How to choose an AI vendor – 10-criteria checklist for mid-sized B2B companies

The AI implementation market in Poland (and most of Europe) looks like it exploded last year. Four distinct types of vendors operate in it, all using similar marketing language but delivering very different value. The first group is product vendors – companies offering a specific tool (Microsoft, OpenAI, Anthropic) or a partner-led implementation of one. The second is software houses that can write any code, including LLM integrations. The third is consulting agencies delivering strategy and often outsourcing execution. The fourth – implementation consultancy with in-house engineers – combines strategy with delivery in one team.

For a mid-sized B2B company (50–500 people) the fourth type usually fits best. A product vendor tries to sell a license — regardless of whether it answers a real problem. A software house will write code but won't design the process or run delivery operationally. A pure consultancy produces great strategy, but execution ends up with another team that doesn't know the context. Implementation consultancy with in-house engineers owns the project from strategy to production.

The 10 criteria below let you compare candidates for AI implementation partner in a measurable way. Score each on a 1–5 scale and sum — a vendor below 35 points should probably not make the shortlist.

The two most important criteria are experience with a similar type of company and clarity of the proposed architecture. They differentiate candidates most when everyone has a polished website full of AI buzzwords.

The first conversation with a potential partner is not a technology demo. It's a conversation about your organization, your process and your business context. A good AI vendor asks more questions than you do. If the conversation looks like a monologue about LLM capabilities, it's a bad conversation.

Below 8 questions worth asking every candidate. The answers let you quickly assess whether the partner has real operational experience or just marketing.

During the selection process signals appear that should end conversations with a candidate regardless of how attractive their proposal looks. Each red flag below has occurred in real projects and each one led to a failed deployment.

The strongest red flag: the vendor can't tell you which projects are NOT worth pursuing. Every AI deployment has unsuitable cases. A partner who says „we'll do anything” in practice can't distinguish valuable automation from interesting demonstration.

An RFP is the document you send to 3–5 candidates after initial selection. A good RFP gives vendors enough context to prepare a realistic proposal, but not so much that everyone returns identical marketing answers.

Below is the minimum structure of an RFP for AI implementation in a mid-sized B2B company. The document should be 6–10 pages — no longer. Long RFPs scare off the best vendors (they have plenty to choose from).

After receiving proposals, compare them in a spreadsheet against the criteria from the RFP. The most common mistake: evaluating only price and time. AI deployments with the worst ROI are often the cheapest ones — because they were designed quickly and don't account for real organizational context.

We recommend comparison across 5 dimensions: fit with the problem, realism of the proposed solution, team quality (CVs of team members are worth more than the company logo), commercial model and risks, quality of communication during the sales process.