“Which model is best?” is the wrong first question for most teams.
A team spends weeks debating leaderboard rankings before they’ve even defined what a good answer looks like. Then they pick a model, build a demo, and wonder why it falls apart with real tasks. Sound familiar?
The model matters, but it’s not the foundation of your AI strategy. What lasts isn’t the model—it’s the workflow around it. A better model can’t guess the context your team never provided. A leaderboard update won’t fix unclear instructions or missing checks.
The workflow determines whether you can trust the output.
Here’s what I mean:
- Define what a useful answer must include.
- Give the model the context a new teammate would need.
- Test outputs against real tasks, not just demos.
- Name the person who approves the result.
If switching models means rebuilding everything, you built too much around the model. A modular workflow—one where prompts, tools, and checks aren’t tied to a specific version—lets you compare models without starting from scratch. But even then, treat model switching as an option, not a promise. Each change needs testing.
Human review isn’t a failure of AI. For consequential work, it’s how your team stays accountable. I’ve seen teams call an answer “good” without checking whether it’s accurate, useful, or safe. That’s not AI’s fault—it’s a process gap.
Leaderboards change. Your standards for good work shouldn’t. Start with the work, not the ranking. Ask:
- What does this output need to do?
- What context is missing?
- Who owns the result?
Then—and only then—compare models. Capability, cost, latency, and data requirements still matter. But they’re secondary to a workflow that ensures quality, no matter which model you use.