How long does it take to implement AI in a business?
A focused first AI deployment usually takes between two and eight weeks from scoping to production, depending on the systems involved, data access, and how clearly the workflow is already defined.
Two to four weeks for a narrow, well-defined workflow. Six to twelve weeks if access, data, or systems integration is more complex.
Week one: scoping and access
The first week is almost always about scoping. Which exact workflow is being addressed. Who owns it. What systems are involved. What approval points exist. What the data looks like. This sounds boring but it is what separates projects that ship from projects that drift.
Access is the other early bottleneck. Getting credentials to the systems involved, getting sample data, and getting permission to read internal material can take days or weeks depending on the company. Starting that early avoids losing time later.
Weeks two to four: building the working version
This is where the actual system gets built. The model is selected, the prompts and logic are designed, the data flow is wired up, and a working version handles the workflow end to end. By the end of this stretch, the team should be able to run real examples through it and see the output.
The mistake to avoid here is polishing too early. The first version should be functional, not perfect. Quality work happens after the team sees the system in action and identifies the cases that need adjustment.
Weeks four to eight: review and rollout
Once the system runs, the focus shifts to review. Is the output reliable enough for the workflow? Where does it need a human approval step? Where is it strong enough to act on its own? These decisions are operational, not technical, and they decide how the system actually gets used.
Rollout itself is usually fast once the team trusts the output. The slow part is building that trust. Two weeks of careful supervised use is worth more than two months of theoretical testing.