Strategic AI Adoption Needs an Enterprise Portfolio, Not a Catalogue of Pilots

Strategic AI Adoption Needs an Enterprise Portfolio, Not a Catalogue of Pilots Decision context An AI strategy should not begin with a technology catalogue or vendor demonstrations. It should begin with a defined decision, service or operating process that the organisation intends to improve, together with the evidence needed before procurement, investment or scale. SDAIA’s AI Adoption Framework connects directions, enablers and outcomes across strategy, governance, data, infrastructure, human capability, applications and impact. A use-case list is therefore insufficient when decision ownership, data stewardship and progression gates remain unclear. SDAIA’s AI Ethics Principles add a second discipline: risk management and oversight should run through design, development and post-deployment operation, under defined accountability and appropriate human intervention. Before an RFP or capital commitment, leadership should ask: which use case matters most, who owns the outcome, what limits apply, and what evidence must exist before the organisation moves into live operation? A proposed work model This is a proposed scope of work, not a claim of prior outcomes or a guaranteed return. It can be adapted to the sponsor’s mandate, sector, evidence base and decision stage: • Translate strategic priorities into candidate operating decisions, services or processes. • Describe and rank use cases by user, decision, data, integration burden and risk. • Define operating roles, approval rights, escalation routes, human oversight and performance review. • Assess data fitness, access, technology dependencies, capabilities, resources and partner requirements. • Build a roadmap with stated start conditions, monitoring measures and decision gates: proceed conditionally, pilot, defer or reject. Implementation sequence The engagement begins by framing the mandate, decision owners, available evidence, assessment boundaries and decision standard. Candidate cases are then documented against the problem, beneficiary, decision influenced, data need, workflow, human intervention point, dependencies and risk. A case without sufficient evidence remains a research hypothesis or is deferred; it is not presented as funding-ready. Priority cases are subsequently translated into an operating proposition: who sponsors the case, owns the data, tests it, authorises progression, observes performance and manages exceptions. The final roadmap distinguishes cases that may start once conditions are met, cases that merit a controlled pilot, cases requiring data or governance foundations, and cases that should not proceed at this stage. The proposed Googan scope An AI strategy should not collapse into a procurement catalogue. The proposed scope combines current-state analysis, portfolio design, operating-model design, evidence and risk registers, and decision gates. It can be configured for government entities, enterprises and investors at the strategic-planning stage, before a tender, pilot or capital allocation. 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