How to Evaluate an AI Tool

“What questions should I ask before buying an AI tool?” is the right question. The wrong question is “is X better than Y?” — that depends on your data, your stack, your team, and what you’ll use it for. This guide is the structured evaluation framework: 7 weighted criteria, red-flag signals, and the legal / ethical questions that should be on every buyer’s checklist.

Score any vendor with our AI tool evaluation scorecard — it forces the same structured thinking you’d get from a good procurement consultant for free.

The 7-criteria framework

Score any AI tool 1–5 across these seven, weighted by importance:

• Privacy + data handling (×3): Does it train on your data? Where’s data stored? Who has access? Retention policy? Is there an opt-out? Is the no-train guarantee in the contract or just the marketing copy?
• Output quality in your tests (×3): Run the tool on your actual data. Vendor demos are curated to make the model look 30–50% better than reality. Test against the failure modes you actually care about.
• Integration cost (×2): Engineering hours to wire it into your existing stack. Auth, data flow, observability, error handling. A tool with great quality but 200 hours of integration is sometimes worse than a weaker tool with native integrations.
• 12-month TCO (×2): License fees + per-seat + per-token + ops + training. Most published “cheap” AI tools are expensive at production volume. Run the math at your expected utilization.
• Vendor stability (×2): Funding stage, runway, customer count, recent layoffs. AI startups in 2026 are a graveyard waiting to happen — picking a vendor that disappears in 18 months is expensive.
• Compliance fit (×2): SOC 2 Type II, HIPAA, GDPR, sector- specific certifications. Not the marketing badge — the actual audit reports.
• Switching cost (×1): Data export format, contract lock-in, prompt portability. The cheapest mistake is overpaying. The most expensive is being stuck with a tool you can’t leave.

Questions to ask before buying

1. “Can we run a paid pilot with our data before committing?” Real vendors say yes. Vendors that resist are flagging that demo-quality won’t hold up.
2. “What’s your data retention policy?” Should be specific: how long, where, who can access. “We follow industry best practices” is not an answer.
3. “Will my data be used to train your models?” If yes, walk away (or use a different tier). If no, get it in writing.
4. “What happens to my data if I cancel?” Deletion timeline + verification mechanism. Some vendors retain “de-identified” data forever; clarify what that means.
5. “Do you have a SOC 2 Type II report we can review under NDA?” A real cert comes with an audit report. A badge alone is just a logo.
6. “What’s your latest customer-funded ARR? Customer count?” Vendors at <$5M ARR or <100 customers carry higher disappearance risk.
7. “Show me the data export format.” Should be clean JSON or CSV, not vendor-specific binary. Otherwise switching costs explode.
8. “What’s your model upgrade cadence?” If the underlying model gets swapped quarterly, your output quality may drift in ways that surprise you. Some vendors lock to a specific model version; others rotate.
9. “If we discover the tool isn’t working, what’s the cancellation process?” Net-30, net-90, auto-renew clauses. Annual contracts often have surprise auto-renewal terms.
10. “Can I talk to a customer using this for [my exact use case]?” Specificity matters — “a customer in your industry” is good but “a customer using this for the exact workflow you’ll use it for” is better.

How to compare fintech and vertical AI tools

Domain-specific AI tools (fintech, healthcare AI, legal AI) have additional considerations:

• Domain expertise of the team. The founders should have worked in your industry. Generalist AI engineers building “AI for finance” without finance experience often miss compliance edge cases.
• Regulatory familiarity. For fintech specifically: familiarity with FINRA, SEC, PCI-DSS, KYC/AML obligations. Ask how they handle each one in their product.
• Audit trails. Regulated industries need records of every decision the AI made. “The model said yes” isn’t enough. Look for tools that log inputs, model version, output, and human review.
• Liability framing. Who’s liable if the AI makes a bad recommendation? Most vendors disclaim all liability; in regulated industries this might be a deal-breaker.
• Reference customers in regulated peers. A bank vouching for a fintech AI tool is worth ten generic enterprise references.

For currency / international payment tools specifically: ask about exchange rate transparency, hidden FX margins, and whether they support all the currencies you actually need (not just the marketing top-10).

Legal risks to know about using AI in business

The five areas to clear with legal before deploying AI in customer-facing contexts:

1. Data privacy laws. GDPR (EU), CCPA (California), state-by- state US patchwork, sector-specific (HIPAA for healthcare, GLBA for finance). AI processing of personal data triggers most of these.
2. Copyright + IP. AI-generated content has murky copyright status. The US Copyright Office has ruled that purely AI-generated works aren’t copyrightable. Substantial human authorship may be. Document your editing process.
3. Disclosure requirements. Some jurisdictions require AI disclosure when AI is making consequential decisions about people (hiring, credit, healthcare). Check your jurisdiction.
4. Output liability. If your AI hallucinates and a customer relies on the false info, who’s liable? Most vendor contracts disclaim liability; you may carry it. Plan accordingly.
5. Bias / discrimination. AI-driven hiring, lending, and housing decisions are subject to existing anti-discrimination laws (Title VII, ECOA, Fair Housing Act). The AI doesn’t exempt you.

Ethical issues before deploying AI

• Transparency with users. Disclose AI involvement when customers interact with it. Hidden AI is a trust killer when discovered.
• Human review on consequential decisions. Hiring, firing, lending, healthcare — these need a human in the loop. AI as advisor, not decider.
• Bias testing. Run your AI against representative samples from groups that historically face discrimination in your domain. Document the results.
• Worker impact. AI deployment displacing employees deserves a genuine conversation, not just a memo. Reskilling, transition support, clear comms.
• Environmental impact. LLM inference has a real carbon cost. Consider this in tool selection at high-volume use cases.
• Consent for data use. Train AI on customer data only with clear consent. Repurposing existing data for AI training without re-consenting is a violation in most jurisdictions.