How to choose the right AI and Data agency: 7 key questions to ask

The AI and data market is booming. Dozens of players present themselves as “experts” in artificial intelligence, data science or digital transformation. However, according to Gartner, over 80% of AI projects fail to scale.

The reason is not technological. It lies in data preparation, alignment with businesses, the ability to measure value and promote adoption. In other words: the problem is not the code, but governance.

Choosing an AI and Data agency is therefore not an operational decision, but a strategic one. To avoid pitfalls, here are the seven questions to ask to distinguish a simple technical supplier from a true transformation partner.

1. What is your approach to data?

Before talking about AI, we need to talk about data. An AI is never better than the quality of what is provided to it. A serious agency should be able to detail its approach to reliability, cleaning and harmonization of data.

Beyond “data cleaning”, the question of governance is central: how do you ensure that data remains usable, traceable and enriched over time? An agency that does not offer a clear vision on this point risks producing a project that is brilliant at the start but unusable after a few months.

2. How do you combine technology and business?

An AI that does not improve a business KPI does not provide value. Many agencies fall into the trap of “technocentrism,” focusing on the most advanced model rather than the real impact.

The right question to ask is: “Can you show me a case where AI improved a concrete business indicator?”.

A strategic partner will not only talk about models, but about results: 15% reduction in delivery times, 10% increase in the conversion rate, improved accuracy of financial forecasts.

It is this articulation between technology and profession that makes the difference between a technical demonstration and an operational transformation.

3. What is your sector experience?

AI is not universal: each sector has its own specificities. Asking an agency for its experience in your field is an excellent indicator of its maturity.

  • En industry, AI must deal with the complexity of production chains and the importance of predictive maintenance. Missing or noisy data can have a direct impact on industrial performance.
  • En real estate and asset management, the value lies in the ability to cross-reference heterogeneous data (land, energy, rental) and to integrate legal or regulatory constraints.
  • En Finances, the priority is the traceability and robustness of the models, because each prediction must be able to be justified to regulators and risk committees.

A successful model in one sector does not automatically translate into another. One AI & Data agency credible must demonstrate its ability to contextualize AI to your business.

4. How do you measure value and ROI?

An AI project should generate value, not just deliverables. However, many agencies limit themselves to talking about “model accuracy” or “response time.”

A strategic partner should be able to explain how it calculates the Real ROI of an AI project. This includes:

  • the savings generated,
  • the time saved,
  • improving the quality of decisions,
  • or even the creation of new revenue opportunities.

Example: an algorithm for forecasting demand in logistics is not valuable because it is 92% accurate, but because it reduces storage costs by 12% and optimizes cash flow.

5. What is your technological independence?

The market is dominated by technological giants (Microsoft, Google, AWS, OpenAI). Many agencies are heavily dependent on a single supplier, which poses a risk of lockdown.

The question to ask is: “Are you able to work in multi-technology?”

A strategic partner will propose an architecture open, modular and reversible, which protects your independence and allows you to adapt your technological choices according to market changes or regulations.

An agency that consistently pushes for the same solution, regardless of the context, does not act in your interest but in that of its commercial partner.

6. How do you support adoption?

The main cause of failure of AI projects is not technical, but human. Resistance to change, lack of understanding of results, lack of training... so many obstacles that condemn a project.

A credible agency should present a clear plan to support adoption. This includes:

  • training courses adapted to teams,
  • a change management plan,
  • progressive pilots that make it possible to quickly demonstrate value and engage users.

AI doesn't transform anything if it's not used. The role of the agency is therefore as much to work on culture as on the code.

7. What is your long-term vision?

An AI project is not an end in itself, it is the start of an ongoing process. Data is changing, models need to be retrained, regulations are changing.

The key question is: “What value will be created in three to five years?”.

A strategic partner will propose a maturity trajectory:

  • gradual enrichment of data,
  • increasing the competence of the teams,
  • adaptation to regulatory changes,
  • integration of new technologies in a sustainable way.

One AI and Data agency who only thinks about the immediate effect of a POC will not bring lasting value.

Conclusion: choosing a partner, not a service provider

These seven questions are not trivial. They make it possible to distinguish a simple tool supplier from a true strategic partner.

  • A service provider will talk about models, deadlines and prices.
  • A partner will talk about governance, value, independence and long-term trajectory.

Choosing an AI and Data agency is not choosing a service provider. It's deciding who will guide your business in its digital transformation.

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