Series
From AI Idea to Production
A twenty-part series on what it takes to move an AI tool from a promising idea to something a regulated company can run every day. The longer versions here go deeper, with examples from apps I have built.
I wrote it for engineers, product owners, and team leads who have seen an AI demo work and now have to ship the real thing. It assumes you know how software gets built and spends its time on what AI changes: models that guess when they lack information, data that may hold patient details, and output that needs a person to check it.
Read here
- Part 5 of 20 · 8 minute readSpec Before Code: Writing Down What the AI May and May Not DoWhy an AI project needs a written spec before any code, and what it should cover: data, limits, human review, testing, and cost. Part 5 of 20.
- Part 6 of 20 · 12 minute readBuilding Beyond the DemoA demo proves an AI idea can work. Production proves it keeps working. The six layers I add before I call an AI system production-ready.
17 of 20 parts on LinkedIn
Each part first ran as a LinkedIn post. Read them there.
- Part 1 Should This Even Be an AI Project?
- Part 2 Prioritizing AI Use Cases
- Part 3 Discovery Before Architecture
- Part 4 Choosing the Right Solution
- Part 5 Spec Before Code
- Part 6 Building Beyond the Demo
- Part 7 Testing AI Systems Properly
- Part 8 QA Is Not the Builder Grading Their Own Work
- Part 9 Integration and Dev Environments
- Part 10 UAT Is Where the Real Workflow Appears
- Part 11 Production Readiness
- Part 12 Post-Production Improvement
- Part 13 Progressive Autonomy
- Part 14 Measuring Green-Dollar ROI
- Part 15 Measuring Blue-Dollar ROI
- Part 16 From Pilot to Enterprise Platform
- Part 17 Why Great AI Demos Fail in Production
Coming next
- Part 18 Designing for Failure and Recovery
- Part 19 What Enterprise-Ready Actually Means
- Part 20 The Full Lifecycle Recap