PIM & MDM / AI Building the Foundation for Future Commerce: Master Product and Agentic Readiness

At their core, these terms are about something businesses have always needed: structured data, clear processes, and strong organizational alignment.

As commerce expands beyond the traditional web storefront, companies must prepare for a future where transactions can happen anywhere: through AI assistants, automated procurement systems, connected devices, or entirely new digital touchpoints.

This article explores what these concepts actually mean, what it means for businesses preparing for what comes next and how a major Nordic B2B retailer navigated this journey.

What Is a Master Product?

Most organizations store product information across multiple systems. ERPs, PIMs, supplier feeds, marketing tools, etc. The result is often fragmented, inconsistent data that creates friction across all the different sales channels.

The concept of the Master Product addresses this challenge head-on. A Master Product is a single, unified view of all product-related information required to make a product sellable across any channel.

This includes:

• Technical specifications and attributes (e.g., standardized data models like ETIM)

• Pricing and campaign information

• Stock levels, delivery times, and regional availability

• Rich content such as descriptions, images, and sustainability information

• Customer-specific data and order history

• CMS content linked to product categories

• Contextual narratives that define how your brand describes products — not just what the product is, but what it means to you and your customers

This last point is increasingly important. An AI assistant already knows general facts about your product, but it doesn’t know how your business thinks about sustainability, sourcing standards, or application-specific use cases unless you provide that context as structured, machine-readable data.

Instead of each system holding its own version of the truth, the Master Product becomes the trusted source that powers every commerce interaction.

What Agentic Readiness Really Means

Agentic Readiness refers to an organization’s ability to support AI-driven commerce, where purchasing decisions may be triggered, researched, or even completed entirely by AI systems acting on behalf of buyers. Traditionally, the “buy button” lived on a retailer’s website. However, that model is changing fast and today purchasing interactions can occur through:

• AI assistants like ChatGPT or Gemini

• Search engines like Google

• Automated procurement systems

• IoT devices such as smart cabinets

For AI agents to discover your products and execute transactions, your underlying data must be structured, accessible, and machine-understandable. Agentic Readiness is therefore not about adopting a new interface but ensuring your product data ecosystem is prepared for AI-driven discovery and transactions.

A useful parallel: AI agents in commerce are, in many ways, an evolution of EDI and punch-out solutions that B2B organizations have used for decades.

The principle is the same: Enabling a transaction to occur in a third-party environment and receiving the resulting order into your systems. The difference is scale, speed, and the fact that AI agents will be far more widespread than any EDI integration.

The Alligo Journey: Building from the Ground Up

Alligo, a major Nordic B2B group operating brands such as Swedol in Sweden and Tools and Grolls in Norway and Finland, provides a practical example of what this transformation looks like.

Following a major merger of several companies, Alligo faced a fragmented technology landscape: multiple PIM systems, several ERP platforms, different data standards, and distinct organizational cultures. Rather than jumping immediately to AI-driven initiatives, they chose to build from the ground up in different stages — a decision that now positions them well for the next wave of commerce innovation.

Stage 1: Aligning Communication and Culture

The first challenge was organizational rather than technical. Since merging organizations brought incompatible systems, different ways of working, and distinct cultures, Alligo had to establish shared collaboration tools, align processes, and bring teams together under common goals before any data work could begin.

This organizational groundwork is often underestimated in digital transformation projects. Technology without cultural alignment delivers poor results.

Stage 2: Structuring Data Infrastructure

Next came the critical work of cleaning and structuring product data. This work was slower and more difficult than anticipated. Years of operating across separate systems had left a significant backlog of incomplete, inconsistent, and unstructured product information.

One major challenge was supplier data. Alligo adopted ETIM (an industry-standard product classification model) and had to systematically drive supplier compliance. Larger suppliers could adapt; smaller ones, such as manufacturers of basic tools or consumables, required different approaches.

The lesson from this phase was that data quality must be treated as a procurement requirement. Just as price and delivery terms are negotiated with suppliers, digital product data should be a formal part of supplier onboarding and contracts.

Stage 3: Enabling New Storefronts

With cleaner data and a consolidated product data foundation, Alligo was able to begin extending commerce into new contexts. One example is their smart cabinets, physical storage units equipped with RFID and weight sensors that automatically reorder products when items are removed. These cabinets effectively act as automated storefronts embedded directly within customer environments.

This is commerce without a storefront. The “buy button” is a physical interaction with a smart object. And it only works reliably because the underlying product data (SKUs, weights, inventory thresholds, pricing, and order routing) is structured and accessible.

Moving Toward Composable Commerce

Understanding why data foundations matter requires stepping back to look at how modern commerce architectures are evolving.

For years, most commerce platforms were monolithic with product data, pricing, inventory, and storefronts all managed in a single system. As these platforms grew, they became harder to maintain and slower to adapt.

Today, modern commerce architectures are increasingly composable.In this model, a product orchestration layer sits between your data sources and your sales channels. It consolidates product data from multiple sources, enriches it, and activates it for specific destinations, whether that’s a web storefront, a mobile app, a smart cabinet, a B2B portal, or an AI agent.

This architecture offers several advantages:

The Commerce Platform Becomes Lightweight

When product data is managed in an orchestration layer, the commerce platform itself can be lean. It only needs to hold the essentials for completing a transaction: product identifier, price, and quantity. Everything else lives in the data layer and is served as needed.

AI Agents Become Just Another Channel

From a data architecture perspective, serving an AI agent is not fundamentally different from serving a website or a smart cabinet. The orchestration layer structures and activates data for each destination. AI agents are a new destination with specific requirements, but the underlying data work is the same.

Hyper-Intelligent Products

With richer and more contextualized data, products become self-describing digital entities that can represent themselves accurately across different channels.

Faster Innovation

A flexible data layer also allows organizations to test new storefronts, channels, or technologies quickly. Companies can experiment, learn, and iterate without reengineering their entire backend infrastructure.

Key Takeaways for Businesses

Data is Still King

No matter how advanced AI becomes, success still depends on high-quality, structured data. There are no shortcuts here.

The Buy Button Is Moving

Commerce is no longer limited to a web storefront. Transactions can now occur through AI chats, automated procurement systems, connected devices, or embedded commerce environments.

Avoid the AI Hype Trap

Instead of chasing the AI hype, organizations should focus on building open, flexible data infrastructures that allow them to adapt quickly. Flexibility is the real competitive advantage here

Operations Matter More Than Interfaces

While AI interfaces may capture attention, the real work happens behind the scenes, managing product data, integrations, and operational processes.

Start with the pyramid

It is tempting to begin at the top with marketing automation, AI agents and personalization. The thing is, organizations that skip the foundational layers of data and infrastructure will struggle to make those capabilities work reliably. Build upwards.

Supplier Data Is Part of the Product

Organizations must increasingly treat digital product data as part of the deliverable. Suppliers should provide structured, high-quality data alongside the physical product itself.

The End Goal: Self-Describing Products

Ultimately, the ambition is to create products that can accurately represent themselves in any context. Whether the interaction happens on a website, through an AI assistant, within an automated B2B procurement flow, or via a physical smart device, products should be enriched with enough context and information to represent themselves accurately wherever they appear.

It is this kind of practical architecture that organizations like Alligo are actively building today. The tools exist and the frameworks are established. What is required is the organizational discipline to treat product data as a strategic asset and the patience to build the foundation before chasing the capability.

Businesses that build this foundation today will be the ones ready for the next generation of commerce.