AI consulting for mid-market companies: what it costs and what you actually get

If you are a CEO of a 50 to 500 person company and you search for AI consulting this week, you will find roughly the same range of pricing you found six months ago. And roughly the same confusion about what you are actually buying.

The floor is a solo consultant with a slide deck and a rate card. The ceiling is a Big Four team with a methodology, a brand, and a six-figure engagement letter. The middle is full of boutique firms, managed service providers, and technical shops that sound identical in their proposals and deliver radically different results.

All three exist. None of them are the same product. The industry calls them all AI consulting and expects you to figure out the difference between a diagnostic, a deployment, and a training workshop based on marketing language.

You cannot. Not from the outside. Not without someone who has been inside multiple engagements and can tell you what actually happens after the proposal is signed.

The first thing that separates real consulting from theater is what the person has built.

Not recommended. Not studied. Not designed on a whiteboard during a workshop. Built. Deployed. Run in production on real company data with real process constraints. Found out what was wrong with the original plan. Fixed it.

There is a difference between a consultant who knows what AI can do and a consultant who has made it work inside a company with legacy systems, undocumented processes, and people who have been doing things a certain way for eleven years. The first can write a report. The second can tell you which spreadsheet three people maintain in three different formats, which senior analyst runs a critical process entirely in her head, and which approval chain is actually fear wearing a responsible suit.

The ones who have built things will show you specific examples. Not case studies with the company names redacted and the results described in terms like improved efficiency and reduced processing time. Concrete examples. A process they mapped. A system they designed. A result they held themselves accountable for.

If a consultant cannot give you a specific example of something they built in a company your size, you are not buying consulting. You are buying a hypothesis.

The second thing that matters is scope clarity.

Most AI consulting engagements fail at the boundary between diagnosis and execution. The consultant comes in, maps the situation, delivers a report, and leaves. The report is good. The recommendations are clear. The execution is not.

This is not always bad faith. It is a structural problem. Diagnosis and execution are different skills, different timelines, and different risk profiles. A team designed to assess is not necessarily a team designed to build. The deliverables reflect that: maturity scores, roadmaps, priority matrices. Useful artifacts for someone who already knows how to execute them. Not useful for a CEO who needs someone to actually do the work.

The engagements that work connect the two. The person who maps the processes stays to design the systems. The person who identifies the bottlenecks stays to remove them. The report is not the end product. It is the starting point for the build phase.

That is a different business model. It requires someone who is willing to hold accountability for a result instead of a deliverable. Most consulting firms are not structured that way. Their margins depend on diagnosing, recommending, and moving to the next engagement. The ones who stay and build operate on a different economic model: fewer clients, deeper work, pricing tied to scope rather than time.

Here is what the pricing landscape actually looks like.

Solo consultants and independents tend to charge 200 to 500 euros per day or 2,000 to 8,000 euros for a fixed-scope diagnostic. The quality varies enormously. Some are genuine senior operators with deep domain experience. Others are people who attended a conference, read a few whitepapers, and decided to reposition their existing consulting practice. The only reliable filter is whether they have built things, not just talked about them.

Boutique firms and specialized AI consultancies typically range from 15,000 to 60,000 euros for a diagnostic-plus-first-implementation engagement. The higher end includes technical deployment work, not just analysis. The differentiator at this level is whether the firm has a repeatable methodology or is winging it with every client. Ask to see their intervention framework. If they cannot articulate how they go from initial assessment to first deployment, the price is not justified by the process.

Large consultancies and Big Four firms price from 50,000 to 200,000 euros or more for multi-week assessments. The deliverable is comprehensive. The methodology is thorough. The team is experienced. The structural gap remains: the people who do the assessment are not the people who build the systems, and the engagement model is designed to produce recommendations, not operational results.

None of these prices are inherently wrong. They are different products for different needs. The problem is that the industry does not label them clearly, and the procurement process treats them as interchangeable.

Solve IT prices its diagnostic engagement, called a War Map, at 9,500 euros. Fixed price. Six weeks. The deliverable is a prioritized intervention plan: what to build, what to kill, what to launch in 30, 60, and 90 days. The fee is credited against implementation if the company moves forward. The total engagement typically runs four to six months.

The guarantee is specific: if the War Map does not produce a clear next step the CEO wants to execute, the fee is multiplied by five and returned. That is not a marketing claim. It is a risk reversal. It means the diagnostic has to work, or it costs the company nothing and costs five times the fee.

The pricing is transparent because the alternative is not. The industry is full of assessments that start at one price and expand into multi-phase engagements that cost five or ten times the original estimate. That is a business model. It is not the only one.

The question you should ask before signing any AI consulting engagement is not what does it cost. It is what happens on day one after the deliverable lands.

Does the person who wrote the plan stay to build the first thing? Do they know where the data actually lives, not where the IT diagram says it lives? Have they deployed something similar in a company your size, with your mix of legacy systems and informal processes? Can they show you a specific example of a process they mapped, a system they designed, and a result they held themselves accountable for?

If the answer to any of these is no, you are not buying execution capability. You are buying analysis. There is nothing wrong with that, as long as you know what you are buying.

The cost of getting it wrong is not the consulting fee. It is the six to eighteen months of inaction that follows a diagnostic that produced analysis but no next step. That is the real price. And it is the most expensive one.