Generative AI for business: useful use cases, in production and well governed

Generative AI entered companies through individual use: an employee drafts an email, summarises a document, rewords a proposal. The question is no longer whether AI will be used, but how to move it from scattered personal use to reliable tools built into business processes, with known cost, quality and risk. This is what we call AI in production.

Based in Villeneuve-d’Ascq, in the Lille metropolitan area (northern France), we support SMEs and mid-sized companies through this transition with the same engineering standards we apply to cloud infrastructure: security by design, shared decisions and self-sufficient teams at the end.

What generative AI really changes for a business

A large language model can read, summarise, classify, extract and write text at scale. On its own, it does not know what is in your document base, which rules apply to your customers, or who is allowed to see what. The value therefore rarely comes from the model itself, but from how it is connected to your data, your tools and your processes, with safeguards.

Realistic use cases fall into a few families:

• Finding information: an assistant that answers from your procedures, contracts, product sheets or technical documentation, and cites its sources.
• Processing document flows: extracting fields from an email, invoice or form, classifying requests, preparing a summary for a human who makes the decision.
• Drafting and rewording: first drafts of replies, meeting minutes and descriptions, with human review.
• Automating multi-step tasks: agents that chain search, verification and action in your tools, within a strict permission scope.

Conversely, some uses regularly disappoint: asking the model for exact calculations without a tool, letting it take binding decisions without validation, or feeding it data that nobody has checked.

AI for SMEs: start small, but start right

An SME has neither the time nor the budget for a multi-year AI programme. It does, however, have an advantage: shorter processes and decision-makers close to the ground. Our recommendation is simple: choose a first use case with a real pain point, that is measurable and backed by available data, and take it all the way to real use rather than piling up demos.

In practice, AI support for an SME often starts with an opportunity audit: a few workshops with business teams, an inventory of existing use (including consumer tools used without any framework), an assessment of data quality and a prioritisation. The result is a short roadmap, with a first use case costed in build time and running cost.

For the step-by-step method, from identifying use cases to deployment, see our guide on how to adopt AI in your business.

What makes a generative AI project succeed

Usable data

An assistant is never better than the documents it reads. Outdated versions, duplicates, unstructured files, poorly maintained access rights: these problems existed before AI, and AI makes them visible. Part of the work is identifying the reference sources, their owners and how often they are updated.

A clearly defined process

AI fits into a process: who triggers it, who validates, what happens in case of error or doubt? Without answers to these questions, the tool remains a gadget. With them, it becomes a measurable step in an existing workflow.

Proportionate governance

Usage policy, register of use cases, risk classification, GDPR compliance, obligations under the EU AI Act: governance does not need to be heavy, but it must be in place before wider roll-out, not after an incident.

Controlled costs

Generative AI models are billed per use, according to the volume of text processed. The cost of a use case depends on the chosen model, the size of the context sent and the number of calls. We track these costs per use case, following the same logic as FinOps applied to the cloud, and we promise no flat-rate savings: gains are measured on your data.

Our areas of AI expertise

AI agents for business

An AI agent does more than answer: it chains steps and acts in your tools (CRM, email, ticketing, ERP). This is powerful, which is precisely why it must be designed with limited permissions, human validation for sensitive actions and complete logging. Discover our approach to AI agents for business, from selecting tasks that can be automated to integrating them with your systems.

AI assistant on your internal documents

This is often the first high-value use case: letting teams query procedures, contracts or documentation in natural language, with sourced answers and respect for each person’s access rights. We design these AI assistants on your documents (RAG) from source ingestion through to evaluating answer quality.

Production deployment and LLMOps

A prototype that works in a demo is not a production service. Versioning of prompts and models, evaluation datasets, observability, cost tracking, incident management, model hosting choices: this is the core of our AI production deployment (LLMOps) offering, built on our cloud operations practice.

AI governance and the EU AI Act

AI usage policy, register of use cases, risk classification, alignment with the GDPR and preparation for the obligations of the EU regulation: we help you structure AI governance compliant with the EU AI Act, proportionate to your size and your use cases.

Our method: from idea to production use

We apply to AI the same four-step method we use for our cloud projects.

Step	Objective	Deliverables
Scope	Choose the right use cases and the ground rules	Inventory and prioritisation of use cases, success criteria, initial risk assessment
Build	Deliver a first use case on real data	Evaluated prototype, target architecture, running cost estimate
Industrialise	Move to production safely	Deployment as Infrastructure as Code, automated evaluations, monitoring, documentation
Improve	Measure and make progress	Quality and cost dashboards, incident reviews, team upskilling

Each step ends with a shared decision: continue, adjust or stop. A use case that does not meet its quality or cost criteria should not go into production, and saying so early is part of the job.

Cloud and AI: why they go together

A generative AI use case in production relies on infrastructure: access to models through managed services such as Amazon Bedrock or Azure OpenAI, document storage, search indexes, networking, identity management and logging. Choices of region, encryption and data isolation directly determine confidentiality. That is why our AI expertise is built on our AWS and Azure cloud expertise, with neutrality between the two providers.

When to call on us

• Your teams already use AI tools without a framework and you want to regain control.
• A POC was convincing in a demo but is not making it into production.
• You are looking for a first concrete use case for your SME, without committing to a long programme.
• You need to document your use of AI with regard to the GDPR and the EU AI Act.

In every case, we favour deliverables your teams can take over, documented and handed over. If you would like to discuss your project, you can book a 15-minute call.

[ YOUR QUESTIONS ]

Frequently asked questions

Where should an SME start with AI?

Start from a specific, measurable business pain point rather than a tool: a repetitive task, a slow search for information, the sorting of incoming requests. Then check that the data you need exists and is accessible. A short scoping phase is enough to choose a realistic first use case and set the rules of use.

Can we use ChatGPT at work without putting confidentiality at risk?

The risk depends on the plan you use, its contractual terms and what employees type into it. Banning it without offering an alternative often pushes people towards uncontrolled use. We recommend a clear usage policy, business-grade plans, or models hosted on your own cloud for sensitive data.

What is the difference between an AI audit and a proof of concept?

An opportunity audit identifies and prioritises use cases according to their value, feasibility and risks. A proof of concept (POC) tests one specific use case on real data to check that it works. Both are useful, provided the POC is designed from the outset with clear criteria for going into production.

Does a company need an AI usage policy?

Yes, as soon as employees use generative AI tools, even informally. A policy sets out the approved tools, the data that must never be entered, the human review expected and who is responsible for what. It goes hand in hand with a register of use cases and awareness training.

How much does a generative AI project cost?

The cost depends on the scope, the quality of the data, how deeply the solution is integrated with existing tools and the volume of use, because models are billed per use. We do not quote a generic figure: during scoping we estimate the build cost and the running cost of each use case from your actual volumes.