---
title: "Blog: AI transformation, agents, .NET integration | GiLabs"
description: "Field notes and method on AI transformation for SMEs and mid-caps: AI agents, .NET integration, process automation, real-world cases."
url: "https://gilabs.fr/en/blog/"
lang: "en"
---
# AI transformation for SMEs and mid-caps: notes from the field

Field notes and method on AI transformation for SMEs and mid-caps: AI agents, .NET integration, process automation, real-world cases.

- [The PHP flaw had been known for two years. AI found the key in one summer.](https://gilabs.fr/en/blog/faille-php-ia/) (2026-10-26): Two critical PHP flaws, French government sites taken offline. The underlying defect had been known since 2024, and AI sped up its exploitation. The real risk: not knowing what runs on your servers.
- [An AI agent with its own email address. Where is its job description?](https://gilabs.fr/en/blog/agent-ia-fiche-de-poste/) (2026-10-16): Google has announced an agent with its own account, email address and action log. Before handing it the keys, write three lists: alone, proposes, never.
- [AI is not cutting jobs. It is closing the entry door.](https://gilabs.fr/en/blog/ia-porte-des-juniors/) (2026-10-14): Among 21-29 year-olds in France, permanent hires are down 28% in the occupations most exposed to AI. Total employment has not moved. What it means for an SME.
- [My AI made up a price. My rule fits on one line.](https://gilabs.fr/en/blog/ia-prix-invente/) (2026-10-12): An AI read a free credit as a price, with no warning at all. What that near miss says about the framework a small business needs to use AI.
- [40% of French small businesses use AI. Where do you actually start?](https://gilabs.fr/en/blog/ia-par-quel-bout/) (2026-10-09): Four in ten French small businesses use AI, mostly to write. One in ten automates a task. What the France Num 2026 survey says about the right starting point.
- [Invoices no longer need to use AI. Your other documents still do.](https://gilabs.fr/en/blog/facture-electronique-extraction/) (2026-10-08): Since September, French invoices arrive as data: nothing left for an AI to read. Document extraction is not going away, it is changing targets.
- [What keeps a document pipeline alive in production](https://gilabs.fr/en/blog/ce-qui-tient-en-production/) (2026-09-17): Three engineering decisions separate an AI proof of concept from a system that runs every day: human review, a correction loop, and interchangeable extraction engines.
- [We rolled out Copilot, and nothing changed in the accounts](https://gilabs.fr/en/blog/copilot-deploye-rien-na-change/) (2026-09-15): Copilot speeds up the person. What a process costs is elapsed time, not hands-on time. Here is how to measure both, and why it decides your AI budget.
- [A 30-day AI diagnostic, one day of your time](https://gilabs.fr/en/blog/ce-que-le-diagnostic-demande-aux-equipes/) (2026-09-08): What blocks an AI diagnostic is almost never the price, it is the load on the team. Here is the exact figure: three fixed milestones, a few interviews, one decision.
- [The AI POC that will never reach production as-is](https://gilabs.fr/en/blog/industrialiser-poc-python-dotnet/) (2026-08-12): A Python POC that convinces in a demo but stays stuck for six months does not need to be rewritten in C#. It needs to be exposed behind an API and brought into the rest of the estate's rules.
- [Who said what, without voice recognition: diarization by metadata](https://gilabs.fr/en/blog/diarisation-sans-reconnaissance-vocale/) (2026-08-10): Attributing every sentence in a meeting summary to the right person without creating biometric data. Three approaches compared, only one creates no voice fingerprint.
- [The pivot that saved a voice AI project: think field-first before you code](https://gilabs.fr/en/blog/pivot-capture-teams/) (2026-07-31): A project assumed a technician would call an automated assistant to dictate a job estimate. Four very concrete obstacles showed the idea didn't hold up in the field.
- [Employee voice recordings should never leave Europe: a rule set before the technology](https://gilabs.fr/en/blog/whisper-onprem-souverainete/) (2026-07-31): To produce a reliable meeting summary, a leadership team first settled a matter of principle: where do voice recordings actually go? Three options compared, one chosen.
- [The 30-Day AI Diagnostic: why I packaged my offer into five fixed deliverables](https://gilabs.fr/en/blog/diagnostic-ia-30-jours-packaging/) (2026-07-27): A fixed-scope package with five written deliverables, not an audit that stops at a report: why a packaged AI consulting offer gets decided faster than an open-ended conversation.
- [Hiring a .NET developer and a data scientist at the same time: one need, not two](https://gilabs.fr/en/blog/pont-dotnet-ia-recrutement/) (2026-07-22): A company that hires a .NET developer and a data scientist at the same time is sending a clear signal: it does not yet know it is looking for a single person.
- [AI proposes, deterministic logic disposes: the pattern that keeps AI production-ready](https://gilabs.fr/en/blog/ia-propose-deterministe-dispose/) (2026-07-15): AI in production is not AI that decides on its own. It is AI that proposes, to a system that has the right to say no. The three-layer architecture pattern behind it.
- [Your problem is not (yet) an AI problem](https://gilabs.fr/en/blog/socle-data-avant-ia/) (2026-07-10): "We want AI on our technical documentation. Where is your documentation? In binders." The data foundation comes before AI, and that is good news.
- [Concierge first, code second: why I spent weeks automating nothing](https://gilabs.fr/en/blog/concierge-dabord-code-ensuite/) (2026-07-08): Before writing the first line of my prospecting agent, I did the work by hand, for a long time. That step, not the code, is what actually makes the difference.
- ["It's expensive": a category problem, not a pricing problem](https://gilabs.fr/en/blog/outil-vs-transformation-categorie/) (2026-07-07): An AI tool is an expense. An AI transformation is an investment. Confusing the two lenses explains most of the "it's too expensive" reactions heard at SMEs.
- [The 2025 METR study: developers thought they were 20% faster, they were 19% slower](https://gilabs.fr/en/blog/metr-productivite-ia/) (2026-07-02): A controlled trial with experienced developers reveals a striking gap between perception and measurement. AI's gain is neither automatic nor free: it has to be measured.
- [Garry Tan's AI software factory: the lesson is not in the tools](https://gilabs.fr/en/blog/usine-logicielle-ia/) (2026-07-01): Y Combinator's president published his method to "ship like a team of twenty." The real lesson is not in the 30 tools he shares, but in the process they run on top of.
- [I built an AI agent for my own prospecting: here is where it gets it wrong](https://gilabs.fr/en/blog/agent-n8n-prospection/) (2026-06-19): An n8n agent sourced, enriched and scored my prospects every morning. What it taught me best was one concrete case where a confident AI can be completely wrong.
- [Zero human validation, and that is the right call: the Convention Online AI chain](https://gilabs.fr/en/blog/convention-online-ia-autonome/) (2026-06-17): One pipeline requires human validation at every step. Another one runs all night with nobody watching. How to decide where to set the dial, based on the real business stakes.
- [From scanned timesheets to the business database: the AI pipeline, step by step](https://gilabs.fr/en/blog/pipeline-idp-production/) (2026-06-11): Hundreds of scanned timesheets, keyed in by hand, with no systematic check. Here is the AI pipeline I designed for an industrial group, from the scan landing to the database write.
- [Why I chose Semantic Kernel over Python for AI in .NET](https://gilabs.fr/en/blog/certification-az2005-semantic-kernel/) (2026-06-08): Holder of the Microsoft Applied Skills AZ-2005 credential, I chose to integrate AI into existing .NET systems rather than rewrite everything in Python. The reasoning, and the Semantic Kernel pattern.