95% of retailers use AI. Only 5% see ROI. Read the report →

On demand webinar: AI advantage isn’t a feature race. It’s a system design challenge.

AI experimentation is widespread in retail, but real, scalable returns remain rare. In this webinar, we explore how retailers can move from pilots to real commercial impact, sharing insights from new research and what leading teams are doing differently.

AI is not a feature race_Voyado_Retail Economics_Webinar
AI advantage webinar coverAI advantage webinar cover

95% of European retailers are experimenting with AI. Yet only 5% report clear, scalable ROI. With €14.9 billion of retail marketing and e-commerce spend set to be reshaped by AI, adoption is no longer the differentiator. Structural maturity is.

Most teams are somewhere in the middle: Running smarter campaigns. Testing new tools. Improving efficiency. But struggling to embed AI deeply enough across data, workflows, and decision-making to see real return on their investment.

And the biggest blocker isn’t technology. It’s culture, skills, and knowing where to start. AI doesn’t scale because it’s smart. It scales when organizations are built to use it. And time saved only matters when it turns into better decisions and stronger outcomes.

In this webinar, Richard Lim, CEO of Retail Economics, Felix Kruth, CPO at Voyado, and Aron Lewis, Retail Expert, unpack insights from our latest research report and explore what it really takes to move from experimentation to embedded, agentic decision-making across marketing and e-commerce.

The webinar focuses on what needs to be in place across:

  • Data foundations
  • Organizational culture and skills
  • Governance and decision-making

What you’ll learn:

  • Where retailers are today on the AI maturity curve, and why most are stuck in the middle
  • Why skills and operating models matter more than tools and features, and how leading teams are organizing around AI
  • How AI agents are already reshaping retail decisions and execution across the customer journey.
  • What data and infrastructure must be in place to successfully scale AI agents

More inspiring guides