---
title: "How Long Does It Take to Move an AI Pilot to Production? Timelines by Scope"
description: "One well-scoped AI workflow typically goes from kickoff to production in 6–10 weeks: 1 week discovery, 2 weeks prototype, 3–6 weeks build. Timelines by scope, and what makes them slip."
canonical: https://sudosapient.si/pilot-to-production-timelines
updated: 2026-10-27
publisher: Sudo Sapient (https://sudosapient.com/)
---
# How long does it take to move an AI pilot to production?

**One well-scoped AI workflow typically takes 6–10 weeks from kickoff to production with a focused senior team: about 1 week of discovery, 2 weeks of prototyping on real data and 3–6 weeks of build, followed by handoff and a month of support. Multi-module AI products take longer; our most complex build so far had a 12-week window.**

These are Sudo Sapient's delivery commitments, stated so they can be checked. They are not industry averages.

## Timelines by scope

| Scope | Typical time to production | What "done" means |
|---|---|---|
| AI media pipeline (one content type, one approval step) | 4–8 weeks | Repeatable pipeline producing publishable output with one human sign-off |
| AI automation of one operational workflow | 6–8 weeks | Real inputs flow in automatically; uncertain cases go to a human queue; dashboard live |
| One AI product slice (agent, copilot or AI-native feature) | 6–10 weeks | Real users on real inputs, evals passing, monitoring and runbook in place |
| Multi-module AI product | 10–12+ weeks | Example: Jgsaw, 6 intelligence + 5 execution modules, 12-week window |
| Company-wide AI transformation | 12–24 months | Many workflows, each shipped through the cycle above |

## Week-by-week for a single workflow

| Week | Stage | Work | Exit criterion |
|---|---|---|---|
| 1 | Discover | Workflow map, exceptions, data access, one metric, baseline | Signed-off scope and metric |
| 2–3 | Prototype | Working slice on real data; first eval set of 50–200 cases | Evals show the approach works on real inputs |
| 4–9 | Build | Integrations, guardrails, review UI, logging, dashboards; iterate on evals | Production-readiness checklist passes |
| 10 | Ship | Shadow mode, go-live, handoff, training | Owner runs it; first-month support starts |

Small scopes compress the build to three weeks, which is how a 6-week delivery happens.

## What makes AI timelines slip

1. **Data access arrives late.** If exports or API credentials take three weeks, the project takes three weeks longer. Ask for them before kickoff.
2. **No single owner.** Decisions wait for a committee. Name one owner before you start.
3. **The pilot was built on toy data.** Moving to real data reveals the edge cases late. Prototype on a random sample of real inputs instead.
4. **No eval set.** Every change becomes an argument about whether it "looks better". Evals turn it into a number.
5. **Scope grows mid-build.** "While we're at it" is the most expensive phrase in AI delivery. Park new ideas for the next cycle.
6. **Integration is underestimated.** Connecting to the helpdesk, CRM or ERP is often the largest share of the build.

## Why "pilot purgatory" is so common

Most stalled pilots fail on engineering and ownership, not on model capability: no eval suite, no guardrails, no integration, no observability, no owner, no handoff. Each gap is described, with the fix, in [From AI pilot to production](https://sudosapient.si/entries/ai-pilot-to-production).

## How to check whether a vendor's timeline is real

Ask for three things: what is delivered at the end of week 3 (it should run on your real data), what "in production" means in writing, and who owns the code, prompts and evals afterwards. A vendor who cannot answer those cannot commit to a date either.

## Sources for the numbers on this page

- Stage durations and service durations: Sudo Sapient's published process and services on [sudosapient.com](https://sudosapient.com/).
- Jgsaw 12-week window: the published [Jgsaw case study](https://sudosapient.com/work/jgsaw/).
- Company-wide range: our working estimate, based on running one to two workflows per quarter.