The market for enterprise AI help is crowded, loud, and — let's be honest — hard to navigate. Every firm promises transformation, every demo looks impressive, and the gap between the ones who can build a proof-of-concept and the ones who can put AI into production is invisible from a sales deck. Since most AI pilots never ship, choosing wrong is expensive: you pay for a partner, get a demo, and never move a business number.
Here is the checklist we'd hand you if we were sitting on your side of the table.
1. A production track record, not a demo reel
Anyone can produce a convincing demo in 2026 — the models are that good. The hard part is everything after: data readiness, evaluation, guardrails, change management, and a clear owner. Ask for case studies with real, measured outcomes, and ask specifically what broke on the way to production and how they handled it. A partner who has only ever built pilots will learn on your budget.
2. Outcome-first, not output-first
The single best filter. Does the firm tie the engagement to a business metric — hours removed, cost per case, cycle time, revenue — and capture the baseline before building? "We'll deploy an LLM" is an output. "We cut the reconciliation backlog and took month-end from nine days to three" is an outcome. If nobody's naming the number, you're buying activity, not value — the same FinOps discipline that keeps AI funded.
3. Data and governance maturity
AI performance is a function of your data, not the model. Probe how they'll ground answers in your trusted data (retrieval-augmented generation), how they'll evaluate quality before and after launch, and how they handle guardrails, security, and compliance (including the EU AI Act). A partner without an evaluation and governance story is a partner who'll ship you confident nonsense.
4. Right-sizing, not frontier-everything
A mature partner reaches for the smallest model that does the job and can defend the cost per use case — not one that bills you frontier prices for work a compact model does just as well. If every answer is "use the biggest model," that's a cost problem waiting to happen.
5. Senior delivery and knowledge transfer
You want experienced practitioners on your problem, not juniors learning on your budget — and a deliberate plan to leave your team more capable, not more dependent. Ask who actually does the work, and what you'll own at the end.
6. Fits the stack you already own
If you're Microsoft-first, a partner who lights up Azure, Copilot and Power Platform — capability you already pay for — beats one selling you a brand-new stack to learn and maintain.
The red flags
- Only demos, never metrics.
- Black-box "trust us" models with no evaluation.
- Vendor or platform lock-in by design.
- Junior-heavy teams behind a senior sales pitch.
- No answer on data governance, security, or the EU AI Act.
Questions to ask before you sign
- Show me a case study where AI reached production — what number did it move, and what was the baseline?
- How will you ground answers in our data, and how will you measure quality?
- What will our team own and be able to run when you leave?
- How do you decide which model to use, and how do you control cost?
- How do governance, security, and compliance get built in?
The bottom line
The firms that impress most in the room are often the ones with the least production experience. Judge an AI consultancy on shipped, measured outcomes — grounded in your data, governed properly, delivered by senior people, and tied to a number your CFO recognises. That's exactly the standard we hold ourselves to in our artificial intelligence work.
Evaluating partners and want a straight, no-hype read on what's actually achievable with your data? Let's talk.