Chief Data & AI Officer

The Chief Data & AI Officer, or director of data and artificial intelligence, is the leader who brings together data strategy, data governance and the development of AI use cases. They connect these activities with the organisation’s objectives and coordinate the teams delivering them. The title Chief Data and AI Officer also refers to this combined role.

Their work involves decisions that extend beyond a single project: which data to make usable, which use cases to fund, which responsibilities to assign to teams and how to track results. They set technical direction while being able to delegate detailed implementation to platform and engineering leaders. This delegation requires sufficient understanding of architecture and models to discuss the proposed choices.

Data and AI leadership, CDO and CAIO: who does what?

• The Chief Data Officer (CDO) organises data strategy and use. The UK public-sector framework places particular emphasis on data quality, sharing and contribution to decision-making.
• The Chief AI Officer (CAIO) leads the adoption of artificial intelligence and the associated changes. In the Australian public-sector framework, this responsibility is distinct from that of those responsible for AI governance and risk.
• The Chief Data & AI Officer brings these two areas under a single leadership role. The scope of activities and the decisions assigned to this role must be specified in each organisation.

The title alone therefore describes neither the teams supervised, nor investment authority, nor the responsibilities of other departments. Before defining the role, clarify its relationships with IT, business functions and risk leaders. Its reporting line and access to leadership decisions must enable the holder to fulfil the responsibilities actually assigned. The title alone does not establish the reporting line or access to leadership decisions.

Data quality and clear responsibilities are important conditions for developing AI use cases. A single leadership role can connect project needs with the necessary work on data. However, it must be able to set priorities and involve the relevant teams.

The first challenge is deciding which projects to pursue. To frame this decision, connect each project with a business need, the data required and an observable outcome. Also examine the preparation and follow-up work it entails. The number of experiments launched tells you little about their usefulness if no one tracks what happens to them.

The second challenge concerns data reliability. Quality, traceability and responsibilities must be organised so that teams know where information comes from and who handles problems. The director sets direction and priorities. They can delegate detailed architecture work to the relevant specialists. The Data Architect job profile explores this technical dimension in greater depth.

The third challenge is moving into real-world use. An AI project needs a deployment framework, monitoring and model lifecycle management. The leadership team must also organise user adoption, with the necessary training and discussions. Legal, security and risk management teams contribute to this work according to their responsibilities.

• Define a shared strategy for data and AI, linked to business objectives.
• Prioritise investments across the data and AI portfolio and track their results.
• Organise data quality management, metadata management and data traceability.
• Assign data governance responsibilities with the relevant teams.
• Organise the transition from AI experiments to production use and the monitoring of models.
• Lead data and AI teams and develop their skills.
• Support adoption with business functions and training managers.
• Coordinate data and AI decisions with legal, security and risk management teams.

Studies in computer science, information systems or data science can provide useful foundations. These fields are among the educational backgrounds sought by FHLBank San Francisco for its data and AI leadership role. This example does not constitute a qualification requirement applicable to every company.

Useful technical knowledge covers data flows, architecture choices, information quality and how models operate in production. Technical education is particularly valuable for this role when accompanied by the ability to connect these subjects with business decisions.

The UK public-sector framework lists several possible routes into the CDO role: architecture, analysis, data engineering, governance and data science. For a role combining data and AI, then examine the AI responsibilities actually held. A career path into data leadership does not, on its own, demonstrate experience in leading AI use.

Further learning can bring technology and management closer together. The Data Science & AI For Managers programme at HEC and Polytechnique combines data, AI, business issues and law. It is aimed at students and does not, on its own, provide sufficient preparation for this leadership role.

GetPro structures assessment around a framework of prioritised criteria, distinguishing what a candidate’s background can establish from what needs further exploration in an interview. Each criterion is linked to an assessment method. Open questions invite concrete examples, and reference checks help explore points that remain uncertain.

For a Chief Data & AI Officer, the guidance below helps you adapt this framework to the role’s responsibilities. The questions and fictional scenario are suggestions for preparing your assessment.

1. Define the criteria before interviews

Specify the role’s priority responsibilities: strategy, governance, AI projects, teams and adoption. For each responsibility, identify an expected decision and an outcome you will be able to observe.

Build a shared assessment framework for the evaluators. Distinguish technical understanding, decision-making ability and management. Give greater weight to the criteria that address your organisation’s difficulties.

A positive sign is a clear match between the candidate’s experience and the decisions involved in the role. A warning sign is an assessment that mainly rewards familiarity with brands or a prestigious title.

2. Examine achievements and trade-offs

Ask the candidate to present a project portfolio for which they set priorities. Ask them to specify the business objective, constraints and criteria used to continue or stop a project.

Then ask for an example of data governance: who was responsible for data quality, how were problems reported and which decisions fell to the candidate?

Distinguish their contribution from that of specialists and other leaders. A solid account describes decisions, their limits and lessons learnt. Claimed results without an explanation of the candidate’s personal role require further exploration.

3. Propose a scenario close to the role

Fictional example: two business functions request AI projects that use the same data, while the technical team reports quality problems.

Ask the candidate to prepare a recommendation for senior leadership. Let them ask for missing information and explain their assumptions. Examine how they connect the proposed uses with the work needed on data.

Ask them to specify who should be involved, which decisions need to be made and the conditions for moving into production. Also ask about model monitoring after deployment.

A positive sign is the distinction between a leadership decision and a question requiring technical investigation. A warning sign is a solution presented as certain despite essential information being missing.

4. Assess team leadership and adoption

Ask how the candidate developed their teams’ skills and worked with leaders outside their direct reporting lines. Ask them to describe a disagreement and how the resulting decision was explained to the people involved.

Also examine an adoption difficulty. Seek to understand how users were listened to and which lessons led to changes in the support provided.

A useful answer names the people involved, their responsibilities and the candidate’s actions. An account that attributes all resistance to users without examining the organisation warrants questioning.

5. Compare perspectives and references

If your company lacks expertise, involve a professional capable of examining architecture choices and approaches to putting AI into production. Ask the relevant leaders to assess business priorities and specify which decisions the role holder will be authorised to make.

With the candidate’s agreement, compare the responsibilities described with professional references. Ask targeted questions about decisions made, cooperation and team development.

Finally, compare these observations with the initial assessment framework. Document the points established and the remaining uncertainties, then decide which require a further discussion. Avoid letting a general impression replace the agreed criteria.