AI agency or AI consultancy: how to choose for your custom projects

You have a custom AI project in mind: an agent that prepares sales proposals, a tool that sorts customer requests, a platform that scores prospects. Now you need to choose who will build it. AI agency, AI consultancy, freelance consultant: on their websites, they all promise roughly the same thing.

At AI Sisters, an AI consultancy, we train teams and build custom agents with the AI Sisters Lab. We read a lot of specifications, and the projects that succeed tend to look alike. Here are the six criteria we recommend you check, whatever the provider, including us.

In brief

• An AI agency usually starts from a need that has already been described and delivers it. An AI consultancy first helps you decide what to build, then follows the project through to actual use.
• If your need is written down and costed, a development partner may be enough. If you are torn between several ideas, start with a scoping phase.
• Ask for an estimated gain for each use case before any development, then a POC on your own data.
• Have it written into the contract that the code belongs to you.
• Check where your data goes: hosting, a data processing agreement (DPA), and no model training on your conversations.
• Build user training into the project. A tool nobody opens delivers nothing.

Further reading: integrating AI into a company, the 8-step method, how to build an AI agent and measuring the ROI of AI training.

AI agency, AI consultancy, AI consultant: who does what?

The labels overlap, and many providers do a bit of everything. Yet their starting point differs, and that is what shapes the project.

These portraits describe trends. Some agencies have a genuine consulting team, and some consultancies outsource all their development. So it is better to judge each provider on specific criteria than on its label.

To put it simply. For a house, the architect checks that the project is sound and fits the budget, then the building firm builds it. Some firms do both. Trouble starts when the plans are drawn by someone who has never seen the site.

The six criteria for choosing your AI partner

These criteria apply to an agency as much as to a consultancy. For each one, we give you the question to ask in the meeting.

1. Scoping and ROI, before the first line of code

An AI project launched without figures is hard to stop when it does not pay off. Gartner predicts that over 40% of agentic AI projects will be abandoned by the end of 2027, because of spiralling costs or value that is never demonstrated. Scoping is there to sort before you spend.

At JCDecaux France’s IT department, several divisions each had their own AI project idea, and none was costed. In seven weeks, the AI Sisters Lab ran five use cases through the same grid: a co-design workshop with the teams, technical and business analysis, and an impact and feasibility score out of ten. The IT department came away with €2.19M in estimated annual gains across all the cases and two MVPs selected. One case was dropped because its gains did not cover the investment. The full details are in the JCDecaux case study.

The workshops also produced very concrete measurements. Preparing a sales proposal took 2 hours 35 minutes, and the MVP aims for 25 minutes. With this kind of figure, measured with the teams, management can make a decision.

The question to ask: “How do you estimate the gain of each use case, and when would you advise us not to build it?” A partner who can say no saves you money.

2. Change management support

Even a very good agent is useless if teams keep working as before. Your partner must know how to surface needs with the business teams, then get the tool adopted.

ENGIE’s GET programme is a good example. The journey began with a Copilot workshop and an AI Show for the Adoption teams. On 24 March 2026, a morning Agent-a-thon surfaced 16 use cases. Five months later, agents were running in production, including a Learning Coach that became the programme’s official training tool. The ideas came from the teams, and they use the agents. The full journey is described in the ENGIE case study.

The question to ask: “How do you involve future users, and who measures usage after delivery?”

3. Code ownership

For custom development, ask who owns the code at the end. Depending on the contract, you leave with a tool you can develop freely, or with a subscription that is hard to get out of.

At Energies France, an energy broker, the AI Sisters Lab built a platform that scores each prospect out of 100 and prepares two batches of calls a day for 30 salespeople. The code belongs to the client. The platform reads the existing system without ever changing it, and it was delivered in increments over four months. Before, the salespeople spent 80% of their time looking for leads. Today, they spend 80% of their time on calls. See Energies France’s AI sales platform.

The question to ask: “Who owns the code, the prompts and the documentation at the end of the project, and what happens if we end the contract?”

