How much does AI implementation cost?

The two parts of the cost

1. The build (one-time). Designing, building, connecting and testing the system. The price depends on how many workflows are involved, how many of your systems it connects to, and how much human review it needs.

2. Running it (monthly). Hosting, the AI usage itself, monitoring, support for your team and ongoing improvements. AI usage costs have fallen sharply, so for most business workflows this is a small fraction of what the same work costs when people do it by hand.

What makes a build cost more or less

• Number of workflows. One focused workflow, such as answering and qualifying leads, costs less than an end-to-end operations system.
• Integrations. Connecting to modern software with good access is quick. Older systems take more work.
• Accuracy requirements. Work where a mistake is costly needs more testing and approval steps.
• Volume and languages. High volumes and several languages need more tuning.
• Data and hosting rules. Hosting in a specific country or under specific regulations adds setup.

How to judge whether it pays back

Add up what the work costs today: the hours people spend on it multiplied by what those hours cost, plus revenue lost to slow replies, missed calls or errors. Compare that monthly number with the running cost, then see how many months it takes to recover the build. Most well-chosen first builds pay back within months, not years. That's why we start with the opportunity that pays back fastest.

Watch out for

• Open-ended hourly billing with no fixed scope.
• Platforms that lock you in, where leaving means losing the system.
• No plan for after launch. Unsupported AI systems drift and break.

Get a real number for your business

Our free AI audit ends with a written AI Opportunity Map that includes a fixed price and timeline for your first build, and the hours and money each opportunity is worth.