WEBINAR | First AI Project in Retail: A Case Study

Launching a first AI project quickly raises practical questions: where do you start, which use case should you prioritize, and how do you secure buy-in from business teams?

During this webinar, we walked you through a real client case led by SBI Canada in the retail sector, a project our AI Consultant Kenza Battah worked on directly. The goal: break down, step by step, how this first AI initiative moved from zero groundwork to a live sales forecasting model used by operational teams.

Rather than a theoretical framework, this session is built around a concrete project, including its constraints, trade-offs, and measurable outcomes.

AI in Retail: A Strategic Lever for Performance 

In retail, artificial intelligence plays a critical role in improving sales forecasting, inventory management, promotional planning, and overall decision-making. A well-structured AI project can increase predictive accuracy while directly supporting operational and financial performance.

However, the success of an AI initiative in retail depends on more than algorithm performance. It requires reliable data, clear strategic alignment, strong governance, and most importantly, real adoption by business users.

Launching a First AI Project Without Disrupting Operations 

A technically strong model is not enough. What truly matters is its ability to integrate into existing processes and become part of everyday decision-making.

In the case we presented several challenges shaped the project from the outset:

• Clarifying business objectives and prioritizing a high-impact use case
• Working with imperfect and heterogeneous data
• Aligning technical and business teams
• Defining impact metrics that leadership could clearly understand

The focus was sales forecasting, a central lever for improving planning and operational efficiency. This disciplined scoping helped avoid fragmentation and ensured the solution was practical and usable.

What We Covered During the Webinar:

The session walked you through the key stages of the project:

• The client’s initial context and strategic drivers
• The main obstacles encountered at launch
• The structural decisions behind the model’s design and deployment
• The adoption mechanisms that enabled real usage

We also addressed execution, governance, and how impact was measured beyond purely technical metrics.

Key Takeaways 

This real-world case highlights several foundational principles for a first AI project:

• Start with a clear and prioritized business objective
• Design for adoption from day one
• Involve end users early to build ownership
• Measure value in direct connection with operational performance

In retail, the success of an AI initiative depends as much on disciplined scoping and execution as on model quality.

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