive adoption and culture change

Value arrives only when people actually change how they work.

Upskilling at scale: Prioritise short, hands‑on sessions over long theoretical courses. Provide an internal AI guidebook with patterns, dos and don’ts, approved tools and escalation routes. BCG’s research suggests employees want a few focused hours of practical coaching far more than long formal curricula, and that this kind of enablement is a common feature of the small minority of firms getting material value from AI.

Champions and two‑speed execution: Nominate AI champions in each function to localise opportunities and gather feedback. Run a two‑speed model—enterprise platforms and standards at the core, with safe sandboxes for experimentation at the edge. Former General Motors CAIO Barak Turovsky describes these local champions as essential: the CAIO “can’t do all the magic while everyone else just sits there”.

This combination builds confidence, reduces shadow IT, and keeps innovation aligned with risk appetite.

Manage ethics, trust and communications

Trust is a design choice, not a press release.

• Human‑in‑the‑loop: Frame AI as decision support, with humans accountable for consequential calls - especially in early phases. Large financial institutions such as JPMorgan Chase have explicitly treated operational risk as the “dominant gene” for generative AI and kept humans in the loop for money‑movement and credit decisions as a result
• Workforce concerns: Be explicit that some roles will change; pair automation plans with reskilling and redeployment commitments.
• External trust: Tell customers where AI is used, how it is supervised, and what is off‑limits (for example, impersonation or deceptive use). Regulators such as the FCA are clear that existing duties on fairness, bias and foreseeable harm apply just as much to AI‑enabled journeys as to traditional ones

The CAIO becomes the visible owner of both opportunity and safeguards.

Anti‑patterns to guard against

A CAIO should actively watch for:

• “Interesting” use cases that are not economically material.
• Optimistic timelines that ignore data work, policy sign‑off and process redesign.
• Assuming all data is AI‑ready without quality, lineage and context checks.
• Siloed experiments with no shared controls, creating inconsistent risk and duplicated spend.

In practice, the CAIO’s playbook is simple but demanding: focus, plumbing, adoption, trust - and relentless measurement against business outcomes.

AI does not need another evangelist; it needs an accountable owner of value and vigilance. When AI is strategic, risky, and scattered across teams, a Chief AI Officer provides that missing centre of gravity - turning pilots into production, and enthusiasm into controlled, measurable impact. As former GM CAIO Barak Turovsky puts it, “successful AI implementation requires someone in leadership to drive that change, as well as commitment from the top.”

A CAIO tends to make sense when:

• AI touches multiple P&Ls
• Regulatory or reputational risk is real
• Pilots, vendors and models are proliferating
• Agentic AI is entering scope
• CIO/CTO/CDO capacity or remit is already stretched

These are the same conditions in which regulators expect named senior accountability for AI outcomes, rather than diffuse ownership across IT and data functions, as reflected in the FCA’s AI governance speeches and live‑testing framework for UK firms.

You do not need a Chief AI Officer because others have one; you need one when AI is too important, and too risky, to be everyone’s part‑time job. In sectors where AI is already treated as a board‑level capability, from global banks to payment networks, the CAIO (or equivalent) is emerging precisely to own that responsibility end‑to‑end.