AI Strategy & Use Case Design is a strategic advisory service that addresses the most consequential and most frequently skipped step in enterprise AI adoption: deciding, with rigour and discipline, which AI investments are actually worth making, in what sequence, and why. The service exists because the technology market is structured to answer this question badly. AI vendors lead with capability demonstrations and productivity claims. Consultancies lead with maturity frameworks and competitor benchmarks. Internal champions lead with enthusiasm and proof-of-concept momentum. None of these perspectives begins where strategy must begin: with a clear-eyed assessment of where the organisation creates value, where human and operational friction is most costly, and where AI capability can address that friction in ways that are durable, defensible, and proportionate to the investment required.
The central insight of this service is that AI strategy is not primarily a technology question. It is a business strategy question with technology implications. Organisations that approach it as a technology question end up with impressive capability demonstrations, modest productivity gains, and a portfolio of AI investments that are individually justifiable but collectively incoherent. Organisations that approach it as a strategy question end up with a smaller number of higher-conviction investments that trace directly to the sources of competitive advantage they are trying to build or protect, and a sequencing logic that compounds value rather than dispersing it.
This service is the natural front door for organisations engaging the full suite of services. The clarity it produces about where AI should be deployed, what capabilities it will require, and what the operating model implications will be is the prerequisite for every downstream investment — in technology alignment, structural redesign, leadership development, and cognitive fitness.
What It Includes
AI Opportunity Landscape Assessment A structured assessment of where AI capability creates meaningful opportunity across the organisation’s value chain, operating model, and competitive environment. This is not an exercise in cataloguing available AI tools. It is an exercise in mapping the organisation’s most significant sources of friction, inefficiency, and competitive disadvantage against the categories of problem that current AI capability can credibly address. Drawing on Porter’s value chain framework and Jobs-to-be-Done methodology, this assessment produces a prioritised opportunity landscape that is grounded in the organisation’s specific strategic context rather than derived from generic AI productivity claims. The output distinguishes between AI opportunities that are strategically differentiated, those that are competitively necessary but not differentiating, and those that are operationally attractive but strategically neutral — a distinction that fundamentally shapes investment prioritisation.
Use Case Evaluation and Prioritisation A rigorous multi-criteria evaluation of the AI use cases identified in the Opportunity Landscape, producing a prioritised portfolio with explicit investment rationale. Drawing on a structured evaluation framework that assesses use cases across five dimensions, strategic value, feasibility, time to value, risk profile, and capability dependency, this evaluation produces a defensible prioritisation that can withstand CFO scrutiny and board-level challenge. Critically, this evaluation explicitly addresses the use cases that are technically attractive but strategically inadvisable: those that create vendor dependency without strategic benefit, those that automate processes that should be redesigned rather than accelerated, and those that produce short-term productivity gains at the cost of the human capabilities that long-term competitive advantage depends on. The Cognitive-First perspective that runs through the full service portfolio is integrated into the evaluation framework as an explicit dimension of use case assessment.
Build, Buy, or Partner Framework A structured decision framework for determining the right sourcing strategy for each prioritised use case. The build-buy-partner decision is one of the most consequential in AI strategy and one of the most frequently made on the basis of incomplete analysis. Organisations overbuild when they should partner, overbuy when they should build, and under-invest in the internal capability development that makes any sourcing strategy sustainable. This framework provides the analytical structure for making these decisions explicitly, drawing on Teece’s dynamic capabilities theory and strategic outsourcing research to assess which AI capabilities represent genuine sources of competitive advantage that should be owned, which represent commodity infrastructure that should be procured, and which represent specialised functions that should be developed in partnership with providers who share the strategic interest in the outcome.
AI Investment Roadmap and Sequencing Design Design of a sequenced AI investment roadmap that compounds value rather than dispersing it. Sequencing is the most undervalued dimension of AI strategy. The order in which AI investments are made determines whether they build on each other, sharing data infrastructure, developing common governance capabilities, and building organisational confidence and capability, or exist as isolated initiatives that require the organisation to start learning from scratch with each new investment. Drawing on platform strategy principles and the operating model redesign framework developed in Organisational & Operating Models, this service designs a sequencing logic that identifies the foundational investments that make subsequent investments more valuable, the quick wins that build organisational confidence and executive commitment, and the strategic bets that should be deferred until the organisational conditions for success are in place.
Investment Case Architecture Development of the structured investment cases required to secure executive and board commitment to the prioritised AI portfolio. Investment cases for AI are frequently too optimistic about benefits, too vague about costs, and too silent about the organisational capability requirements that determine whether projected benefits are ever realised. This service develops investment cases that are analytically credible, risk-transparent, and grounded in the full cost of successful adoption, including the structural, leadership, and cognitive investments that the technology investment depends on. Drawing on strategic finance principles and real options theory, investment cases are designed to be defensible under the specific scrutiny they will face from CFOs, boards, and sceptical executives who have seen AI productivity claims fail to materialise before.
Outcomes Expected
For the organisation, AI Strategy & Use Case Design produces the upstream clarity that makes every downstream AI investment more likely to succeed. Leadership teams leave with a shared, analytically grounded view of where AI creates real strategic value for their organisation, not for a generic competitor, and a sequenced investment portfolio that they can explain and defend. The organisation stops making AI decisions reactively, in response to vendor pitches and competitor announcements, and starts making them proactively, on the basis of a strategic logic that is uniquely their own.
For the AI adoption programme, this service provides the strategic foundation that gives every subsequent investment, in technology alignment, structural redesign, governance, and human capability development, a coherent rationale and a clear relationship to competitive advantage. Teams implementing AI know why they are implementing it, which investments take priority, and what success actually looks like in terms that connect to the organisation’s strategic ambitions rather than its technology roadmap.