A strategy is a list of choices
An AI strategy does not have to be a thick report. It has to say what you do first, what you leave for later and what you do not do. We make those choices explicit, with a reason for each, so management and teams work from the same list.
When an AI strategy fits
WHEN IT FITS
It fits when there are many ideas and no order, when several teams experiment separately, or when the board asks what the plan is. If you already know your first use case, go straight to implementation.
Use cases in order of value
We collect use cases per department and score each on impact, effort and risk. The result is a short list to start with and a longer one for later.
Data and systems
No AI system performs better than the data it gets. We check where the data lives, how clean it is and what has to be connected before a use case can work.
AI governance
Who may use which tool with which data, who is accountable for the output and how you check it. We write this down before a model reaches production, in line with the rules that apply to your organisation.
Build, buy or wait
For each use case we advise whether a standard tool is enough, whether it needs custom work, or whether it is better to wait. Not everything has to be done now.
How a strategy project runs
Conversations. With management and with the teams, about goals, processes and concerns.
Inventory. Use cases, data and systems in one overview.
Ranking. Each use case scored on impact, effort and risk, discussed with the owners.
Roadmap. What you do in which quarter, who owns it and what it requires.
AI in practice
Looking for an order in your AI ideas?
Tell us where your organisation stands and what the board wants to know. We reply with a proposal for a strategy project.
