How long does it take to build an AI integration?

Understand

Learn how the work is done today, what information it uses, which systems are involved and what should stay with a person.

Design

Decide what the system will do, what it may do on its own, what needs approval, how data will be handled and how success will be judged.

Build

Connect the systems, write the instructions and rules the AI follows, and set up logging and review steps.

Test with real examples

Run the system on actual past enquiries, documents or records, compare its output with what a person would have done, and fix what it gets wrong.

Launch

Switch it on for real work, often with a person reviewing everything at first.

Adjust

Watch how it performs, tighten instructions, handle cases nobody anticipated and gradually reduce review where it has proven reliable.

Each phase can be short or long depending on the project. Skipping the early ones rarely saves time, because unclear decisions resurface during the build.

Usually faster

• –One clearly defined workflow rather than several at once.
• –Clean, consistent data in a system the integration can reach.
• –Tools that offer APIs or reliable integrations.
• –A decision-maker who is available to answer questions and approve the design.

Usually slower

• –Many systems that all need to be connected.
• –Messy or scattered data that has to be organized first.
• –Internal approvals, security reviews or legal checks that happen partway through.
• –Unclear ownership, where nobody can say how the process should work.

A pilot is a narrow first version: one workflow, one team, a limited set of actions and a person reviewing the output. It gets something useful running sooner and shows what the system handles well before anyone commits to a larger build.

For example, a home renovation company might want AI to handle enquiries, quoting, scheduling and follow-up. A sensible pilot handles only incoming enquiries: the system reads each one, extracts the project type, location and budget, adds a summary to the CRM and flags anything unclear for the office manager. Once that works reliably, quoting support can be added using what the pilot revealed about the company's real enquiries. Choosing which tasks to automate first is often the most useful decision in the whole project.

AI systems do not fail the way ordinary software does. They can produce answers that look correct but are wrong, and those errors only show up when the system meets real, varied examples. Testing on past enquiries or documents is how you find them before customers do.

Review time after launch matters for the same reason. Someone needs to check outputs, note patterns in the mistakes and feed that back into the instructions. It is tempting to cut this short to go live sooner, but it is the part that decides whether the system can be trusted.

The same factors apply, with more weight on design and testing. An agent chooses between actions, so the time goes into defining what it may do, what it must hand back to a person and how it behaves when it is unsure. A narrow agent with a small set of allowed actions is a much smaller project than one that works across several systems. The difference between AI tools, automations and AI agents explains when an agent is needed at all.

• –Monitoring: checking logs and outputs regularly, especially in the early period.
• –Adjustments: refining instructions and rules as new kinds of requests appear.
• –Maintenance: fixing connections when a CRM, form or other tool changes.
• –Gradual expansion: reducing review where the system has earned trust, or adding the next workflow.

When comparing proposals, ask each provider to describe the phases, what they need from you at each one and what support looks like after launch. For our own AI integration projects, scope, cost and timeline are agreed in writing before work begins, and a clear written plan is a better guide to how long the work will take than a single number quoted up front.