4. Data security

An AI agent reads your documents, your emails or your CRM. Before signing, you need to know where this data goes and what happens to it:

• where the tool is hosted: in your environment, on a European cloud, elsewhere;
• which AI models it uses, and under what contract. A DPA must guarantee that your data does not train the models;
• what access rights the agent gets. Read-only by default, write access only once approved;
• how the project complies with the GDPR and the AI Act.

On Lab projects, these constraints are set from the scoping stage. Tools are built in your environment or on services covered by a DPA, and your data is not used to train the models.

To put it simply. Handing your data to an AI provider is like handing over the keys to your premises. You want to know who has a spare set, which doors they can open and whether anyone photocopies your files on the way out.

5. The ability to train your teams

An agent changes the way people work. Its users need to know how to talk to it, check what it produces and spot its mistakes. If your partner can only build, you will need a second provider for training, and two contacts who pass the buck when adoption gets off to a slow start.

This is where a consultancy that also trains adds the most value. AI Sisters has supported more than 900 companies and trained 60,000 employees. Our training courses are rated 9.5/10, and our Qualiopi certification makes them eligible for OPCO funding (French training funds).

The question to ask: “Who trains the users, on which cases, and who answers their questions after delivery?”

6. Verifiable references

Ask for case studies with a name, a date, a scope and a measured result. A logo on a home page says nothing about what was delivered. “Five use cases scored in seven weeks” or “agents in production in five months” can be verified, and you can ask to discuss them with the client.

The question to ask: “Can you put us in touch with a client whose project is similar to ours?”

AI agency or consultancy: three situations, three answers

The right entry point depends mainly on where you are now.

Your need is written down and costed

You know which task to automate, how long it takes and what you want to gain. A development partner can start quickly, whether it is an agency or a consultancy’s Lab. Still insist on a POC on your data and on code ownership. For a targeted need, our custom AI agent development offer takes three days of work, with a test phase by your teams in real conditions before the final iteration.

You have several ideas, none of them costed

This was the situation at JCDecaux France’s IT department before scoping. Building the first idea that comes along is a gamble. A scoping engagement runs each case through the same grid, and you only fund the ones that hold up. Our AI consulting support starts from your business divisions and delivers a prioritised roadmap.

Your AI tools are in place, but little used

Copilot or ChatGPT licences lying unused, teams reluctant to use them: a new custom tool will not change that. Train the teams first on their real use cases. Ideas for agents then come from the business teams themselves, as at ENGIE.

Launching your AI provider tender in five steps

Whether you approach an agency, a consultancy or both, these five steps make the responses comparable.

1. Describe the problem on one page

The task, who does it, how long it takes, what you want instead. Leave the solution aside: proposing it is the provider’s job.

2. List your constraints

The data involved, the tools in place (Microsoft 365, Google Workspace, your CRM) and your IT department’s security rules. A provider who asks no questions about this has surprises in store for you.

3. Ask for a short, costed scoping phase

A few weeks, a decision grid announced before the workshops, and a verdict per use case. At JCDecaux, it took seven weeks from kick-off to the final presentation.

4. Insist on a POC on your data before the go-ahead

A demo on dummy data tells you nothing about your files, with their gaps and exceptions. The POC is there to check this before committing the MVP budget.

5. Include adoption in the quote

User training, usage measurement at 30 and 90 days and a point of contact after delivery should all appear in the proposal, alongside the development.

Not sure yet where your company stands? Our free AI assessment measures its maturity in 15 questions and about 4 minutes.

Key takeaways

• Judge a provider on specific criteria rather than on the “agency” or “consultancy” label.
• No development without an estimated gain per use case and a POC on your data.
• The code must belong to you, and the contract must say so.
• Hosting, DPA and access rights are settled at the scoping stage.
• User training is part of the project from the quote onwards.
• Ask to speak to a client whose project is similar to yours.

Sources

• AI Sisters, From idea to tool: JCDecaux France’s IT department identifies €2.19M in annual gains thanks to AI, case study, accessed 6 November 2026.
• AI Sisters, Sales prioritisation: Energies France equips its 30 salespeople with a custom AI platform, case study, accessed 6 November 2026.
• AI Sisters, AI agents in business: the ENGIE case, case study, accessed 6 November 2026.
• Gartner, Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027, press release, 25 June 2025.