1. Diagnose
Interviews with managers, a survey of activities and pain points, an inventory of existing tools and data. We look for tasks that cost time, generate errors or hold up a decision.
Deliverable: a shared picture of the stakes and the data.
2. Prioritize
A workshop with leadership. Each initiative is assessed on its value, its feasibility and its risk. Feasibility depends mostly on data: does it exist, is it accessible, who owns it?
Deliverable: a short, ranked list of initiatives.
3. Plan
A phased roadmap with milestones. We estimate the effort for each initiative rather than promise dates. And we start with low-cost tools, moving to heavier solutions once the value is proven.
Deliverable: a roadmap approved by leadership.
4. Govern
An AI usage policy, data classification rules (what can go to an external model, what must stay in-house), and approval points before anything goes into production.
Deliverable: simple rules the teams know.
Every step ends with a deliverable and a decision: continue, adjust or stop.
What comes next
A roadmap only matters if the first project succeeds. We recommend starting with a use case whose problem, data and owner are already clear, and taking it all the way to production. Our case study on reporting for a major industrial group shows how.
Want a roadmap your leadership can follow? Let’s start with 30 minutes.
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