How long does it take to put enterprise AI into production?

With a scoped use case, available data, and senior practitioners, eight weeks from kickoff to production is realistic: roughly one week to diagnose, three to build and integrate, two to deploy and harden, with monitoring and hand-off following. Most delay comes from data access and unclear ownership, not from the technology.

Why do enterprise AI projects usually take so long?

The time is lost before the build starts — waiting on data access, security reviews, and decisions about who owns the outcome. Long discovery phases and large junior teams add coordination overhead without moving the work closer to production.

What has to be true to ship enterprise AI in eight weeks?

A single, well-chosen use case with a measurable target; access to representative data in the first week; senior practitioners doing the work; a named business owner who can make decisions; and a deployment path agreed up front with security and platform teams.

How fast can you actually ship enterprise AI?

Eight weeks from kickoff to production is realistic. The constraint is almost never the technology — it's everything around it.

Ask a large consultancy how long it takes to get enterprise AI into production and you'll hear a number measured in quarters. Ask an engineer who has actually done it and you'll hear weeks. Both can be telling the truth — the difference is in what they count as "the work."

Our median time from kickoff to AI running in production is eight weeks. That is not a marketing number; it's a schedule we hold ourselves to. Here is what it looks like, and — more usefully — where the time actually goes when projects miss it.

Week 0–1: Diagnose

The first job is to narrow, not expand. Most enterprise AI programs fail because they try to boil the ocean: a platform, a strategy, and five use cases at once. We pick one use case with a measurable target and the fastest path to payback, and we get hands on representative data inside the first week. If data access takes longer than a week, that is the project's real bottleneck — and it's better to surface it on day three than month three.

Week 2–4: Build

With data in hand, senior practitioners build and integrate the system in tight weekly loops. There is no separate "research team" handing findings to a "delivery team" — the people who design the solution also ship it. Each week ends with something runnable, reviewed alongside the client's own engineers so there is no knowledge to transfer later because it was never held in one place.

Week 5–8: Deploy and harden

Deployment is treated as part of the build, not a phase that starts after it. The AI goes into production behind the client's firewall, instrumented with monitoring and evaluation from the first day it serves a result. By the end of week eight it is measured against the ROI target agreed at kickoff — a number, not a vibe.

So why do most projects take six months?

Because the time is lost before the build ever starts. In our experience the delay almost always comes from a short list of non-technical problems:

Data access. Approvals, environments and credentials that take weeks to arrange while the team waits.
Unclear ownership. No single business owner who can make a decision, so every choice escalates.
Discovery theatre. Months of workshops and slideware that produce a plan but no working software.
Staffing pyramids. Large teams of junior analysts generate coordination overhead, not velocity.

None of those are technical problems. They're operating-model problems — which is precisely why adding more data scientists rarely fixes a slow project.

What has to be true to hit eight weeks?

The schedule is achievable, but it is not unconditional. Five things have to hold:

A single, well-chosen use case with a measurable target.
Access to representative data within the first week.
Senior practitioners doing the actual work.
A named business owner empowered to decide.
A deployment path agreed up front with security and platform teams.

Get those right and eight weeks is comfortable. Get them wrong and no timeline survives — which is the more important lesson. The technology was never the hard part.