AI Agency: How to Choose the Right One

The market for AI agencies has grown very quickly since 2023: between retrained consultants, digital agencies that added “AI” to their catalog, and genuine specialists, it has become difficult to know whom to trust with a project that commits your organization. A bad choice is expensive: budget spent, demotivated teams, a tool delivered but never used. To see clearly, you need to know what an AI agency really does, which criteria separate a methodical partner from a mere sales pitch, which questions to ask before signing and which warning signs should make you step back. Here is a concrete selection grid, illustrated by real projects carried out by Qolaig.

What an AI agency really does

An AI agency supports a company in the concrete adoption of artificial intelligence: identifying relevant use cases, estimating their potential, then developing and deploying the selected solutions. It does not just sell a tool. It starts from a business problem, checks that it can be solved, builds a suitable solution and supports the teams in adopting it.

The projects carried out by Qolaig, from Adelya (sales copilot) to Amorino (stock management) and Jeausserand Audouard (CRM governance), all share the same structure: a diagnosis of the real problem before any tool is proposed. This is what distinguishes an agency from a simple license reseller.

The criteria that make the difference

A method before a tool. A serious agency starts by understanding your real problem before proposing a technical solution. At Pan Distribution as at URW, the project began with a precise identification of the stakes, not with the presentation of a technology.

Verifiable references. Precise client cases with quantified results (3 hours saved per week at Adelya, €12,000 of waste avoided at Amorino, 20,000 calls per month handled at Logista) are worth more than a general speech about transformation through AI. Do not hesitate to ask what was measured, and how.

An ability to estimate the return on investment before committing. A good agency must be able to estimate an order of magnitude of the gain before development. Ask how this estimate was built, which assumptions it relies on, and what would make it change.

Change management, not just a technical delivery. A tool that works technically but that nobody uses brings in nothing. At Dana Avocats, the solution deliberately aimed for zero overhaul of work habits, a key factor in its immediate adoption, as described in our article on the enterprise chatbot.

Transparency about limits and governance. At Jeausserand Audouard, every write action of the agent remains subject to validation, a sign that the agency built security issues in from the design stage, not as a constraint added after the fact. Our article on agentic AI governance details this approach.

The questions to ask before committing

How does the agency measure a project's success, and with which precise indicators? What is the realistic time between signing and going into production (at Jeausserand Audouard, deployment took place by day 5)? What happens after delivery, and who maintains the solution? Where is your data hosted, and under what confidentiality conditions? What share of the project relies on third-party tools you will depend on afterwards?

Also ask to meet or contact a previous client. A conversation of a few minutes often says more than a sales presentation. Finally, ask about the exit conditions: ownership of the code, the data and the documentation, and the possibility of taking the project over with another team. A confident partner makes these points explicit from the proposal stage.

The warning signs to spot

A pitch focused only on technology, without ever addressing your concrete business problem.

No estimate before launch, which makes any objective evaluation of the result obtained afterwards impossible.

A vague commitment on maintenance after delivery, leaving the company alone with a tool it does not know how to evolve.

A promise of results with no test period or adjustment. At Adelya, the first deployment on Teams was not enough: it was the adjustment toward a dedicated interface that unlocked adoption, an iteration that a rigorous agency must plan for, not avoid. Our article on the AI copilot in the enterprise tells this story.

A scope that keeps changing. A project whose objective is redefined at every meeting often reveals a lack of initial scoping.

References that cannot be contacted. An agency that cannot present any client willing to testify should lead you to ask additional questions.

How to compare two proposals

To compare two agencies, put their proposals side by side on the same lines: the reformulated problem, the scope of the first phase, the deliverables, the schedule, the price per item, maintenance, data governance and team support. An offer that describes precisely what you will receive and what it does not cover is more reliable than a brilliant but vague offer. The lowest price is not always the best: it must be compared with the scope actually covered.

The role of your team during the project

An agency cannot succeed alone. You need a sponsor who makes the decisions, a business referent available a few hours a week to validate choices, and quick access to the data and tools concerned. Projects that fail often lack an identified internal contact. Plan this time from signing, and ask the agency to tell you what it expects from you.

Why ask for a pilot before committing

A short pilot (a few weeks, a single use case, defined indicators) makes it possible to judge the quality of the work, the communication and the method before a broader commitment. If the pilot reaches its objectives, the next steps are decided on facts. If it does not, the cost of the mistake remains limited. An agency confident in its method readily accepts this format.

How to structure your choice

Start by clarifying your need before contacting agencies: which precise problem do you want to solve, with what indicative budget, over what timeframe. Look first at the use cases that come up most often in your sector, such as automating repetitive workflows, which our article on workflow automation covers in detail. For a first simple, measurable project, a well-scoped chatbot is a good example of what a clear perimeter looks like.

Then compare two or three agencies on the same criteria: method, references, estimate, support, governance. A simple table is enough to bring out the differences.

According to the KPMG AI Pulse 2026 study, clarifying responsibilities and managing risks are among the priorities of French companies scaling up their AI projects, exactly the points to check before choosing a provider.

FAQ

Should you favor a specialized AI agency or a generalist IT services firm?
A specialized AI agency generally has finer expertise on use cases and on the real limits of the technology, which reduces the risk of poorly calibrated projects. Its ability to support the move from a first pilot to wider deployment is also a decisive criterion.

How long does a first project with an AI agency take?
It varies greatly with complexity: from a few days for a simple connector (5 days at Jeausserand Audouard) to several weeks for a more structural project such as a custom platform. Our article on RAG in the enterprise shows the steps of a data-heavy project.

At Qolaig, our Workshop → Build → Run methodology frames this type of project before any technical investment. Let's talk about your project.