AI Experimentation Playbook

The most expensive mistake in AI development is building the wrong thing well. Teams spend months fine-tuning a model before validating that the use case even matters to users. The experimentation playbook prevents this by enforcing hypothesis-driven development from day one.

The Experiment Structure

Every AI experiment follows the same structure, regardless of complexity.

Hypothesis — A falsifiable statement about what the AI will do and why it matters. "Users will complete support tickets 40% faster with AI-suggested responses" is a hypothesis. "Let's try GPT-4" is not.

Success Criteria — Quantitative thresholds that determine whether the experiment succeeded. Defined before any code is written. If you cannot define success criteria, you are not ready to build.

Minimum Viable Experiment — The cheapest, fastest way to test the hypothesis. This might be a Wizard of Oz test (human-powered fake AI), a prototype with a single API call, or a batch evaluation on historical data. The goal is learning, not shipping.

Evaluation — Structured assessment against the success criteria. Includes quantitative metrics (accuracy, latency, cost) and qualitative feedback (user satisfaction, trust, edge cases).

Decision — Ship, iterate, or kill. Based on evidence, not opinion.

Why This Matters

Without a structured experimentation process, AI teams fall into two traps. Either they ship too early (before validating quality) or they iterate forever (because there are no clear success criteria). The playbook creates a forcing function: define what success looks like, test cheaply, decide quickly, move on.

Running Experiments at Speed

We typically run 3–5 AI experiments per month during the Discovery phase. Each experiment takes 3–5 days. By the end of the first month, we have validated (or invalidated) the core AI use cases and know exactly what to build. This is dramatically faster — and cheaper — than the typical approach of building first and validating later.