AI development and integration for a business: what actually works in 2026

Two years of demos have made it hard to see where AI pays off in a normal company. The answer is narrower and more useful than the hype: reading documents that humans used to re-type, answering questions over data that was previously locked in files and systems, and handling repetitive multi-step tasks with a human checking the result. Done as engineering — with evaluation, guardrails and integration into the systems you already run — these deliver measurable savings. Done as a chatbot bolted to a website, they mostly do not.

What an AI integration project includes

• Use-case selection — the tasks where volume, repetition and tolerance for review make AI worthwhile, and an honest estimate of the saving.
• Document intelligence — extracting fields from invoices, orders, contracts and forms into your ERP or DMS, with confidence scores and a review queue.
• Search and assistants over private data — retrieval-augmented generation (RAG) over your documents, tickets, manuals and databases, with sources cited and access rights respected.
• Agents connected to systems — assistants that read from and act in the ERP, CRM or helpdesk through APIs, with permissions and an audit trail.
• Model choice and hosting — commercial APIs, open models on our EU infrastructure, or both, chosen for accuracy, cost and data residency.
• Evaluation and safety — test sets, measured accuracy, prompt and output guardrails, monitoring in production.

What to prepare

• The process and its volumes: how many documents, tickets or requests a month.
• Sample data, anonymised if needed.
• The systems the AI must read from and write to.
• Data residency and confidentiality rules.
• Who reviews outputs, and what accuracy is acceptable.

More on the AI development and integration page.