AI Strategy

A robust AI strategy answers four questions: Where are we now (processes, data, systems, skills)? Where do we want to be (business goals that AI should support)? What exactly are we doing (prioritised use cases with roadmap)? And how do we ensure it works (accountability, policies, data protection, training)? It's less a technology document than an execution plan.

For mid-market companies: the strategy must not be a 50-page paper that ends up in a drawer. A compact document with clear prioritisation, reviewed quarterly, has proven effective — AI development moves too fast for rigid multi-year plans.

The best strategy emerges not from theory but from interplay with early execution: a pilot project yields insights that sharpen the strategy; the strategy gives the next pilot its direction. This iterative approach prevents both classic mistakes — years of strategising without action and unfocused tool adoption without a plan.

Beyond use cases, the framework conditions belong in the strategy: binding rules for AI use (which tools, which data), capability building within the team per Article 4 of the AI Act, and decisions on which skills to build internally and which to source externally.

Every AI strategy must answer which capabilities to build internally and which to outsource. The honest reality for mid-market companies: in-house AI developers are expensive, hard to find and oversized for most firms' needs — yet total reliance on a service provider creates vulnerability. A pragmatic middle ground works best: outsource development and technology, but retain process knowledge, control over rules and system operation internally. Your company doesn't need to build what it uses — but it must understand what its systems do and be able to operate them day-to-day.

Choosing a partner concretely means: prioritise documentation and handover capability (could another vendor take over the system?), insist on training your own team and keep access and data in-house. Internally, it pays to develop one or two AI stewards — employees with an affinity for the topic who act as a bridge between departments and the service provider, make small adjustments themselves and spot new use cases. This role isn't full-time, but it makes the difference between a company that owns AI and one that controls it.

AI strategies repeat two opposite mistakes. One is over-planning: a comprehensive strategy paper that sits in a drawer after completion while technology evolves and the plans become obsolete before the first project starts. The other is pure tool experimentation — each department introduces its own tools without shared direction, rules or priorities. Both extremes waste money and credibility: one through paralysis, the other through disorder.

The answer is a deliberately lean but binding strategy, reviewed regularly — and this is where leadership's real work lies. An AI strategy can't be delegated to IT or a service provider because it touches fundamental questions only the leadership can answer: which business goals should AI support, what priorities apply, what rules govern data use and protection, and what happens to freed-up work time?

Equally important is visible leadership commitment. Where executives make AI a priority, set direction and use the tools themselves, the strategy gains traction. Where leadership delegates and stays remote, even the best strategy remains a paper without impact.

An AI strategy needs someone inside who lives it — not a full-time role, but a named responsibility. AI stewards are employees with an affinity for the topic who bridge between departments and external partners. They understand the running systems, handle small adjustments themselves, spot new use cases and are the first point of contact for team questions. This role often emerges naturally from whoever was earliest to experiment with new tools.

The value lies in independence. A company that calls the vendor for every adjustment doesn't have an AI system — it has a dependency. Internal capability allows faster response, vendor switching without starting over and self-directed development. The investment is manageable: knowledge transfer during setup, regular exchange with the partner and access to your own systems and data. Building and supporting this role makes the difference between a company using AI and one that controls it.