Design an intake process that captures the business problem and expected user behind every proposed AI use case.
Digital Transformation & Artificial Intelligence
Building and Prioritising an Enterprise AI Use Case Portfolio
Equips portfolio and analytics leaders to intake, score, sequence and retire AI use cases so investment follows evidence of value and readiness, not enthusiasm.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Most organisations do not lack AI ideas; they lack a way to choose between them. Proposals arrive from every department, each argued on its own terms, while data science capacity stays fixed and only a fraction of pilots ever reach production. This course builds the portfolio discipline an enterprise AI use case portfolio needs: a consistent intake process, a scoring model that weighs value, feasibility and risk together, and a sequencing method matched to real delivery and data capacity. Participants work through their own candidate use cases, scoring and ranking them, choosing between building, buying and partnering, and identifying shared data and platform dependencies that would otherwise be built twice. Later sessions cover tracking realised benefit against the original business case and retiring or consolidating use cases that no longer earn their place. The course ends with a portfolio review method participants can run every quarter, so prioritising the AI portfolio becomes a repeatable governance habit rather than a one-off exercise.
Expected Learning Outcomes
Score competing AI use cases on value, feasibility and risk using one consistent, weighted model.
Sequence approved use cases against actual data science and engineering capacity for coming quarters.
Decide between building, buying and partnering for a given use case on total cost of ownership.
Identify shared data and platform dependencies before two teams build the same component independently.
Track realised benefit against the original business case at defined checkpoints after deployment.
Retire, consolidate or rebalance portfolio use cases as evidence of value and strategic priorities change.
Who Should Attend
Heads of data and analytics building a formal AI investment portfolio for the first time.
AI and innovation programme managers coordinating proposals from multiple business units.
Enterprise architects assessing shared data and platform dependencies across AI initiatives.
Finance business partners who approve funding for competing AI and analytics proposals.
Product owners and business sponsors preparing a use case for portfolio review.
Portfolio and PMO staff extending existing project governance to cover AI-specific criteria.
Course Modules
Select any module to see its sessions and points.
01Building the Intake and Evaluation Pipeline for AI Ideas
2 sessions · 8 points
Session 1Capturing and Qualifying AI Use Case Proposals
- Standardise an intake form that captures the business problem, target decision and expected user of every proposed AI use case.
- Screen incoming AI ideas against strategic objectives before committing analyst time to detailed assessment.
- Interview the business sponsor of each proposal to separate a genuine operational problem from enthusiasm for a technology.
- Reject or park a proposal formally, with a documented reason, so a rejected idea does not quietly resurface unchanged.
Session 2Assessing Data and Technical Readiness Before Scoring
- Assess the availability, quality and labelling status of the data each proposed use case depends on.
- Score technical feasibility against the organisation's current data infrastructure and model deployment capability.
- Flag use cases that require data not yet collected, and separate the data project from the AI project in the plan.
- Record known bias or coverage gaps in candidate datasets before a use case is allowed to proceed to a pilot.
02Prioritising and Sequencing the AI Portfolio
2 sessions · 8 points
Session 1Scoring Use Cases on Value, Feasibility and Risk
- Score each use case on business value, delivery feasibility and operational risk using a shared, weighted model.
- Weight reach, impact, confidence and effort consistently across proposals so scores can be compared fairly.
- Separate quick, low-risk automation opportunities from use cases that need a multi-quarter data investment.
- Present the scored portfolio to a cross-functional review board rather than letting one sponsor set the sequence.
Session 2Sequencing Pilots Against Delivery and Data Capacity
- Sequence approved use cases against the data science and engineering capacity actually available each quarter.
- Group use cases that share a dataset or platform component so infrastructure is built once, not per project.
- Set a realistic pilot cohort size that governance and monitoring capacity can support without shortcuts.
- Reserve a share of portfolio capacity for maintaining and retraining models already in production.
03Governing Build, Buy and Partner Decisions
2 sessions · 8 points
Session 1Choosing Between Building, Buying and Partnering
- Compare build, buy and partner options for each use case against total cost of ownership, not licence price alone.
- Test a vendor's proposed model against the organisation's own data before committing to a buy decision.
- Document the trade-off between a customised in-house model and a faster, less flexible packaged tool.
- Involve enterprise architecture early so a chosen option fits existing integration and security standards.
Session 2Managing Shared Platform and Data Dependencies
- Identify where two use cases depend on the same underlying data product or platform service.
- Assign a shared component owner so competing use cases do not each modify the same pipeline independently.
- Track cross-use-case dependencies on a single map that portfolio and technical leads both maintain.
- Resolve conflicting priorities over shared infrastructure through the portfolio board, not ad hoc negotiation.
04Tracking Value and Evolving the Portfolio
2 sessions · 8 points
Session 1Measuring Benefit Realisation After Deployment
- Define the benefit metric for each use case before deployment, agreed with the function that owns the outcome.
- Compare realised benefit against the original business case at fixed checkpoints after go-live.
- Attribute change in the target metric to the AI use case only after accounting for other contributing factors.
- Report portfolio-level value achieved and value still pending to the sponsors funding the programme.
Session 2Retiring, Consolidating and Rebalancing the Portfolio
- Retire use cases that fail to reach agreed benefit thresholds within a set review period.
- Consolidate overlapping use cases that different teams built independently for a similar purpose.
- Rebalance the portfolio when strategic priorities shift, rather than continuing legacy commitments by default.
- Refresh the scoring model periodically so ageing assumptions about cost, risk and value are corrected.
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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