AI strategy consulting

What is AI strategy?

AI strategy turns a general intention, “we should be doing something with AI”, into a short list of decisions: which problems to solve, which to leave alone, what data and skills each one needs, how success will be measured and who is accountable. It is a business plan with a technical spine, not a catalogue of technologies.

The goal is to put money where it returns the most and to stop early the ideas that will not. Most organizations have more candidate use cases than delivery capacity. Without a method to compare them, the loudest idea or the most impressive demo wins, and the team only learns the real constraints after months of work.

The scope covers four questions: where value sits in your processes, whether your data can support each idea, what the organization needs in skills, tools and governance, and in which order to build. It is the entry point of our AI consulting practice, and its output feeds directly into machine learning, predictive models or custom applications.

Signs you need an AI strategy

Most companies do not lack AI ideas. They lack a way to choose between them and the conditions to deliver the ones they choose. These are the signs that tell us a strategy engagement will pay for itself.

• Several pilots run in parallel with no common criteria

  Each department experiments on its own, with different tools and success measures, so no result can be compared or scaled.

• The board wants an AI plan by a fixed date

  Leadership needs a defensible answer, with costs and expected value, rather than a list of vendor demos.

• Nobody knows whether the data is good enough

  Ideas are discussed without checking that the data exists, is accessible, is labeled and can legally be used for that purpose.

• Staff already use public AI tools without rules

  Employees paste documents into consumer assistants because no approved alternative or policy exists, and the exposure grows every week.

• A vendor proposal needs an independent view

  You have a quote for an AI platform or project and want to know whether it solves the right problem at the right cost.

The fourth sign is often the most urgent, because it means confidential data may be leaving the company today. In that case the engagement starts with a short acceptable use policy and an approved alternative, while the longer work on use cases continues in parallel.

What our AI strategy assessment covers

The assessment combines interviews with the people who run the processes and a hands-on look at the data. Opinions about what AI could do are tested against what your systems actually contain, and every finding is written down so it can be challenged.

• Process mapping and opportunity inventory

  Workshops with operational teams to find repetitive decisions, document-heavy tasks and forecasting gaps, each described with its current cost.

• Data readiness audit per use case

  Availability, quality, history, labeling and legal basis checked for each candidate, with what is missing and how long fixing it would take.

• Use cases ranked on one grid

  Value, feasibility and time to value scored the same way for every idea, so the ranking can be explained and revisited.

• Build, buy or wait recommendation

  For each priority use case, whether an existing product, a custom build or no AI at all is the better answer.

• Governance and operating model

  Ownership, approval steps, risk classification, acceptable use policy and the skills to hire, train or borrow.

The result is a roadmap your leadership can approve and your teams can start on: a ranked list, the data work each item needs, an estimated effort per item, and the first use case framed in enough detail to launch it without another round of workshops.

How we build the roadmap: four phases

Each phase narrows the field. We start wide, with every idea on the table, and end with a short list that has been checked against data, cost and risk by the people who will have to deliver it.

The ranking session is where most of the value appears. Business owners see their ideas next to everyone else’s, scored on the same criteria, and the conversation moves from enthusiasm to trade-offs. We facilitate it, but the decisions belong to your leadership team, which is what makes the roadmap hold once we leave.

Which AI use cases should come first?

We score each candidate on a small number of criteria and look at the combination, not at a single total. A high-value idea with no usable data is a data project first, not an AI project, and the roadmap should treat it that way.

The first use case should be valuable enough to matter and safe enough to learn from. Ideas that score high on value but low on readiness go into a data preparation track, so they become feasible later instead of vanishing from the plan. Once framed, a priority item moves into machine learning delivery or a custom application build.

Risks and pitfalls of AI strategy

AI strategies fail in recognizable ways. We name these risks at the start of the engagement so the roadmap is built to avoid them, and we come back to them at each phase review.

• A strategy written around a technology

  Starting from a model or a platform produces solutions looking for problems. We start from processes and what they cost.

• A roadmap with no owners

  Use cases without a named business owner stall at the first hard decision. Each priority item gets one before the roadmap is signed.

• Underestimating data and integration work

  The model is often the smaller part. Data preparation, integration and change management take most of the effort, and the plan says so.

• Leaving regulation until late

  Personal data rules and sector regulations, including the EU AI Act where it applies, shape what can be built. They are checked during ranking.

• Measuring activity instead of outcomes

  The number of pilots is not progress. Each use case carries a business metric and a date to check it.

A good strategy is also willing to say no. When a simple rule, a better report or a process change solves the problem, we recommend that instead, and the roadmap is shorter and cheaper for it. Leadership gets fewer projects, each with a clearer reason to exist.

AI strategy pricing

Every engagement starts with a diagnosis and ends with a fixed quote. Durations are typical, not promised.

What changes the price: number of departments involved, number of candidate use cases, state of the data documentation, and whether a proof of concept is included.

The quote covers senior consultants only, a defined scope and the deliverables: opportunity inventory, data readiness findings, ranked roadmap and governance outline. Software licences, model usage fees and cloud costs for any proof of concept are separate from our fees and estimated before they are incurred.

AI strategy FAQ