Draft an executive charter that defines the Chief Data and AI Officer's decision rights and scope of authority.
Digital Transformation & Artificial Intelligence
Defining the Chief Data and AI Officer Mandate and Team Structure
Learn to design the mandate, operating model and team structure of a Chief Data and AI Officer function, from executive charter to reporting lines and role design.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Many organisations have created a Chief Data and AI Officer role without first agreeing what it owns, how it is funded or how it relates to the Chief Information Officer and business unit leaders, and the mandate then drifts or stalls within its first year. This course gives newly appointed and prospective Chief Data and AI Officers a structured method for scoping the mandate: an executive charter that sets decision rights over data platforms, AI investment and governance policy, and a reporting line and escalation path that gives the role real authority. Participants then design the operating model, choosing between centralised, federated and hybrid structures, and work through funding, prioritisation and value-tracking mechanisms that keep the function accountable for measurable outcomes. A significant part of the course covers building the team itself: core roles from data engineering to responsible AI, sourcing strategy, and career pathways that retain scarce talent. The course closes with governance forums, stakeholder management and a scorecard for reporting impact to the executive committee and board. Exercises use a realistic organisational scenario so participants leave with a mandate, operating model and team design they can adapt to their own organisation.
Expected Learning Outcomes
Position the role's reporting line and escalation path to maximise its authority within the executive structure.
Choose between a centralised, federated or hybrid operating model that fits the organisation's maturity and culture.
Design core data and AI roles, capability mix and a RACI that clarifies central and business-unit decision rights.
Build a sourcing and career pathway strategy that attracts and retains scarce data and AI talent.
Establish governance forums and stakeholder management that resolve conflict between central standards and business speed.
Define a scorecard that reports data and AI impact in terms executives and the board recognise.
Who Should Attend
Newly appointed Chief Data and AI Officers designing their first mandate and team structure.
Chief information and technology officers negotiating the boundary of a new data and AI function.
Heads of data and analytics preparing a business case to expand their remit.
Human resources business partners supporting data and AI talent and career pathway design.
Board members and executive committees sponsoring a new data and AI leadership role.
Programme leads responsible for transitioning between data operating models.
Course Modules
Select any module to see its sessions and points.
01Defining the Chief Data and AI Officer Mandate
2 sessions · 8 points
Session 1Scoping the Mandate and Executive Charter
- Draft an executive charter that defines the Chief Data and AI Officer's decision rights over data platforms, AI investment and governance policy.
- Distinguish a mandate focused on data quality and infrastructure from one that also owns AI strategy, model risk and generative AI adoption.
- Negotiate the boundary between the Chief Data and AI Officer's remit and that of the Chief Information Officer and business unit leaders.
- Secure executive sponsorship and a founding budget sufficient to deliver the mandate's first-year priorities.
Session 2Positioning the Role Within the Executive Structure
- Assess reporting lines to the chief executive, chief operating officer or chief financial officer and their effect on the role's authority.
- Define escalation paths that let the Chief Data and AI Officer bring data and AI risk directly to the board or audit committee.
- Establish the role's relationship with business unit leaders who continue to own data quality and AI use cases in their own functions.
- Position the mandate to survive a change of chief executive or a merger, by anchoring it in enterprise strategy rather than a single sponsor.
02Designing the Data and AI Operating Model
2 sessions · 8 points
Session 1Choosing a Centralised, Federated or Hybrid Model
- Compare centralised, federated and hybrid data and AI operating models against the organisation's size, maturity and culture.
- Design a hub-and-spoke structure that places platform and governance capability centrally while embedding delivery capacity in business units.
- Define which decisions, such as tooling standards and model approval, must stay centralised regardless of the chosen operating model.
- Plan a transition path from a fragmented, business-unit-led starting point towards the target operating model.
Session 2Funding, Prioritisation and Value Tracking
- Build a funding model that blends central investment with business-unit contribution for shared data and AI platforms.
- Prioritise a portfolio of data and AI initiatives using a scoring model that weighs value, feasibility and risk.
- Establish a value-tracking mechanism that attributes measurable business outcomes to specific data and AI investments.
- Defend the data and AI budget in an annual planning cycle against competing enterprise technology priorities.
03Building the Team and Capability
2 sessions · 8 points
Session 1Core Roles and Capability Design
- Design the core roles a data and AI office needs, including data engineering, analytics, data governance, MLOps and responsible AI.
- Define role profiles and seniority levels that distinguish a data platform engineer from a data product manager and an AI governance lead.
- Assess which capabilities are better delivered by a central team, an embedded business-unit team or an external partner.
- Build a RACI that clarifies decision rights between central data and AI roles and business-unit data stewards.
Session 2Talent, Sourcing and Career Pathways
- Design a sourcing strategy that blends internal redeployment, external hiring and contractor or partner capacity.
- Build career pathways that retain scarce data and AI talent, including technical tracks that do not require moving into management.
- Assess make-or-buy decisions for specialist capability such as large language model fine-tuning or advanced causal analytics.
- Plan a capability uplift programme that raises data and AI literacy across business functions, not only within the central team.
04Governance, Stakeholders and Measuring Impact
2 sessions · 8 points
Session 1Governance Forums and Stakeholder Management
- Establish a data and AI governance board with clear terms of reference, membership and escalation authority.
- Map key stakeholders, including legal, risk, HR and business unit leaders, and their expectations of the new mandate.
- Design a communication cadence that keeps the executive committee and board informed of data and AI risk and progress.
- Resolve conflicts between central governance standards and business-unit demands for speed and autonomy.
Session 2Measuring and Reporting Impact
- Define a scorecard that tracks data quality, platform adoption, AI use case delivery and governance compliance together.
- Report business impact in terms executives recognise, such as cost avoided, revenue enabled or risk reduced.
- Benchmark the maturity of the data and AI function against comparable organisations to identify capability gaps.
- Review the mandate and operating model annually and adjust scope as the organisation's data and AI maturity increases.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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