We asked Alligo and Avensia a simple but tricky question: what does it actually take to be ready for what commerce is becoming? They did not say AI. They did not say new interfaces. They said product data. A strong, structured, and trusted master product. Get that right, and the rest follows. Turns out the behind-the-scenes foundation is what makes all the exciting stuff possible.
What is a Master Product?
Most organizations store product information across multiple systems: ERPs, PIMs, supplier feeds, marketing tools, and more. The result is fragmented, inconsistent data that creates friction across every sales channel.
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 means
Agentic Readiness is about being ready for a world where purchasing decisions are triggered, researched, or completed entirely by AI systems acting on behalf of buyers. It is happening faster than most people think. The buy button used to live on a retailer's website. 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 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: Start with the team, not the tools
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.
Photo: Alligo
Stage 2: Getting the data house in order
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: New channels, same great data
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 best-of-breed 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 — product data, pricing, inventory, and storefronts all managed in a single system. As these platforms grew, they became harder to maintain, slower to adapt, and increasingly disconnected from the reality of how products are actually sold today.
Today, modern commerce architectures are increasingly composable. And at the heart of that shift is the master product.
In a composable architecture, a product orchestration engine — like Occtoo — sits between your data sources and your sales channels. It pulls together product data from multiple systems, enriches it into a complete and trusted master product, and activates it for any destination — whether that is a web storefront, a mobile app, a smart cabinet, a B2B portal, or an AI agent.
This is the shift that changes everything. Instead of product data being scattered, siloed, and inconsistent, it becomes a single source of truth that powers every commerce interaction.
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 engine 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 show up correctly.
This is what the master product makes possible. Not just a cleaner data set, but a product that carries its own story, specs, pricing, sustainability context, and brand narrative — ready to be activated anywhere, by anyone, including an AI agent acting on behalf of a buyer.
Organizations like Alligo are actively building this today, with Occtoo at the center of their data architecture. The tools exist. The frameworks are established. What separates the companies that move forward from the ones that fall behind is the discipline to treat product data as a strategic asset, and the patience to build the foundation before chasing the capability.
This article is a collaboration with Avensia and Alligo.