This week, we posed ourselves a challenge:

To summarise how key ecommerce customer & technology trends like agentic commerce and AI product discovery are going to reshape ecommerce strategy, team structures & the expectations ecommerce business have of employee capabilities and partner skills.

This episode covers our main observations and insights:

• Treat product data as a growth channel.
• Have AI-native product + engineering leadership.
• Run continuous experimentation with agents.
• Properly integrate marketing + data + tech.

Ecommerce is seeing a step change from:

Humans browsing sites, clicking ads & buying products.

To:

Multi-agent systems influencing discovery, comparison and to a lesser extent checkout. More discovery will take place outside the traditional website, and agents & LLMs guide shoppers through decision paths before handover to commerce endpoints (with the website remaining the primary destination).

👉 This creates three major shifts that we explore in more detail:

1. Discovery evolves from SEO → SEO + AEO (Answer Engine Optimisation); visibility depends on machine-readable product data as well as content.
2. AI agents become a new type of shopper, and ecom platforms need API capabilities to facilitate 2-way data and logic flows; agents can compare, filter and decide based on structured data + trust signals.
3. Commerce becomes API-first + autonomous; some purchases happen inside AI ecosystems (chat, assistants, agents) & there’s a need for seamless handoff between AI agents & conventional commerce.

Find out how this is already having an impact on team structures and skill needs.

"Shopify POS is a powerful foundation, but it’s not a turnkey solution for every retail scenario. The platform's maturity is a moving target, and understanding its ecosystem gaps is crucial for strategic planning and preventing costly misalignments."

Joseph Brown, Operations Director, Kubix

Are you considering Shopify POS for your retail operations but unsure about its strengths and limitations?

You're not alone.

As one of the leading ecommerce platforms, Shopify has rapidly expanded into the retail point-of-sale space, but its product is still evolving. In this pod, we explore the realities of implementing Shopify POS backed by expert insights from Joe Brown, Operations Director at Kubix, who has direct experience implementing POS for different business models.

Whether you're running a standalone store or extensive retail estate, this episode has practical advice, product limitations and decisions that can make or break your omnichannel strategy.

The reality…

Shopify POS has become more capable for retail, especially in multistore environments with enhanced permissions and faster workflows. However, it still has capability gaps that can surprise retailers, and requires careful planning, discovery and customisation.

Key discussion points

1. Deep discovery is critical

A recurring theme is the importance of thorough discovery when planning POS projects. Retailers should map existing workflows, identify edge cases (like made-to-order products, custom packing, or complex stock movements) and assess how Shopify’s platform supports or complicates these processes.

Rushing into implementation without understanding detailed workflows can lead to costly rework or operational issues down the line.

2. Inventory management limitations

One of Shopify's gaps is in inventory management for complex use cases. For example, handling stock exchanges between stores where products are unavailable locally remains problematic.

Shopify currently supports split fulfillment orders but lacks native support for multi-quantity line items or real-time transfer workflows, which can frustrate larger or more nuanced operations.

3. Hardware cost & compatibility

POS selection is more than a software decision; hardware investment is foundational. Some issues arise when existing custom integrations, like bespoke receipt printers or scanner setups, are incompatible with new POS hardware or updates.

Testing hardware thoroughly before rollout is essential, and technical teams need to validate network setups, peripherals and existing workflows.

Practical tip: Shopify's recommended hardware kits may not suit every store. Custom hardware may be necessary, but it can add complexity and cost.

4. Ecosystem maturity and functional gaps

While Shopify's ecosystem is growing, certain functionality including multi-currency gift cards, B2B support or advanced inventory tracking, lag behind expectations.

Retailers with complex order workflows may need to integrate third-party apps or custom solutions to fill these gaps.

Chapters

[00:45] Introduction to Shopify POS and Its Evolution

[03:30] Market Positioning and Retail Challenges

[06:40] Discovery Process in Retail POS Implementations

[09:25] Hardware Considerations for Shopify POS

[12:20] Custom Development Needs in Retail

[15:10] Omnichannel Experience and Customer Journey

[17:55] Integration Challenges with Legacy Systems

[20:50] Inventory Management and Workflow Complexities

[23:15] Future Improvements and Wish List for Shopify POS

“In just an hour, I built a UI to interrogate my data, and it handled most of the heavy lifting for a client project."

Chris Marshall, Director & Co-founder, OnState.

Optimising Ecommerce Data with AI: Real-World Applications

Yes, we talk about AI a lot on the podcast. It's inevitable, AI is weaving its way into so many ecommerce processes and tasks.

This episode is highly practical.

We cover real-world examples of how AI tools are being used to speed-up product data tasks whilst reducing the need to rely on expensive licences for specialist tools.

Summary

Ecommerce businesses are increasingly turning to AI to enhance their data management processes. The pod explores how AI tools are being used to clean, enrich, and structure product data, providing real-world examples that highlight their practical applications.

The Build vs. Buy Dilemma Is Being Reframed

Businesses often face the decision of whether to build custom solutions or purchase existing platforms.

In the context of AI for product data, building allows for tailored solutions using tools like Google Sheets and AI models such as ChatGPT for tasks including data transformation and HTML cleaning.

On the other hand, buying involves using specialized AI-enabled tools or outsourcing, which can save time but may incur higher costs.

Practical AI Strategies Discussed:

1. DIY data cleaning: AI models can automate data cleaning tasks, such as reformatting unstructured HTML and standardising attributes, saving significant manual effort.
2. Automating data structure: AI can analyse complex datasets, infer attribute types, and suggest categorisation rules, streamlining the setup of dynamic product groups.
3. Hybrid approaches: combining DIY methods with outsourcing can optimise resources, allowing businesses to handle unique projects efficiently.

Tune in to hear how AI is transforming data migration and management by automating previously manual tasks, increasing speed and allowing for continuous learning.

Chapters

[00:30] The Build vs. Buy Debate in AI Data Management

[03:20] AI in Data Migration: Practical Use Cases

[06:15] Transforming Data with AI Tools

[09:20] The Role of AI in Content Management

[12:20] Engaging with Data Structures

[15:00] Building Custom AI Tools for Specific Needs

[17:45] Tactical Middleware: A New Approach

[20:35] Speeding Up Data Transformation Processes

[23:20] Validating AI Outputs and Managing Expectations

[26:15] The Future of AI in Ecommerce Data Management