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The Agentic Vision

My Role

As Global Director of Product Design for Lower Funnel & Payments, I led the end-to-end strategy and design direction for the initiative. My responsibilities included:

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  • Defining the AI vision and product strategy for the Lower Funnel organization.

  • Prioritizing post-purchase as the initial focus area based on customer research, business impact, and organizational readiness.

  • Leading discovery by analysing customer feedback, CRC contact data, and benchmarking 22 leading retailers and logistics companies.

  • Facilitating cross-functional co-creation workshops with Product, Engineering, Research, Content Design, Data, and Customer Relationship Centre stakeholders.

  • Guiding the team from research to concept development, resulting in a portfolio of AI-powered customer experiences.

  • Establishing the strategic principle that "AI should amplify clarity, not replace it," and defining the long-term roadmap from assistive AI toward agentic commerce.

  • Aligning stakeholders around a research-led AI strategy and ensuring proposed concepts were evaluated against business objectives before entering delivery.

Executive Summery

Artificial Intelligence had rapidly become one of the company's highest strategic priorities. Across Decathlon there was significant excitement about AI, but also growing concern.Everyone wanted to build AI.

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Very few teams could clearly articulate which customer problems AI should solve. Within the Lower Funnel organisation, responsible for Purchase, Payments, Delivery and Post Purchase,we recognised a significant risk: We could easily spend months building technically impressive AI features that customers neither wanted nor trusted.

Internally this was often referred to as avoiding "AI slop"building AI simply because it was fashionable rather than because it solved meaningful problems.

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Rather than starting with technology, I proposed that we begin with customers.

One of the most important outcomes wasn't an individual feature. It was establishing a long-term strategic vision.

Most organisations viewed AI as: Customer → Chatbot → Answer. I proposed a broader evolution.

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Today

  • Assistive AI

  • Customers ask.

  • AI responds.

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Tomorrow

  • Agentic AI

  • AI observes context.

  • AI anticipates needs.

  • AI completes tasks on behalf of customers.

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We also began exploring how future AI agents, such as ChatGPT Operator and emerging commerce agents could transact on behalf of customers through protocols such as the Universal Commerce Protocol (UCP) and Agentic Commerce Protocol (ACP).

Rather than designing only for today's interfaces, we began preparing Decathlon for a future where AI becomes an active participant in commerce.

Building Alignment Across the Organisation

The success of the initiative depended as much on organisational alignment as product design.

I worked closely with:

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  • Product leadership

  • UX Research

  • Staff Designers

  • Content Design

  • Data leadership

  • Customer Relationship Centre teams

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One particularly important achievement was aligning our work with the CRC team's own AI initiatives.

Rather than building competing solutions, we converged research, shared insights and reduced duplication across the organisation.

Key Learnings

This project was far more than an exploration of Generative AI. It represented a shift in how product strategy was developed.

Rather than asking, "How can we use AI?", we reframed the conversation to "Where can AI create measurable value for customers and the business?"

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By grounding innovation in customer research, cross-functional collaboration, and a clear strategic vision, we established a roadmap that moved beyond reactive support toward an intelligent, proactive post-purchase experience—laying the foundation for Decathlon's future in Agentic Commerce.

AI cannot compensate for poor information architecture.

Before building intelligent experiences, organisations must establish a trusted, structured source of truth.

Research prevents expensive AI mistakes.

Benchmarking competitors and understanding customer pain points prevented investment in low-value features and helped the team focus on meaningful opportunities.

AI should reduce cognitive load, not add to it.

The most valuable experiences weren't conversational interfaces.

They were moments where customers didn't need to think because the right information appeared at exactly the right tim

Product strategy matters more than AI capability.

The biggest challenge wasn't choosing the right model.

It was choosing the right customer problem.

Business impact

Although the concepts remained strategically validated rather than fully implemented, the initiative fundamentally changed how AI investment was approached within the Lower Funnel organisation.

Expected outcomes included:
 

  • Reduced Customer Relationship Centre contact volume

  • Improved customer autonomy

  • Higher Net Promoter Score (NPS)

  • Increased Customer Lifetime Value (CLTV)

  • Lower operational costs through intelligent self-service

  • Increased commercial opportunities through proactive recommendations
     

Most importantly, every concept was evaluated against business outcomes before entering delivery.

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