Digital commerce teams rarely lack ideas. Most understand how AI, data, and personalisation could improve customer experiences. The problem, as explored in this episode of Don’t Panic, It’s Just Data, is turning those ideas into something that works at scale, in real time, and without slowing the business down.

Hosted by Dana Gardner, Principal Analyst at Interarbor Solutions, the discussion brings together Jürgen Obermann, Senior GTM Leader EMEA and Piotr Kobziakowski, Senior Principal Solutions Architect from Vespa.ai. Rather than focusing on hype, the conversation centres on the everyday realities of modern e-commerce systems and why progress often feels harder than it should.

When AI Meets Legacy Digital Commerce

AI introduces new expectations around speed, relevance, and adaptability. As a result, many digital commerce platforms are built on foundations designed for a different era. Years of development have resulted in fragmented environments, often based on microservices that once provided flexibility but now introduce complexity.

As Jürgen explains, even small changes can trigger long delivery cycles. Engineering teams may need months to safely update systems, not because the ideas are difficult, but because the infrastructure has become fragile.

Search and Personalisation Are Still Disconnected

Search is where most e-commerce journeys begin, yet many platforms still rely on keyword-focused approaches that struggle to interpret intent. Customers expect results that reflect who they are, what they want, and why they’re searching. Delivering meaningful personalisation requires systems that combine signals, context, and ranking logic in real time. Without that, experiences remain generic even when data is available.

Architecture Becomes the Bottleneck

The conversation then turns to architecture. Traditional search stacks, particularly Lucene-based systems, often hit performance limits when vector operations and advanced ranking are introduced. These capabilities tend to be bolted on rather than designed into the core. Piotr highlights a deeper issue, which is fragmentation. Search, ranking, recommendation, feature stores, and inference engines often live in separate systems. Each integration adds latency, duplicates data, and slows innovation.

A More Grounded Path Forward

This episode of Don’t Panic, It’s Just Data offers a calm, practical view of AI in digital commerce. Progress comes not from adding more complexity, but from simplifying how systems work together. When search, personalisation, and recommendation are designed as part of a cohesive whole, digital commerce platforms become easier to evolve and better equipped to serve both customers and the business.

For more insights into modern search architectures and AI-native commerce platforms, visit Vespa.ai.

Takeaways

  • Many teams see the potential of AI, but face practical blockers.
  • E-commerce companies struggle with operational, customer experience, and business challenges.
  • AI technologies enable sophisticated personalised search experiences.
  • Architectural bottlenecks often hinder e-commerce systems' performance.
  • AI-native architectures can significantly improve search capabilities.
  • Real-time personalisation is crucial for enhancing user experience.
  • Separate systems for search and recommendations create inefficiencies.
  • Phased migration is essential for transitioning from legacy systems.
  • AI's impact on revenue can be profound when implemented effectively.
  • Vespa is a comprehensive platform that integrates various functionalities.

Chapters

00:00 Introduction to AI-Driven Search in E-Commerce

01:38 Challenges in Adopting AI for Digital Commerce

04:02 Architectural Bottlenecks in E-Commerce Systems

07:39 Designing AI-Native Search Architectures

12:00 Advancements in Personalisation for E-Commerce

16:21 Inefficiencies of Separate Search and Vector Systems

19:24 Phased Migration to AI-Native Platforms

21:51 Business Implications of AI in Search

23:57 Advice for Technical Leaders in E-Commerce