A Pragmatic Playbook for AI Adoption

The fastest way to waste a quarter is to start an AI project with the model instead of the problem. Pragmatic adoption flips that order: decide what outcome is worth money, then ask whether AI is the cheapest reliable path to it.

Start with a decision, not a demo

Every useful AI feature changes a decision someone is already making — a support agent's reply, a developer's next line, a buyer's recommendation. If you can't name the decision and who owns it, you're building a demo, not a product.

• Name the decision and its current cost (time, money, error rate).
• Estimate the value of making it faster or better.
• Only then choose between rules, classical ML, or an LLM.

Prefer boring infrastructure

The model is the exciting part and usually the least of your problems. Evaluation harnesses, data plumbing, and guardrails are where projects live or die. Treat them as first-class from day one.

If you can't measure whether the AI got better, you can't ship it responsibly — you can only hope.

Build an evaluation loop early

A small, version-controlled set of representative cases beats a vague sense that 'it seems smarter now.' Run it on every prompt or model change and track the numbers like you track latency.

cases = load_eval_set("support_replies.jsonl")
score = sum(grade(model(c.input), c.expected) for c in cases) / len(cases)
assert score >= BASELINE, f"regression: {score:.2f} < {BASELINE:.2f}"

Ship the smallest useful slice

Put a narrow version in front of real users behind a flag. Real usage surfaces the failure modes no offline eval predicted, and it earns the trust you'll need to expand scope.