For boards: AI and strategy
For boards under pressure to adopt AI who would rather know before they spend.
AI & Strategy
You leave with one decision instead of a list of options: what we do this year, what we deliberately do not, and how we will know it worked.
- Two hours with your board: where you actually stand, and what follows from it.AIM6™ Executive Briefing · 2 hrs
- A full assessment in 4–6 weeks, ending in a plan: what to do, in what order, at what cost.AIM6™ Assessment Pro
- A part-time AI chief — boardroom experience without a permanent hire.Fractional CAIO
Three sizes of company, three different first steps.
A company setting prices in spreadsheets. A manufacturer whose board had reached a turning point. A listed company with nine systems, each telling a different story. See which one looks like your situation — the first step was different in every case. Client names are left out on purpose.
Prices and margins from a single source of truth
Prices set in spreadsheets, margin known after the fact, four pricing roles with no shared data. The board wanted pricing decisions based on numbers, not instinct.
- First step
- A paid diagnosis and a data model built from scratch
- What was built
- A data platform on Microsoft Fabric (Direct Lake), a pricing engine, 112 measures, pricing roles captured in architecture decision records
- My role
- Solution architect and program lead, two phases in six months
Transforming production management
Falling margins, an ERP used everywhere except production, no bills of materials. The board did not need a system. It needed a new way of running the company.
- First step
- A production expert review and implementation analysis before anyone said “system”
- What was built
- Production re-implemented in the ERP, a new company strategy and margin strategy, BOMs, an organizational structure refined over four iterations, departmental cost analyses
- My role
- Transformation lead and advisor to the board for the whole change
A data warehouse on Azure: from team to go-live
A legacy warehouse inherited from two previous vendors, a new ERP, nine source systems and dozens of operational reports the business uses daily. There was no team.
- First step
- Recruit the team and set the architecture before the first pipeline ran
- What was built
- A Bronze / Silver / Gold lakehouse (Data Factory, Databricks), 9 sources in one model, Power BI semantic models, QA and PROD environments with CI/CD
- My role
- Data Delivery Manager and Solution Manager; I selected and recruited the team myself
Flagship transformation (delivered): a research institute, budget ≈ €230K / PLN 1M. Details and a reference available in our meeting.
Client names are left out on purpose. References are shared directly, with client consent.