AI & data strategy: from fragmentation to focus in two to four weeks.

from data
to direction

Four weeks.
Four answers.

The design phase is not a months-long project. In four weeks we shadow your team on the work floor, speak with the people who do the work and quantify the opportunities. Each week answers one question.

Week 1

How do you really work?

shadowing days on the work floor

observe
Week 2

Where does it hurt?

interviews with the people who do the work

listen
Week 3

What is it worth?

weighing opportunities on impact and feasibility

quantify
Week 4

Where do we start?

business case and concrete starting point

decide
2-4 weeks

from first conversation to a substantiated business case

On facts

shadowing days and analysis, no assumptions from a distance

No report

a concrete starting point you can act on tomorrow

Strategy without assumptions, that matches reality.

Many organisations start with AI because they feel they have to. They buy tooling, launch pilots and get stuck.

We start with the question behind the question. With shadowing days, interviews and analysis of what already exists. Only once we understand where the real opportunities are do we build a strategy that holds up.

The result isn't a thick report that disappears in a drawer. It's a clear business case: what it delivers, what it costs, what the risks are and where you start.

You'll recognise one of these situations

Serious about AI, but where to start?
Pilots that don't land or scale
An AI roadmap built on facts, not promises
A major data or platform decision

Our approach

Two to four weeks that set the direction. Well-founded, not based on assumptions.

We embed in the organisation. We observe processes, ask the questions an external consultant wouldn't, and look at where data is actually generated, used or ignored.

We talk to the people who do the work and the people who decide. Where's the frustration, where are the ambitions, and where does that clash with daily practice?

We test the opportunities against what's technically feasible and what the data allows. No assumptions, but a well-founded picture of what's really possible.

A clear business case: what it delivers, what it costs, what the risks are and where you start. Concrete enough to act on tomorrow.

Direction you can move forward with

Not a thick report, but a concrete starting point. After the design phase you know exactly where you stand and what the next step is.

Colleagues in discussion at a screen
01

Which AI and data opportunities deliver the most.

02

What the technical and organisational preconditions are.

03

In what order to start, and why.

04

What a realistic path from idea to production looks like.

Related expertise

A concrete AI challenge?Interested?Get a call back
Eduard van Pagée

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