You know AI is going to change your market — just not yet what, in which order and with which risks. We build an AI strategy that takes into account your data, your people, your regulations and your budget. And we don't walk away after the first presentation: we stay on to build it with you.
Most organisations that come to us have the same story. The board wants "something with AI". One department is already experimenting with ChatGPT, another team has quietly started a Copilot pilot, and the CIO is worried about data leaks. Everyone has an opinion, but nobody has the overview. The question you really want answered is: where can AI deliver measurable value in the coming period, without exposing you to legal or reputational risk?
A good AI strategy gives a well-founded answer to that. Not a slide deck full of buzzwords, but a concrete roadmap that takes your people, your infrastructure and your regulatory context as its starting point. We develop that strategy and, what sets us apart, we then help build the first pilots. Our advice isn't handed over to an implementation partner; we get our hands dirty in the system ourselves. That keeps us honest too: an adviser who never writes code finds it much easier to recommend something that won't work.
An AI strategy differs from a digital transformation programme. Digital transformation covers technology, processes and people broadly; an AI strategy zooms in specifically on where machine learning, language models and agents can add value, and which preconditions are missing in your organisation to make that possible. Many clients run both programmes in parallel or in sequence, particularly when their broader digital maturity is still limited.
Depending on where you stand, an AI strategy starts with sharper insight, a focused roadmap, or straight away with building the first pilot. We often work through them in this order, but not always.
We map out where your organisation stands today: data maturity, infrastructure, team skills, governance and culture. Not a questionnaire with scores from 1 to 5, but conversations with IT, business owners and management, combined with a short analysis of your actual data and systems. The result: an honest view of what is feasible and what needs to be solved first. Often, data quality, accessibility or organisational ownership prove more important than the choice of model.
Which processes or products could benefit from AI, and which couldn't? We score opportunities on three axes: potential value (cost savings, revenue growth, customer experience), implementation complexity and risk. That overview gives us a phased roadmap: which pilots to start first for quick wins, which larger transformations to save for later, and which ideas to drop because they promise more risk than return. We also attach a rough first investment estimate so that management can turn the roadmap into budget discussions.
We help build the first concrete application from the roadmap. That could be an AI agent that takes over a specific process, an internal knowledge assistant for your staff, or an extension of existing software with AI functionality. It is not intended as a finished product, but as evidence that the strategy works in your reality. During this phase we sharpen the strategy based on what we learn, because in AI projects theory and practice diverge more often than in classic software implementations.
An AI strategy that sits on the board table, with enough detail beneath it to take the first steps the next morning. No 80-page report that nobody opens.
Four signals we often see in organisations that are ready for a strategy engagement. If you recognise one or more, we'd be happy to talk further.
The board expects action, but there is no plan. You want to prevent AI from becoming a collection of scattered experiments without a shared direction. The C-suite is looking for justification for budget allocation.
Different departments are experimenting with ChatGPT, Copilot or industry-specific AI tools. Nobody has visibility over which data ends up where, which contracts have been signed, or what is actually being delivered.
You want to know in advance which planned AI applications fall into high-risk categories, what documentation you need to keep, and how to organise that in practice, before the regulation catches up with you.
Market players claim impressive AI results in pitches and webinars. You want to be able to separate marketing from reality, and decide which claims you can make yourself without damaging your credibility.
With OneDayBuild, we turn your idea into something tangible in one day for €1,150, so you can see whether further development is worth the investment. Decide to go ahead with the full build? Then we credit the full cost.
Explore OneDayBuild →A conversation in which we understand where your question comes from, who is around the table, and which decision you want to be able to make on the basis of the strategy. No sales pitch. We will also tell you honestly if you are not yet ready for a strategy engagement.
Interviews with IT, business owners and leadership. A brief data audit of a sample of your systems. Workshops to bring use cases to the surface. At the end of this phase: a shared picture of what is feasible and which opportunities look most promising.
We build the roadmap with dependencies, risk classification and build-versus-buy decisions for each use case. At the same time we develop the governance structure: who decides, which guardrails apply, which yardstick we use. A closing session with leadership to secure commitment.
We build the first pilot from the roadmap, or guide your own team through it, whichever suits you. Afterwards we remain available for the next hurdle: new use cases, AI Act documentation, or a revision of the strategy should the landscape shift again.
Four things we do differently from the average strategy consultancy, and which we make explicit so you know exactly what you are buying.
Our background is software engineering, not PowerPoint. A strategy we deliver is workable because we understand the implementation, and because we often build the first pilot ourselves. That keeps our advice honest and grounded.
We have no partner contract that pushes us towards a single AI vendor. For each use case, we make the choice on the merits: reasoning quality, data residency, cost model, or a multi-vendor setup to avoid lock-in.
Not every process is improved by AI. Sometimes a good search function is more effective than a chatbot. Sometimes workflow automation without a language model is faster, cheaper and easier to explain. We state this explicitly in the roadmap.
The EU AI Act in practice, GDPR implications of American AI vendors, sector regulators (DNB, NZa, AP), and model performance in Dutch. This is built into our engagements as standard, not as an appendix but as a design parameter.
What decision-makers ask us before an AI strategy engagement begins.
A free, no-obligation half-hour introductory call. We listen to where your organisation stands and where the pressure is coming from, and give you direction you can act on straight away, even if we turn out not to be the right partner for the next step. An AI strategy often ties in with broader decisions; we're happy to discuss how it fits with your digital transformation programme.
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