From pilot to production: an evaluation-first approach to enterprise AI

Why pilots stall

A pilot that impresses in a demo can still fail in production. Typical causes are unclear success criteria, unrepresentative test data, no plan for monitoring and no owner once the project team leaves.

Start with an evaluation set

Collect a representative sample of real cases with known correct outcomes: documents with verified extracted fields, questions with approved answers, or tickets with correct classifications. This set becomes the yardstick for every design choice.

Agree thresholds in business terms, for example "95% field-level accuracy on invoices, with low-confidence cases routed to a reviewer".

Ground and measure generative AI

For assistants and search, retrieval-augmented generation keeps answers tied to your own approved sources. Measure groundedness (is the answer supported by the retrieved text), answer relevance and refusal behaviour when information is missing, alongside latency and cost per request.

Engineer for production

Production AI needs versioned prompts and models, automated evaluation in the deployment pipeline, monitoring for drift and quality, access control and logging. Human review should be designed into the workflow for high-impact decisions.

Under data-protection laws such as the NDPA 2023, organisations should also keep an inventory of AI systems, assess their risk and document how they are overseen.