How to Choose an AI Consulting Company (Without Falling for Buzzwords)

Every AI consulting website says roughly the same thing: end-to-end, cutting-edge, tailored solutions, trusted partner. None of that helps you choose. What helps is asking questions that are uncomfortable to answer vaguely.

Here's a practical way to evaluate AI consulting companies before you commit budget.

Start by deciding what you're actually buying

AI consulting firms cluster into three types, and mismatching type to need is the most common expensive mistake:

• Strategy houses. Great at market positioning and board-level roadmaps. Usually don't build.
• Delivery shops. Engineers who ship working systems. Less useful if you need help deciding what to do.
• Product resellers. Implement one vendor's platform. Cheap and fast when their platform fits; a poor fit otherwise.

If you know the problem and need it solved, you want a delivery shop. Write that down before you take any calls, because everyone will present as all three.

Consultancy, agency, or freelancer?

The labels blur, but the operating model doesn't. An AI consulting agency usually pairs strategy with a production team and works in phases. A boutique consultancy is a small group of senior people doing the work themselves. A freelancer is cheapest and fastest for one narrow build, and the riskiest for anything that has to keep running.

Match the model to the size of the problem: one workflow suits a freelancer or boutique, several connected systems suit an agency with bench depth.

Does "AI consulting near me" matter?

Mostly no. Almost all of this work is delivered remotely, and restricting your search to an AI consulting company near you shrinks the pool for no gain in quality. Local matters in three cases: your data can't leave a jurisdiction, your team genuinely needs on-site workshops, or you want same-timezone response during rollout. Otherwise, weight timezone overlap and industry experience above postcode.

The questions that separate them

Ask each of these and note how specific the answer is.

"What will be running in production when this engagement ends?" A good answer names systems, users, and outputs. A weak answer describes phases and frameworks.

"Walk me through a project that didn't work." Everyone has one. Firms that can describe a failure honestly — what they misjudged, what they changed — are the ones who scope realistically.

"What parts of this are off-the-shelf?" Custom work everywhere is a margin strategy, not an engineering decision. You want a firm that will happily tell you a €40/month tool solves 70% of it.

"Who is actually doing the work?" Meet the engineers, not just the partner in the pitch. Ask what percentage of their time is on your project.

"What does handover include?" Source code, documentation, credentials, and a training session should be standard. If they're negotiable extras, that's a lock-in model.

"How do we measure success in 90 days?" Push for a number: hours saved, error rate, turnaround time. "Improved efficiency" isn't a metric.

Red flags worth walking away from

• No discovery before a price. A quote produced without seeing your workflows is a guess dressed as a commitment.
• AI as the answer to every question. Sometimes the fix is a rules-based script or fixing the data entry form. A firm that never recommends the boring solution is selling, not advising.
• Metrics with no baseline. "10x productivity" means nothing without the before number and how it was measured.
• Case studies with no client, no numbers, no dates. Anonymised is fine; unfalsifiable is not.
• A proposal that's all phases and no deliverables. Phase names are not outputs.
• Pressure to sign a twelve-month contract first. Good partners are happy to start small.

If a firm cannot describe what will break and how they'll handle it, they haven't run enough of these in production.

How to read a proposal

Print it and mark every sentence as either a deliverable (something that will exist), an activity (something someone will do), or filler. A healthy proposal is mostly deliverables. If it's mostly activities, you're buying time, not outcomes — which is fine, but price it accordingly.

Then check for: acceptance criteria, a named point of contact, assumptions listed explicitly, what happens on scope change, and running costs after launch. Missing assumptions are the usual source of later disputes.

A simple scorecard

Score each firm 1–5 and compare:

1. 1Named, relevant delivery experience in your type of workflow
2. 2Specificity of the proposed first deliverable
3. 3Willingness to recommend cheaper or off-the-shelf options
4. 4Access to the actual engineers
5. 5Clarity of handover and ownership terms
6. 6Realistic, measurable 90-day success criteria
7. 7Ability to start small without a long lock-in

Anything scoring poorly on 3, 4, or 7 tends to get expensive later, regardless of the headline price.

Test with a small engagement first

The cheapest due diligence available is a paid audit or a single small build. You learn how they communicate, whether estimates hold, and whether their engineers are as good as the pitch — for a fraction of the cost of finding out mid-programme.

Our own approach to this is on the how we work page, and past outcomes are documented in our case studies.

The short version

Choose on specificity, not vocabulary. The firm that tells you what will be live, who will build it, what it will cost to run, and which parts you shouldn't build at all is almost always the right one — even when their deck is duller.