How to Choose an AI Vendor for Your Industrial Business

To choose an AI vendor, look less at the technology showcase and more at whether they can solve a specific business problem, with timeline, price and responsibilities in writing. In manufacturing and B2B, good vendors talk about process, data and outcomes; weak ones talk only about models and buzzwords. Here are criteria, questions and red flags.

Criteria for evaluating an AI vendor

They understand your kind of operation

Ask for examples of projects similar in size and sector, without demanding customer names they cannot share. A vendor who has handled quoting, technical catalogs or distributor support asks better questions in the very first meeting.

They start from the problem, not the tool

A good vendor asks how long the task takes today, who does it and what an acceptable result looks like. If the conversation opens with which model to use, the project is upside down.

They treat data and security seriously

They should explain where data lives, who can access it, whether it trains any model and how deletion works. Vague answers, or blanket promises of total security with no detail, are a bad sign.

They deliver in stages, with metrics

Prefer a short pilot with a numeric goal over an open-ended annual contract. The proposal should say how results will be measured and what happens if the goal is missed.

They keep you independent

Confirm who owns the code, prompts, knowledge base and accounts. You must be able to change vendors without losing what you built.

Questions to ask in the meeting

• Which part of the work does the AI do alone, and which goes through human review?
• How do you measure answer quality, and how often do you review it?
• What will this solution not be able to do?
• Who handles maintenance after launch, and what does it cost per month?
• Which integrations with systems like ours have you already built?
• How long until the first result I can see working?

Red flags

• A promise of 100% accuracy or of replacing whole teams.
• A proposal with no written scope, only a total price.
• Refusal to show a demo with your data or close cases.
• Pressure to sign fast under an expiring discount.
• A contract that locks you into the platform with no data export.

A template to compare proposals

Build a simple table with one column per vendor and identical rows: scope, pilot length, deliverables, setup cost, monthly cost, API cost, who maintains it, result target, asset ownership, security and contract exit. Score each row from 1 to 5 and discuss the gaps with your team.

If a vendor leaves a row blank, ask. No answer is an answer. You can see how we structure projects in our AI and automation services and use these same questions with us or anyone else.

Decide with a paid pilot

When down to two finalists, hire a small 30-to-60-day pilot from each, same problem and same goal. The cost of testing is low next to getting the choice wrong for a year.

Frequently asked questions

Should we hire an agency, a consultancy or an AI startup?

The type matters less than experience with your kind of problem and the clarity of the proposal. Evaluate cases, working method and post-launch support.

Is building in-house better?

Only if you have an IT team with time and AI experience. For most small and mid-sized firms, hiring a vendor for the pilot and bringing it in-house later is more realistic.

How do I know the price is fair?

Compare at least two proposals against the same written scope and calculate the 12-month cost, not just the monthly fee.