AI delivery

A six-week playbook for shipping AI copilots with confidence

Turn ambiguous AI ideas into measurable launches. This playbook shows how we validate value, design with safety rails, and go live within a single quarter.

Published on: May 14, 20248 min read

#AI#Product#Delivery

1. Align on outcomes, not features

We start by mapping desired business outcomes to user jobs-to-be-done. Each potential AI interaction gets scored on impact, confidence, and effort so teams focus on launches that matter.

Interview users, agents, and executives to understand friction points.
Translate findings into success metrics paired with leading indicators.
Score opportunities against guardrails like compliance, LLM readiness, and data availability.

2. Prototype with real data

Static mockups fall short when evaluating copilots. We prototype with staged data and prompt variants inside a shared notebook so stakeholders see realistic responses early.

Run RAG experiments with anonymised data to validate recall and precision.
Document hallucination cases and mitigation tactics inside the prototype.
Use short video walkthroughs to collect async feedback without derailing velocity.

3. Build evaluation into delivery

Shipping AI without instrumentation invites surprises. We build automated eval suites, human review workflows, and observability dashboards alongside the product to keep quality high post-launch.

Set thresholds for acceptance metrics like accuracy, escalation rate, and latency.
Create a lightweight human-in-the-loop queue for flagged conversations.
Instrument prompts, embeddings, and integrations for trend monitoring.

Key takeaways

Six-week cadence ensures every sprint has a measurable deliverable.
Prototype with production-like data to expose failure modes early.
Evaluation harnesses are not an afterthought—they launch with the product.