What AI and Data Leaders Need From Organisations to Deliver Results

Senior AI and data leaders are usually hired with ambitious expectations.

They are asked to accelerate AI adoption, improve the use of data, build capability, establish governance and demonstrate business value. In larger organisations, they may also be expected to coordinate work across technology, operations, risk and individual business functions.

Whether they can do that depends on much more than the quality of the person appointed.

Lucent Search's research found experienced AI and data leaders working through significant organisational constraints: 87% reported difficulty with cross-departmental collaboration, 71% struggled to secure sufficient budget and 88% found AI governance challenging.

Attraction and retention still matter, but organisations also need to consider what they give these leaders once they arrive.

A strong AI or data executive still needs a workable role, enough authority to make important decisions, visible sponsorship from senior leadership and an organisation prepared to change around the work.

Start with a role the executive can actually deliver

AI and data leadership roles can become very broad very quickly.

A CDO may be responsible for data ownership, governance, platforms, analytics and organisational adoption. A CDAO can add data science and AI to that remit. A CAIO may be expected to set enterprise AI direction, coordinate investment, establish governance and drive adoption across functions.

Those responsibilities may belong together, but they can also create a job that depends heavily on decisions the executive does not control.

Before appointing somebody, be clear about what you expect them to have changed within the first 12–18 months.

If the expectation is to move AI into core operations, which systems need to change? If the role is expected to improve the commercial use of data, which business leaders will need to work differently? If enterprise AI governance sits with the executive, which decisions can they make and which remain with risk, legal, technology or the business?

A broad title can hide a role with surprisingly little room to act.

Authority needs to match the responsibility

Senior AI and data leaders often work through functions they do not directly control.

The CIO may control enterprise technology. Business leaders own operational processes. Finance influences investment. Risk and legal may determine whether particular applications can proceed. Data itself may be distributed across different parts of the organisation.

If an executive is accountable for an outcome, they need sufficient authority to influence the decisions on which that outcome depends.

The AI or data leader does not need control over every adjacent function, but the organisation does need to be explicit about where they can decide, where decisions are shared and how disagreements will be resolved.

The survey found that 51% of respondents considered cross-departmental collaboration very challenging and a further 36% moderately challenging. Leaders described competing priorities, fragmented data agendas and difficulty getting other parts of the organisation to participate in AI delivery.

That is difficult to solve through personal influence alone.

If the role continually requires the executive to negotiate from scratch for cooperation, data access, technology support or business participation, the organisational design is placing much of the burden of execution on the individual.

Executive sponsorship becomes visible when priorities compete

Many organisations describe AI as strategically important.

Sponsorship becomes visible when AI competes with another priority.

A business unit does not want to change an established process. Technology investment is diverted elsewhere. A programme requires more funding than expected. A difficult governance decision slows deployment. A senior colleague disagrees with the proposed direction.

Senior sponsorship determines what happens next.

The survey found that around three-quarters of respondents experienced some difficulty securing organisational buy-in for AI initiatives. At the same time, a meaningful minority reported that buy-in was not a problem, suggesting quite different operating environments for leaders doing similar work.

Visible senior backing allows the AI or data executive to spend more time delivering the work. Nominal sponsorship leaves them spending much more of the role persuading others to participate.

One survey respondent captured that distinction particularly well:

“I want to move from fighting for attention on AI, to focusing on delivering results with AI.”

That is a useful description of the difference between having an AI leader and creating the conditions in which that person can lead.

Budget tells you how much authority the role really has

Responsibility becomes difficult to take seriously if the executive cannot secure the resources required to deliver it.

71% of respondents reported difficulty securing sufficient budget for AI initiatives. The survey also found considerable pressure around demonstrating ROI as work moved from experiments into scaled deployment.

Some tension around investment is healthy. AI should compete for capital like other major business priorities, and leaders should be able to explain what the organisation expects to gain.

The problem arises when the role carries ambitious enterprise expectations but funding remains fragmented, short term or dependent on repeated negotiations with other functions.

An executive may have formal responsibility for AI while having little influence over technology investment, data capability, headcount or business participation.

That is accountability with very limited control over the conditions required for success.

Before appointing or expanding an AI leadership role, understand where the budget sits and how investment decisions will actually be made.

Cross-functional delivery needs more than goodwill

AI increasingly sits across organisational boundaries.

A model may be developed by a specialist team, depend on data owned elsewhere, require integration from technology and ultimately change a process managed by operations or another business function.

The executive leading AI cannot personally own every part of that chain.

The organisation therefore needs ways of working that support shared delivery.

That may involve clearer decision rights, joint ownership of important initiatives, aligned priorities between functions or formal governance for decisions that cross several areas.

The precise structure will vary.

Cooperation cannot depend entirely on whether the AI leader happens to have strong personal relationships with every executive whose help they need.

Lucent's research found stakeholder management and change management among the most sought-after organisational capabilities in AI teams. 52% rated stakeholder management as highly in demand, while 43% said the same of change management.

Those capabilities matter because AI requires organisational change, but they should complement good organisational design rather than compensate for its absence.

