Fractional AI Leadership & Programme Management
Scope of our engagement
- Programme governance, AI committees and executive/business/IT coordination
- Project portfolio, investment trade-offs and prioritisation
- Budgets, investment planning and supplier oversight
- Risk, dependency and decision management
- Adoption management, training and awareness
- Benefit, cost and ROI tracking; continuous improvement
Deliverables and decision criteria
Current portfolio, AI dashboard, investment plan, executive reporting and decision log. Benefits tracking distinguishes observed gains, remaining assumptions and operating costs. Each project has a sponsor and an accountable business owner.
Prerequisites, controls and limitations
The mandate defines delegated authority, availability, decisions reserved to the client and exit arrangements. Fractional leadership does not replace executives’ legal duties or process ownership. Management tools and evidence remain accessible to the client.
Our method: from business need to measured value
Understand
Analyse the organisation, activities, people, data and IT landscape. Observe real work and pain points before selecting a solution.
Prioritise
Compare business value, feasibility and risk. Establish a baseline, a business owner and success criteria for each use case.
Design
Define the target process, architecture, interfaces, responsibilities, human approvals and performance indicators.
Experiment
Test a pilot on representative situations, including failure cases. Compare quality, cost and usefulness with agreed criteria.
Deploy
Integrate with applications, run acceptance and rollback checks, train users and support managers.
Measure
Track benefits, errors, costs, adoption and incidents. Regularly reassess models, data and controls.
FAQ
How should an AI programme be measured?
Compare outcomes with a baseline: quality, end-to-end time, total cost, adoption and incidents. Measure review effort and errors, not just the number of generated answers.