The journey to a first production AI release typically spans three months. This timeline is not due to working faster but rather strategically overlapping tasks. This post outlines our structured approach to achieve a successful AI product launch within this timeframe.
Month 1 — Discovery, and governance the same week. 📅
Most engagements start with discovery and bolt governance on at the end, as a compliance scramble. We start both in week one. Discovery is not a workshop — it is a pod sitting next to the people doing the work, observing where time actually goes and which decisions are challenging. Governance starts the same week because the only cheap time to decide who signs off on a model's decision is before there is a decision to sign off on. By the end of month one, we have one workflow chosen, and a one-page memo naming the human who owns its output. Neither of those is software. Both are prerequisites for software that ships. Similar approaches are used by companies like IBM Watson and DataRobot.
Month 2 — The eval harness goes up before the model goes in. 🔧
This inversion buys the schedule. We write the evals — the frozen behavioral set, the regression gate, the first red-team probes — before the first line of model integration. The harness is the contract: it is the agreed definition of "good," fixed while everyone is calm, so nobody renegotiates it at week eleven under deadline pressure. Integration then has a target to hit instead of a feeling to chase. Most of month two is unglamorous plumbing — data access, the retrieval path, the audit log — done against a bar that was set before anyone was tempted to lower it. Competitors like H2O.ai also emphasize robust evaluation frameworks.
| Month | Key Activities |
|---|---|
| 1 | Discovery & Governance |
| 2 | Eval Harness & Integration |
Month 3 — Shadow first, then a trickle, then traffic. 🚦
The model reaches production weeks before it touches a real decision. It runs in shadow — fed the same live inputs as the real process, its outputs logged and compared, trusted with nothing. We watch where it disagrees with the humans and why. Only when the shadow numbers hold do we let it take real decisions, and even then with a person holding the override. The "first release" milestone on the diagram is not the deploy. It is the first time the system makes a real call that matters, with the rollback target named and the on-call watching.
Why it overlaps — the schedule is the sequencing. 🔄
Three months is not fast work. It is overlapped work, and the order is the whole trick.
- Governance overlaps discovery so the rules exist before the build can outrun them.
- The eval harness overlaps the build so the bar is set before the code is allowed to move it.
- The shadow run overlaps nothing. It is the one stage we never compress because it is the only stage where the cost of rushing lands on a customer instead of on us.
Further Reading
- From Proof Of Concept To Production: Embracing Systems Thinking
- Rethinking Mobile Dev Timelines With AI Engineering
- Bridging The Gap: How Cross-Functional Teams Drive Success In Data Science Projects
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