Phase 2 of the AI adoption strategy
Once the assessment is done, Phase 2 turns the opportunity map into a roadmap you can execute. AI strategy design isn’t an academic exercise — it’s a concrete definition of what gets done, in what order, with what resources, and against what success criteria.
A good AI strategy answers five questions. Which use cases do we tackle first? Why in that order and not another? What enabling investments do we need before we can take on the priority use cases? How will we know it’s working? And what do we do if it doesn’t work as expected?
Evaluating and prioritizing use cases
The assessment produces an opportunity map. Strategy design turns that map into a sequence of prioritized projects, using four criteria:
Business impact. The use case’s potential value in lower costs, higher revenue, or reduced risk. We quantify it as an order of magnitude, not an exact figure — false precision at this stage is a common mistake.
Technical feasibility. Given the current state of the data and technology infrastructure, how hard is this use case to implement? Feasibility covers data availability, integration complexity, and how mature the solutions on the market are.
Speed to value. Not every project pays off on the same timeline. Projects with faster returns make better first initiatives: they generate positive cash flow that can fund more complex projects, and they prove value internally, which keeps the program going.
Strategic sequencing. Some projects are prerequisites for others. A data quality project may have to come before a predictive model that depends on that data. The roadmap has to respect those dependencies.
What the strategy includes
Project roadmap. The sequence of initiatives, with their order, dependencies, the enabling investments they need, and review milestones. The roadmap is a guide, not a contract — it has to be flexible enough to absorb what we learn during implementation.
Project briefs. For each initiative on the roadmap: the use case, expected business value, data and technology requirements, project scope, and success KPIs. These briefs underpin investment decisions and vendor selection.
Enabling investment plan. AI projects often require prior investment in data, infrastructure, or training. The strategy spells out those enabling investments and sequences them correctly.
AI governance model. Who decides on AI investments, who evaluates results, and how a project gets scaled up or redirected when it doesn’t deliver the expected return.
A roadmap isn’t a project list
A project list says what you want to do. A roadmap explains why in that order, what depends on what, and how we’ll review and adjust the plan. We build our roadmaps as living documents, not files that get written and shelved.
The full sequence
Strategy design is Phase 2 of the AI adoption process. It builds on the Phase 1 assessment and produces what Phase 3, implementation, needs to get started.