AI implementation in business: how to automate processes and cut costs
AI implementation cuts costs when it starts with a process, not a tool. First you calculate how much time repetitive tasks take, then you choose the one with the biggest return — most often handling enquiries, documents, recruitment or tender analysis — and automate it in stages, on company data and in the company’s systems.
Why do many AI implementations fail to cut costs?
The most common mistake is starting with the tool: “let’s buy ChatGPT for everyone” or “let’s build a chatbot, because everyone has one”. A project like that ends with a nice demo that nobody uses, because it isn’t connected to the team’s real work.
AI only cuts costs when it takes over specific, measurable work — phone calls, documents, data re-entry, reading CVs or tender notices — and does it in the systems the company already uses.
How to choose the first process to automate?
The best candidate meets three conditions:
- It’s repetitive — the same questions, the same documents, the same steps.
- It takes a lot of time — measured in hours per week, not minutes.
- It has a clear outcome — you know how to measure success (response time, number of cases handled, less manual work).
In practice, these are most often: customer service and bookings, patient registration, recruitment and onboarding, document workflow and tender analysis.
What does implementation look like step by step?
- Analysis and workshops — we map out how the process works today and how it should work a year from now.
- Wireframes and prototype — we show what working with AI will look like before any code is written.
- Model selection and architecture — an off-the-shelf model, a private model or a narrow model trained on company data.
- Building in stages — the features with the biggest savings first.
- Deployment and training — integration with systems and preparing the team.
- Development — we measure results and automate further steps.
We describe the details on the How we work page.
Examples from our deployments
- Employment agency from Paris (R2T) — a dedicated system brought its branches together in one environment and reduced manual work by more than 30%.
- AI chatbot for recruitment — the candidate goes from the first WhatsApp message, through CV and document analysis, to a signed contract, without involving the HR team.
- Dedicated AI model for tenders in a pharmaceutical company — the system downloads notices from an aggregator, scores their fit based on the company’s history, and fills in the bid and documents. The same work is now done by 3 people instead of more than 10.
How to protect company data when implementing AI?
If your processes involve data about customers, patients or candidates, consider a private AI model — running on the company’s servers or in an isolated cloud in the EU. The data then doesn’t go to public models or train other people’s systems, and costs are predictable. More: Private AI models.