AI Use-Case Assessment: Turning an Operating Question into a Governed Investment Decision

The client problem: a backlog of ideas is not an AI portfolio Industrial groups, financial institutions and public bodies rarely lack AI ideas. They receive them from operating teams, technology partners and senior sponsors: automate an assessment, forecast demand, prioritise a service request or assist a case worker. The harder question is whether any idea deserves commitment before it has a defined operating decision, accountable owner, evidence base and control route. SDAIA’s AI Adoption Framework places AI within three connected pillars: directions, enablers and outcomes. Strategy and governance sit alongside data, infrastructure and human capability; delivery is then considered through deployment, monitoring and organisational impact. This provides a practical discipline for a use-case study. A candidate should not be assessed only by what a model might do. It should be examined through the business decision it supports, the operating workflow it changes, the data it needs, the people who retain responsibility and the conditions under which it can progress. SDAIA’s AI Ethics Principles reinforce the same operating test. Accountability, documented responsibility, human oversight and risk-management mechanisms must be present across the AI lifecycle. The decision is therefore not simply whether a use case is innovative. It is whether a sponsor can explain who owns it, how it will be observed, when human judgement overrides it and what evidence would justify a controlled pilot or deployment. A proposed consulting work model for decision-grade use cases This is a proposed consulting scope. It does not claim that any candidate is viable, compliant or investment-ready before the client’s mandate, evidence, data and control environment have been reviewed. 1. Operating decision: define the decision, service or process in scope and identify its accountable owner. 2. Use-case design: document the user, problem, workflow, expected output and human intervention point. 3. Evidence and data: assess sources, quality, access, integration requirements and known limitations. 4. Governance and risk: assign sponsorship, approval rights, controls, exception routes and escalation. 5. Portfolio decision: compare candidates by strategic relevance, evidence, feasibility, dependencies and risk. The output is not a technology catalogue. It is a transparent decision record. A less visible case may rank ahead of a popular one because it has a defined owner, an observable workflow and an evidence base that supports testing. Conversely, an attractive case may be deferred because the data, controls or decision rights are not ready. Delivery sequence: from demand capture to controlled progression Phase 1: Frame the mandate and decision standard. The study begins with a sponsor session that defines the decision to be supported, the functions in scope, the stakeholders who will review the output and the evidence standard for progression. Candidate cases are gathered from material operating needs rather than vendor features. Phase 2: Build decision-grade case files. Each candidate is documented against the operating issue, intended user, affected decision, current workflow, data requirement, interfaces, control point and open assumptions. A proposition without sufficient support remains a research hypothesis; it is not represented as an implementation recommendation. Phase 3: Test readiness, accountability and safeguards. The review assesses data fitness and accessibility, relevant capabilities and dependencies, then establishes ownership and intervention logic. For each priority candidate, it defines what needs to be tested, who accepts the result, which conditions trigger escalation and what prevents transition into live operation. Phase 4: Present a governed portfolio. Leadership receives a portfolio that distinguishes conditional starts, controlled pilots, foundational work and no-go cases. The roadmap identifies decision owners, evidence requirements and review gates for each wave rather than assigning unsupported benefit projections. The proposed Googan advisory scope A client appointing a consulting office does not need a generic list of AI opportunities. It needs a study that connects operating demand, data, ownership, governance and an investment decision. The proposed Googan scope is designed to create that connection and produce an auditable basis for a procurement, pilot or capital-allocation discussion. The scope can be configured for government bodies, industrial enterprises, financial firms and investors before they commit to delivery. An executive alignment session can define the decision question, candidate cases, evidence available and the authority that will approve the next stage. Sources: SDAIA AI Adoption Framework: https://sdaia.gov.sa/en/SDAIA/about/Files/AIAdoptionFramework.pdf | SDAIA AI Ethics Principles: https://sdaia.gov.sa/en/SDAIA/about/Documents/ai-principles.pdf