What It Really Takes to Go Live With AI-Built Products
In this Tinker Club session, Leigh-Anne Nugent and Micah Adler get honest about one of the least glamorous but most important parts of building with AI: go-live readiness. Proof of concept is exciting, but moving from “it works in theory” to “it is stable, secure, and usable in production” is a very different challenge. This conversation is a practical look at what breaks, what gets overlooked, and why shipping something real takes more discipline than most people expect.
LESSONS YOU CAN TAKE FROM THIS:
1. AI tools are great for prototypes, but production is a different game
A major takeaway from this session is that AI builders are excellent for proof of concept, early discovery, and validating whether an idea is possible. But once you move into user acceptance testing and go-live prep, the cracks start to show. Security gaps, unstable outputs, broken logic, and edge-case failures all become much more visible when the product has to perform in the real world.
2. Smaller launches are often the smarter path
Both Leigh-Anne and Micah highlight an important product lesson: trying to launch everything at once can keep you from launching at all. A smaller, simpler release with fewer moving parts is often more realistic, easier to test, and faster to improve. That kind of discipline matters, especially when the tooling is evolving so quickly underneath the product itself.
3. Go-live readiness includes governance, not just functionality
This session also surfaces an issue more builders need to think about: if agents or automations are updating files, sheets, or client records, who is actually making those changes? Auditability, ownership, email setup, bot identities, and traceability all matter. A tool that “works” is still not ready if you cannot clearly track what changed, why it changed, and who or what triggered it.
4. Sometimes the fastest path forward is rebuilding on a better foundation
One of the strongest insights in this conversation is that iteration is not always about adding more. Sometimes the right move is to start again with what you now know. Because these tools are improving so quickly, rebuilding a cleaner version with a stronger foundation can be more effective than endlessly patching an early prototype that was never built to scale.
KEY TAKEAWAYS:
AI-built prototypes can validate ideas quickly, but production readiness takes much more work.
Smaller, simpler launches are often more realistic than feature-heavy first releases.
Security, audit trails, and ownership matter as much as the app itself.
Rebuilding from a stronger foundation can be smarter than over-patching a weak one.
Go-live is often the longest and most demanding phase of the whole build.
