AI Automation Vendor Evaluation: A Checklist for Enterprise Buyers

What this checklist is for

You are shortlisting vendors for an AI automation programme. It probably spans several capabilities at once: document processing with OCR and model-based extraction, workflow automation across back-office processes, and a conversational agent connected to systems your teams already use.

You have constraints. Data residency. Audit requirements. A timeline. A budget. Existing systems that the result has to fit into.

The difficulty is that vendor websites answer none of this. They describe capabilities in language chosen to survive any requirement. This checklist is what we would use to cut through that, including when evaluating ourselves.

1. Scope and capability fit

Establish what is actually being proposed before anything else.

• Which parts of the scope does the vendor build themselves, and which are licensed or subcontracted?
• Have they delivered this combination together, or only the components separately?
• What is explicitly out of scope in their proposal?
• What will the system be unable to do in year one?

The last question is the most revealing. A vendor who cannot name a limitation has either not thought about your problem or is not being straight with you.

2. Document processing specifics

Document extraction is where projects most often underdeliver, because demo accuracy and production accuracy diverge sharply.

• What accuracy do you expect on our document types, and how is that measured?
• How do you handle poor scans, handwriting, stamps, and mixed-language documents?
• What happens to a document the system is not confident about?
• Is there a human review queue, and how does confidence routing decide what enters it?
• How does accuracy improve over time, and who does that work?
• Can you run extraction on a sample of our real documents before contract?

That final point is the strongest signal available. A vendor confident in their extraction will run your documents. Test with genuinely difficult examples, not clean ones.

3. Workflow and process automation

• How do you discover and document the current process before automating it?
• What happens when an upstream system is unavailable mid-process?
• How are exceptions surfaced, and to whom?
• Can business users change rules, or does every change require the vendor?
• How is the automation monitored once live, and who is alerted when it fails?

That fourth question determines your long-run cost. If every rule change is a vendor engagement, you have bought a dependency rather than a capability.

4. Conversational agent requirements

• Which systems will the agent read from and write to?
• How are permissions enforced so a user sees only what they should?
• What is the escalation path to a human, and does it carry full context?
• For multilingual deployments, has it been demonstrated in the actual languages, including code-switching?
• What proportion of conversations should reach a human, and how was that number chosen?

5. Data residency and security

For regulated industries, and for any UAE deployment involving personal data, this determines architecture. Settle it before evaluating anything else.

• Exactly which endpoints will our data reach, and in which country does each run?
• Can you deploy into a UAE region, our own cloud tenancy, or fully on-premise?
• Is personal data redacted or tokenised before reaching a model, and how is that verified?
• What are your contractual terms with model vendors about training on our data?
• What is logged per model call, and for how long is it retained?
• If a regulator asks where a specific transaction was processed, can we answer?

Be precise about the difference between a vendor designing architectures aligned to SOC 2, GDPR, HIPAA, or UAE PDPL controls, and your environment being certified against those frameworks. The first is a capability a vendor can offer. The second is an audit your organisation commissions. A vendor conflating them is a risk signal.

6. Model strategy

• Which models do you propose, and why those?
• Are we locked to one vendor, or can models be swapped as capability and pricing change?
• How do you evaluate model output quality, and can we see the evaluation harness?
• What happens when a model version is deprecated?
• If we require private-hosted open-weight models, what changes about capability and cost?

Model choice moves faster than any other part of the stack. An architecture that assumes today's model is permanent will need rework within the year.

7. Integration reality

• Which of our systems have you integrated with before?
• Do you use supported APIs, or screen-level automation that breaks on UI changes?
• What access will you need in our environment, and for how long?
• How are credentials managed and rotated?
• What happens to integrations when we upgrade an underlying system?

8. Timeline and delivery

• What is the phase structure, and what is delivered at each gate?
• What are the dependencies on our side, and how much of our team's time?
• What is the most likely cause of delay in your experience?
• Which milestones are contractual?

A typical enterprise engagement of this shape runs eight to twelve weeks from discovery to production. Compressed timelines usually mean either reduced scope or deferred integration work, so ask which.

9. Post-launch support

Frequently the least examined and most consequential section.

• What support models do you offer, and what does each cost annually?
• What is the response time for a production failure?
• Who owns model retraining and content updates?
• If we end the relationship, what do we keep, and in what condition?
• Can our team be trained to operate this independently?

Ask specifically about handover, co-managed, and fully managed options. A vendor offering only one is selling a delivery model rather than a fit.

10. Commercial structure

• What is fixed and what is variable?
• What drives cost up after go-live: volume, users, integrations, model usage?
• What are the licence costs we pay directly to third parties?
• What is the total three-year cost, not just implementation?

Model usage costs in particular are frequently underestimated at proposal stage because volume assumptions are optimistic. Ask for the calculation, not just the figure.

Scoring your shortlist

Weight by what actually constrains you. A useful default for regulated environments:

• Data residency and security fit: 25 percent
• Demonstrated capability on your real documents and systems: 25 percent
• Integration depth and approach: 20 percent
• Support model and exit position: 15 percent
• Timeline credibility: 10 percent
• Commercial structure: 5 percent

Commercials rank last deliberately. The cheapest proposal that cannot meet your residency obligations has a score of zero regardless of price.

Where CodexaAI fits

We are a Dubai-based enterprise AI consultancy delivering document processing, workflow automation, and conversational systems for organisations across the UAE and GCC, including regulated sectors where data residency determines architecture.

We deploy into UAE regions, client tenancies, or on-premise with private-hosted models, and we offer handover, co-managed, and fully managed support so the operating model matches your internal capacity. We will run extraction against a sample of your real documents before any contract.

If you are building a shortlist, book a discovery call. We are happy to be evaluated against this checklist alongside anyone else.