AI strategy for the mid-market: prioritising beats vision documents

When the word strategy comes up, dense documents with technology radars and maturity models quickly follow. For small and mid-sized companies that is usually wasted time: the bottleneck is not a missing strategy paper but the missing decision on where to start – and who stands behind it.

Why an AI strategy looks different in the mid-market

Large corporations can run technology trials in parallel across several areas. The mid-market has neither the budget nor the staff for that; the consequence: few projects, cleanly selected, carried through to a running result. A good AI strategy is therefore more of a filter than a roadmap: it says what you start with – and what you deliberately do not.

The three questions an AI strategy must answer

• Where do we regularly tie up time or money today? Not where AI could theoretically help, but where the effort is measurable: receipts, enquiries, data preparation, reports.
• Which data and systems are available for it? A use case without usable data stays theory – the quality of the data decides the order.
• Who is accountable for the result? A name, not a committee. Without an owner, every solution goes unused.

Prioritising use cases: four criteria

• Frequency: how often does the task occur? Daily tasks pay off earlier than annual ones.
• Effort today: how many hours or euros does it currently cost – estimated honestly, not smoothed over?
• Data situation: does the necessary information already exist in structured form, or would it have to be built up first?
• Risk: what happens if the result is wrong? The higher the impact, the later in the order – or with an approval step before it.

Combined, these criteria usually produce a small number of sensible starting points: frequent, costly, with usable data and manageable consequences. That is exactly where the first pilot begins.

From plan to result: a 90-day rhythm

Instead of an annual plan, a short cycle has proven itself:

• Weeks 1–2: select one use case, define the goal and metric (e.g. handling time per enquiry).
• Weeks 3–10: build the pilot – small enough to test, concrete enough to measure.
• Weeks 11–12: evaluate and decide: expand, adjust or stop. A cleanly stopped pilot is also a result.

After two or three cycles, a strategy emerges from the experience that fits the company's reality – not the other way around.

Common mistakes in AI strategies

• Technology first: starting from a tool and then looking for a process for it inverts the logic and usually ends with solutions nobody adopts.
• Everything at once: ten use cases in the pipeline means, in practice, zero running solutions.
• Ownership by committee: strategy papers without named owners do not get implemented.
• Success without a metric: if you never measured the baseline, you cannot show afterwards what it achieved – and lose internal support.
• Set up once, never revisited: the strategy should be sharpened with every completed cycle.

When external support makes sense

For prioritisation, a moderated workshop with the people who know the processes is often enough. External help pays off when implementation capacity is missing or system knowledge is needed for integration – not to write the strategy. When commissioning external consultants, make sure the outcome is a running pilot, not another document.