Developing an AI Strategy

AI strategy in one sentence

An AI strategy defines which business goals AI should support, which use cases get implemented in what order, how much you build yourselves, and which rules and metrics you use to steer. Everything else — tool selection, license models, model versions — is implementation, not strategy.

The difference matters in practice. Start with the tool question and you end up with a collection of subscriptions. Start with the goal question and you get a decision logic that still holds even if the vendor landscape has shifted twelve months from now. This article assumes you already have some initial experience — for the operational starting point, see Introducing AI in Your Company.

Component 1: Aligning with business goals

Don't start with AI — start with the two to four goals that already apply for the current fiscal year: margin, growth in a segment, throughput time, customer satisfaction, skills shortage. For each goal, ask the same question: Which bottleneck is preventing this goal from being reached, and is that bottleneck information- or text-heavy?

Only if the answer to the second part is yes is AI even the right lever. A bottleneck in the supply chain won't be solved by a language model. A bottleneck in quote creation, support volume, or content production very much can be.

The result of this step is a short goal hierarchy: Business goal → Bottleneck → AI contribution → Owner. Initiatives that can't be traced back to a business goal don't belong in the portfolio — no matter how impressive the demo was.

Component 2: Prioritizing use cases

Four evaluation dimensions are enough for prioritization, each rated on a scale of 1 to 5:

The value results from value contribution × frequency, divided by the effort from data readiness and risk. What matters is less the exact formula than the discipline of evaluating every proposal by the same criteria — including those coming from management.

Turn this into a portfolio across three horizons: short-term efficiency cases with reliable feasibility, mid-term process overhauls with data integration, and one or two long-term initiatives that touch the business model. Without the third horizon, you're only optimizing the status quo; without the first two, you lack the funding for it.

Component 3: Buy, Configure, or Build

The question of build depth determines cost, speed, and dependency. Three levels are enough:

The realistic answer for most companies is Configure: your own assistants with embedded company knowledge, connected via automation platforms. For what this connection looks like technically, see AI Automation; a comparison of the common platforms is in n8n vs. Zapier vs. Make.

Every build decision should account for two side constraints: model interchangeability — build so that switching providers doesn't mean a rebuild — and operating cost per transaction, because what looks cheap in a test with a hundred runs can tip the economics at a hundred thousand.

Component 4: Governance that doesn't slow you down

Governance has a bad reputation in AI projects because it often shows up as an after-the-fact list of prohibitions. It works when it defines clear degrees of freedom up front. Five elements are enough:

Risk classification per use case. Assign each use case to one of three internal classes — uncritical, review-required, approval-required — and derive the level of control from that. The regulatory basis for this is the EU AI Act.

Data rules. Which data categories may go into which system, on what contractual basis. Details in AI and Data Protection.

Quality assurance. A defined review step with a named role for every production use case. Who reviews what, how often, and what happens on deviation.

Competence. Role-based training instead of a one-size-fits-all mandatory session — users, owners, and leadership need different levels of depth.

Transparency. A maintained registry of all production AI applications with purpose, system, data basis, and owner. This registry is the one governance component you need in every case — for audits, for an overview, and to avoid duplicate development.

Component 5: Measurability

The most common measurement mistake is confusing usage with impact. Active users and prompt counts say nothing about business contribution. Use three levels instead:

1. Adoption (early indicator): share of the target group using the use case day to day. Explanatory power: low, but available early.
2. Process impact (core level): throughput time, processing time per transaction, error or rework rate, output volume — each measured against the baseline captured before the project started.
3. Business impact (goal level): cost per transaction, revenue per employee, response time to customers. This level takes time and is rarely cleanly isolated — document the assumptions instead of claiming causality.

Set one core metric per use case at level two and review it quarterly. And deliberately plan for an end: use cases that show no impact after two quarters get shut down instead of quietly kept funded.

The strategy on one page

A viable AI strategy fits on one page and answers six questions:

• What for? Two to four business goals with a named bottleneck.
• What? A portfolio of prioritized use cases across three horizons.
• How deep? A fundamental Buy/Configure/Build decision per case.
• By what rules? Risk classes, data rules, review steps, registry.
• Who? An owner per use case plus one coordinating role.
• How do we recognize success? One core metric per case, reviewed quarterly.

Everything beyond that is implementation planning. The strategy itself needs to hold for a year — the tool selection underneath it can change at any time.

Conclusion

An AI strategy isn't a technology selection — it's a prioritization and control logic. It starts with business goals, evaluates use cases by consistent criteria, makes a deliberate decision on build depth, sets up governance as a framework of freedom rather than a list of prohibitions, and measures process impact instead of usage numbers. The most valuable part of it is the ability to end initiatives again — because capacity, not a lack of ideas, is the real bottleneck.