Choosing an AI vendor is really a risk and operating model decision
Before buying an AI tool, test one real task against its data terms, output quality, integration effort and review controls. Ask the vendor for evidence that applies to the exact product tier you would use.
Evaluate each vendor against your workflow, governance requirements, data handling obligations and implementation needs, as well as its features.
Include client work, internal controls and accountability in the review for professional services, finance, legal and regulated workflows.
The four checks that matter before you buy
Review these four areas before buying:
- Governance: can the vendor explain how outputs are controlled, reviewed, monitored, and escalated when something goes wrong?
- Security and data handling: do they handle confidential data appropriately, with clear boundaries around storage, model access, retention, and third-party processing?
- Implementation fit: can the product actually work with your systems, team capability, and workflow design, or will it depend on manual workarounds?
- Evidence of value: can the vendor show measurable improvements in efficiency, quality, risk reduction, or turnaround time in use cases that resemble yours?
Record any unanswered questions in these areas before deciding whether to buy.
Stress test AI vendor risk before procurement
What good vendor evaluation looks like in practice
Review the proposed use and its risks before signing the contract.
The evaluation can cover:
- a clear problem definition, so the firm knows what job the AI tool is meant to do
- an assessment of where human review remains necessary
- a check on whether the vendor supports audit trails and traceability
- a review of integration requirements and hidden implementation effort
- a basic test of whether the tool performs reliably on realistic internal scenarios
Include the operational questions in procurement discussions.
The governance questions firms should ask every AI vendor
Ask the vendor to explain accountability, handling of incorrect outputs and controls over time in plain English.
Useful questions include:
- What decisions can the system make or recommend, and what still requires human sign-off?
- How are inaccurate, low-confidence, or inappropriate outputs detected?
- What logging is available for prompts, outputs, user actions, and overrides?
- How often does the model, product logic, or workflow behaviour change?
- What is the escalation path if the tool creates a compliance, quality, or client risk issue?
- Can the system support policy-based controls for different user groups or workflows?
Record the answers and any control gaps for the proposed workflow.
Put governance around vendor selection and AI rollout
Why security claims on their own are not enough
Check security claims against the controls needed for the proposed use.
For example, a vendor might have sensible infrastructure security but weak controls over prompt leakage, poor access segregation, unclear retention settings, or no meaningful review workflow for sensitive outputs.
Review the vendor's controls alongside the firm's workflow design.
How to judge implementation fit before procurement
Estimate the change management, data preparation, process redesign and human oversight involved in fitting the tool to the workflow.
Before buying, check:
- what systems need to integrate
- what data needs cleaning or structuring
- what internal team will own rollout
- what controls need to be designed before go-live
- what exceptions and fallbacks must exist when the AI cannot complete the task safely
Ask the vendor to explain the implementation effort and the support included.
A simple AI vendor evaluation checklist
- Define the business problem and expected outcome clearly
- Assess whether the use case is low, medium, or high risk
- Review governance controls, review points, and accountability
- Check confidentiality, retention, access, and third-party handling
- Validate auditability and logging capability
- Test implementation requirements against current systems and workflows
- Ask for evidence from comparable use cases, not generic claims
- Run a scoped proof of value with realistic success criteria
- Confirm who owns monitoring, exceptions, and change control after deployment
Use the checklist results when comparing vendors.
Move from vendor shortlist to workable implementation
Conclusion
Compare governance, security, implementation fit and evidence of value alongside product features.
Record how each shortlisted vendor would support the workflow and who would be accountable for deployment.
Need a safer AI route for your firm?
If this article reflects a live decision in your practice, the useful next step is to map the workflows, confidentiality risks, supplier controls and governance gaps before tools spread informally.