Governance has to help the executive make decisions

Governance is now part of the leadership job rather than something that happens after the technology has been built.

Lucent's survey found that 88% of respondents experienced difficulty establishing effective AI governance.

For the leader, the difficulty is often practical.

Who can approve higher-risk use cases? Who owns model risk? Where does data accountability sit? When should legal or risk become involved? Who can stop deployment? Which issues need to reach the executive committee or board?

Governance becomes useful when those decisions are understood.

Poorly designed governance can create the opposite effect: the AI leader is held accountable for progress but has to navigate several committees and functions without knowing where final decisions sit.

In regulated and highly governed organisations, strong governance should give the leader clearer routes to decisions and escalation rather than simply adding more process.

The role needs enough support underneath it

The scope of an executive role depends partly on the capability beneath it.

Lucent's research found substantial variation in AI and data functions, from relatively small specialist teams to large global organisations. Around 40% of respondents managed teams distributed across multiple regions.

A CDAO with experienced leaders running platforms, governance, analytics and AI can operate very differently from one personally carrying much of that responsibility.

The same applies to a CAIO.

If you expect the executive to spend their time influencing strategy, coordinating across functions and making enterprise decisions, somebody else needs to be capable of running the specialist work beneath them.

Where that leadership layer is weak, the senior executive may spend much more time solving operational problems than the role design assumes.

Before deciding that the person needs broader experience, look at whether the organisation around them is strong enough to support the job being asked of them.

Judge the leader against outcomes they can genuinely influence

AI and data roles can accumulate expectations faster than authority.

The executive may be asked to increase AI adoption, generate ROI, improve data quality, modernise platforms, change culture and reduce risk simultaneously.

Some of those outcomes depend heavily on other executives.

Business leaders still need to own the performance of their functions. Technology leadership still needs to deliver reliable systems. Finance remains responsible for capital discipline. Risk and legal have their own responsibilities.

The senior AI or data leader needs clear personal accountability without becoming the default owner of every problem connected to AI or data.

This becomes particularly important when assessing performance.

If an AI initiative has not scaled, understand whether the constraint was the executive's judgement, the capability of the team, technology integration, lack of funding, business adoption or an unresolved organisational dependency.

Those are very different leadership conclusions.

High mobility should make boards examine the role as well as the person

Lucent's survey found that nearly 63% of AI and data leaders planned to change roles within the following 12 months.

That is a significant continuity risk for organisations investing heavily in AI.

The survey does not establish that poor role design, limited authority or weak sponsorship caused those intentions. The respondents had many possible reasons for considering a move, and strong demand for their experience gives them options.

Across the wider survey, leaders repeatedly raised concerns about organisational backing, resources, cooperation across functions and their ability to create visible business impact.

Boards should therefore be cautious about treating retention entirely as a compensation or career-development problem.

If a senior leader is continually accountable for outcomes they cannot influence, replacing them may simply reset the same problem with a different person.

Before replacing an AI or data leader, examine the conditions around the role

Leadership capability still matters. Some executives will lack the commercial judgement, technical depth, influence or change-leadership ability required for the next stage of the organisation's AI agenda, and there will be times when a new appointment is necessary.

Before reaching that conclusion, examine the environment around the current role. Does the executive have a clearly defined remit? Can they make or materially influence the important decisions? Is there visible sponsorship when priorities conflict? Are resources aligned with the expectations placed on them? Do adjacent executives understand their own responsibilities for delivery? Is the team beneath the leader strong enough?

If several of those conditions are missing, the organisation may be asking the executive to solve a structural problem through individual capability. A stronger hire may cope for longer, but the structural problem will remain.

What do strong AI and data leaders need from the organisation?

Strong AI and data leaders need a role with clear expectations, enough influence over the decisions that shape delivery, visible senior sponsorship, usable governance and resources that match the scale of the task. Business leaders also need to understand that AI adoption cannot be delegated entirely to the AI or data function.

The quality of the executive still matters enormously. But where authority, sponsorship or ownership are weak, changing the person will not solve the underlying problem.

Before asking more of an AI or data leader, examine whether the role and the organisation around it give them a realistic chance of delivering.

Related analysis and executive search

Can your CIO lead AI, or do you need a different leadership model?

How to assess whether the existing CIO has the capability, remit and authority to lead enterprise AI.

Should we hire a CDO, a CDAO or a CAIO?

How to decide whether data, analytics and AI should sit with one executive or require separate leadership.

We’ve invested heavily in AI but aren’t seeing enough business value. What kind of leadership do we actually need?

How to identify whether the constraint sits in leadership, ownership, data, technology or execution.

Chief AI Officer Executive Search

For organisations considering dedicated enterprise AI leadership.

CDO / CDAO Executive Search

For organisations appointing senior data and analytics leaders.

Is the leadership role designed to succeed?

Lucent Search works with boards and executive teams to clarify what a senior AI or data leader is expected to deliver, which decisions they need to influence and whether the organisation around the role gives them a realistic chance of succeeding.

Where the capability gap requires a new appointment, Lucent conducts retained executive search for senior AI, data and technology leaders.

Speak to Rebecca