AI Implementation for Business: Scope, Timeline and Price

A company ends up with a working process rather than access to a neural network: one task taken apart, an agent built for it, tested on real data, and an instruction an ordinary member of staff can follow.

What does end-to-end AI implementation mean?

End-to-end means no technical work is left on your side. One specific process is examined, an agent is written for it, data sources are connected, checks are configured, and the result is written back into wherever your company already works — email, a spreadsheet, your accounting system, a messenger.

The boundary runs through the data. Access, real documents and the rules of your business come from you; everything else — the build, the testing, the corrections — is on the supplier. If a project ends with a presentation and a login, it was not end-to-end.

Which task should you begin with?

The first process taken should repeat every day, be describable by rules, and cost a living person real time. Three typical entry points:

• handling incoming enquiries — sorting, enriching, creating records;
• reading documents and moving their contents into your systems;
• answering the routine questions customers ask before they buy.

What unites them is that the result is verifiable. You already have a benchmark — the way a person does it — and the agent is measured against it rather than admired.

How long does implementing an AI agent take?

One process: thirty calendar days from start to acceptance. Inside that, a week for examining the process and obtaining access, two weeks for the build and testing on the customer's real data, and a final week for corrections raised in review and training the people who will use it.

Anything promising a result in three days is selling a demonstration. Anything quoting six months for one process is either solving five problems at once or has not defined the problem yet.

How much does AI implementation cost?

Guide figures for the Moscow market, quoted in roubles, for small and mid-sized companies:

• Audit of one process — from ₽30,000, three working days, with the return calculated before anything is built.
• One process end to end — ₽150,000 to ₽400,000, depending on data sources and required accuracy.
• Several linked processes — from ₽600,000.
• Support — from ₽20,000 per month.

What actually moves the figure is not the technology but the precision demanded. Ninety per cent accuracy on a routine sorting task is inexpensive. Ninety-nine per cent on an unusual one, where every remaining error costs money, is a different project entirely.

What must remain with your company afterwards?

A checklist worth holding at acceptance:

• a working agent running on your own data;
• the process written out in plain words;
• a one-page instruction for the person who works with it;
• accounts and keys registered to your company, not the contractor;
• a test report with the number of cases checked and the match rate;
• a warranty on corrections.

If what remains is only a presentation, nothing was implemented.

How do you know AI is not needed yet?

Three honest signs. The process happens less than once a week, so the saving never covers the setup. The rules change constantly and are written down nowhere, so the agent will err exactly where people do. There is no data from previous periods, so there is nothing to test against. In each case it is cheaper to put the process in order first and add AI second.

Where do you start once the decision is made?

With one process and a measurable result: how long it takes now and how long it should take afterwards. A three-day audit produces that figure before significant money is committed, and shows which task will repay the investment soonest.