How to Choose an AI Model for a Business Use Case

Choosing an AI model is not about picking the leaderboard winner. It is about matching a business use case to quality, cost, latency, and risk.

Use this scorecard to decide without endless demos.

Start from the job, not the brand

Write one sentence:

“When [user] needs [outcome], the model must [constraints].”

Examples:

• Support agent: accurate, cite policy, under 3s, low cost at volume
• Legal draft assist: careful tone, human review required, strong reasoning
• Classification: tiny latency budget, cheap model, high precision

The five criteria that matter

1. Task quality on your examples (not Twitter screenshots)
2. Latency at p95 for the UX you promised
3. Cost per successful task
4. Privacy & region (data processing agreements, residency)
5. Operability (uptime, rate limits, fallback models)

Build a tiny eval set first

Collect 30–100 real prompts with “good enough” answers. Score models blindly:

• Correctness
• Hallucination / policy violations
• Format compliance
• Tone

The cheapest model that passes your bar usually beats the smartest model you cannot afford at scale.

Routing beats monogamy

Mature products use model routing:

• Cheap/fast model for classification and short drafts
• Stronger model for hard reasoning
• Fallback provider when the primary is down

Your backend should abstract providers so switching is a config change.

Privacy and on-prem notes

If data cannot leave your VPC, shortlist models you can host or contractually lock down. “Best chatbot” is irrelevant if legal will block the launch.

Decision template

Use case	Prefer	Avoid
High volume triage	Small/cheap + RAG	Always-on frontier model
Complex analysis	Frontier / large reasoning	Tiny local model alone
Strict privacy	Enterprise/private deploy	Consumer free tiers

Bottom line

Choose an AI model for a business use case by measuring success on your data under your constraints. Leaderboards inform; evals decide.