Before You Start
Before writing a single line of code, align on three things: the business problem you are solving (not the technology you want to use), the success metric you will track, and the stakeholders who need to be involved. Skip this step and you risk building something nobody needs.
Weeks 1-2: Discovery
Week 1: Deep dive into the current process. Shadow the people who do this work today. Document every step, decision point, and edge case. Measure the current baseline — how long does it take, what does it cost, what error rate exists?
Week 2: Define the AI solution architecture. Select models, design the data pipeline, plan integrations, and identify risks. Create a technical design document and review it with the team.
Weeks 3-4: MVP Build
Build the minimum viable AI workflow. Focus on the happy path — the 80% of cases that are straightforward. Use existing AI APIs and tools; don't build custom infrastructure yet. The goal is a working end-to-end pipeline that you can demonstrate to stakeholders.
Key principle: get to a demo as fast as possible. A working demo builds momentum and stakeholder confidence far more than a detailed plan.
Weeks 5-6: Testing
Test with real data, not synthetic samples. Create an evaluation dataset of 50-100 representative examples. Measure accuracy, consistency, and edge case handling. Involve the end users who will work with the system daily — their feedback is more valuable than any benchmark.
Iterate rapidly: identify failure modes, improve prompts or logic, and re-test. Most systems need 2-3 iteration cycles to reach production quality.
Weeks 7-8: Deployment
Deploy to production with monitoring from day one. Start with a limited rollout — perhaps one team or one process variant. Monitor closely for errors, user confusion, and unexpected behaviour. Have a rollback plan ready.
Key deployment checklist: error handling and fallbacks, monitoring dashboards, alerting for anomalies, usage tracking, user documentation and training, and a feedback mechanism for users to flag issues.
Scaling Beyond the Pilot
Once the pilot proves value, scale systematically. First, harden the pilot — improve edge case handling, add better monitoring, and document operational procedures. Then, identify adjacent use cases that can reuse the same infrastructure and patterns.
Common scaling pattern: the first use case takes 6-8 weeks. The second takes 3-4 weeks (you reuse infrastructure). By the fifth, you are deploying in 1-2 weeks.
Measuring Success
Track both operational metrics (time saved, error rate reduction, throughput increase) and business metrics (revenue impact, cost savings, customer satisfaction). The operational metrics prove the AI works; the business metrics justify continued investment.
Report results monthly to stakeholders. Be honest about limitations and failures alongside successes. Trust is built through transparency, not cherry-picked metrics.