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Montréal, QC, H2J 2L1

How about starting your AI strategy with a pilot project?

AI seems accessible, right up until your projects go overbudget and exceed both projected hours and everyone’s enthusiasm. Few insights. A smattering of AI tools. Barely any productivity. Then the project ends up in an archived folder without even having moved past the proof of concept. Your team had wanted to move fast and aim big. But the right way is actually the reverse: Begin little by little, and the marketing department is the best place to get started. This is often where we already have the most data ready to be exploited as well as the most processes ready to be automated.

80%–90% of AI projects fail, not because of the technology, but due to a lack of alignment between data, processes, expertise, and business objectives.

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Your AI strategy confronted by real life

We put your AI projects to the test to validate their true business value.

Use case and build vs. buy analysis

Use case and build vs. buy analysis
Prioritize AI use cases with real impact on your business objectives and true feasibility. Together we evaluate the options to test them with existing tools, configurable platforms, and made-to-measure models (build vs. buy).

1

Preparation and centralization of data

Prepare your ecosystem for the AI pilot project—CRM, media, e-commerce, and CX. We audit, centralize, and structure your existing marketing data within a secure framework in order to supply the AI with signals that are reliable, consistent, and actually exploitable.

2

Design of the AI pilot project

Test your needs in AI within a real-world framework. We develop or configure relevant AI models based on your use cases—segmentation, scoring and prediction, recommendation, marketing automation, personalization—then we train them on your data in a controlled environment.

3

Measurement, learning, and decision-making

Get a clear view of the results of the AI pilot project thanks to shared indicators (KPIs). Along with your marketing and data teams, we interpret the results and arrive at a clear decision: deploy, adjust, or end the project.

4

Preparation for roll-out

Prepare roll-out from pilot project to in-production. We gather together documentation, coordinate knowledge transfer, and build a road map to wide-scale deployment while aligning teams, processes, and technologies.

5

We master the tools that matter

Why us?

To us, an AI pilot project isn’t a technology demo. It’s a tool for business decisions.

We prefer an agnostic build vs. buy approach that doesn’t leave out any options. We analyze the value of AI from every angle: business strategy, marketing, data and engineering, machine learning. We guide you from strategic thinking to testing, then on to the next steps—whether the pilot advances to large-scale roll-out or not.

Our clients’ results say it all.

85%
accuracy in detecting churn
Reduce customer churn through predictive modeling and AI
Artificial Intelligence +13

In a high-growth context where retention is key to profitability, we helped WeCook turn its data into a competitive advantage.

By building a unified data warehouse and developing an AI-powered churn prediction model, we enabled the team to identify at-risk customers and act proactively, with insights directly usable by marketing teams.

Results: 85% accuracy in detecting at-risk customers, a full implementation completed in just one month despite complex data sources, and a stronger ability to generate advanced insights to optimize marketing operations.

Proof that a strong data foundation, combined with AI, can turn complex data into actionable business decisions.

Finding answers is in our DNA.

What is a marketing AI pilot project?

It’s a test of a precise AI use case with real data, within a controlled framework. The goal is to validate business value, feasibility, and adoption before any large-scale deployment.

Why start with an AI pilot project instead of quickly deploying a complete solution?

To reduce risk and make fact-based decisions. The pilot enables rapid learning and the measurement of real impact and lets you avoid investments without proof of value.

Why do so many AI pilot projects in marketing fail?

Because the data are fragmented, the use cases are poorly connected to business objectives, and the teams are working in silos. The failure is usually organizational and rarely technological.

Do you have to build an AI solution or buy one (build vs. buy)?

That depends on the use case, data, and maturity. Buy when the tools fulfill your needs, build when it’s justified by a competitive advantage.

How do you measure the success of an AI pilot project?

By its capacity to generate exploitable insights, produce productivity or performance gains, and be adopted by teams. Its results lead to decision making: deploy, adjust, or end the project.

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You know where you want to go.
We know how to get you there.


Tell us what you need and we’ll help you find the best solution.
Contact us or 514-589-4563

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