AI strategy and discovery
From hundreds of ideas to a roadmap the board can fund
Discovery produces breadth. Prioritisation turns it into decisions: ideas consolidated into a small set of well-formed use cases, each written in your own investment format, assigned an adoption pathway, and sequenced by readiness into a roadmap with a first quarter you can start on Monday.
Two hundred ideas is not a submission list
Assessing every idea individually is useful for understanding the shape of the opportunity, but it is no way to decide. Many ideas call on the same underlying capability, the same language-model licence, the same knowledge platform, the same productivity tooling, and differ only in who is using it and for what. Submitting them one by one understates their value and overwhelms the assessment process in equal measure.
So the findings are consolidated. Ideas that share a capability and can be delivered together become a single consolidated use case, which is the unit written up in full and put forward for investment. One funded capability returns benefit many times over across the business, and one well-made case is far stronger than a dozen thin ones. Every individual idea stays recorded and traceable beneath it; the business decides at the level of capability, not the level of the sticky note.
Written to stand up to scrutiny
The problem and the opportunity
In the words of the people who raised it: the pain, how the work is done today, and what the AI capability would change.
What it consolidates
Every underlying idea, by reference, with the function it came from, so the "one licence, many benefits" case is explicit and auditable.
The convergence scorecard
Priority, strategic fit, feasibility, complexity, cost, benefit, risks, data position, vendor options and recommendation, on the same scale as every other use case.
Adoption pathway and pyramid layer
Research-led, product-led, system-embedded or data-foundation; and where it sits on the Opportunity Pyramid, so time-to-deploy is explicit.
Governance fields
Risk category, data protection, human oversight and transparency, which most investment templates lack and the EU AI Act makes useful.
Your format, not ours
Mapped onto your existing technology-investment or innovation template so it enters your process without rework. Fields we cannot fix in a workshop are flagged for the owner, never guessed.
Grouped by readiness, not by enthusiasm
These are priorities, not fixed phases: different teams progress different groups in parallel, and the data foundation runs underneath them all.
Ready now
High value, low complexity, data in hand or blocked only by an access decision. The productivity assistant, content tooling, and the data-in-hand proofs of value. The first quarter.
Nearly ready
High value, needing data preparation or coordination first: the knowledge platform, the master-data foundation, work already in flight that needs coordinating rather than re-funding.
Strategic
High value but data-gated and complex, phased through bounded pilots: forecasting and inventory, factory and energy optimisation, visual inspection.
Research
Highest differentiation, longest horizon, funded as research or a market scan until the evidence supports a business case: embedded and on-device AI, design automation, digital twins.
A credible start, not a grand plan
Every roadmap ends with a recommended first quarter: the governance decision that unlocks the rest, two or three switch-on wins, two or three data-in-hand proofs of value, and the foundations started in parallel. The aim is visible value from day one while the ground for the larger prizes is being laid, and a business that can see its own programme working rather than waiting for it.
Where the business has an existing evaluation framework, our scores are offered as a recommended straw man for participants to confirm; the formal assessment stays with the business's own process.
About prioritisation and business cases
Can you prioritise ideas we gathered ourselves?
Yes, provided each idea has enough capture to score: what the pain is, how it is done today, and what AI would do. If it does not, a short convergent workshop per function fills the gaps.
Do you assign owners?
No. Owning departments, contacts and accountable stakeholders are yours to determine; we flag the fields rather than pre-fill them.
How precise are the cost and benefit figures?
At this stage they are relative, business-calibrated bands for prioritisation, not financial estimates. They are firmed up as each use case moves into its business case, usually with a data audit as the first task.
What if the top-scoring ideas are not ready?
That is the most common finding, and the reason the roadmap groups by readiness. The highest-appetite themes enter a data-foundation or research pathway now so they can deliver later, while the ready opportunities deliver value immediately.
Turn the ideas into decisions
If discovery has happened, in any form, this is the step that makes it fundable.