Product data is not an operational problem. It's a growth problem.

Most companies treat product data as an operational problem. Something to manage, maintain, and fix. It's the cost of doing business. That framing is costing them more than they know.

Product data infrastructure is not an operational problem. It is a growth constraint. The way your product data is structured, connected, and activated determines how fast your business can move - how quickly you can launch, how many destinations you can reach, and whether your products show up when AI agents go looking.

Get it right, and you remove the ceiling. Get it wrong, and you spend your time banging your head.

The ceiling nobody talks about

Weeks become months. The opportunity shrinks. A competitor gets there first.

Or a campaign needs to go live. The merchandiser needs to pull data from three systems, reconcile inconsistencies, and wait for IT to configure the feed. By the time everything is ready, the moment has passed.

What fragmented product data costs

When companies calculate the cost of their product data setup, they typically look at licence fees and perhaps the cost of the last big integration project. But what about everything in between?

Every integration project. Every new destination, every new system, every new data source requires an integration project. Weeks of IT time, development budget, and project management, for something that should take days. Multiply that by every initiative in the roadmap.

The IT bottleneck. Category managers, merchandisers, and ecommerce teams all need to act on product data. When the infrastructure is not built to empower them, every change goes through IT. The backlog grows. The business slows.

Duplicate tools. When one system cannot do everything, organisations add another. Then another. Each tool solves a slice of the problem and adds its own integration overhead. The total cost of ownership compounds quietly.

Delayed launches. Every day a product is not live is a day it is not generating revenue. When product data infrastructure is the bottleneck, that delay is systematic.

AI readiness debt. AI agents and AI-powered search are already evaluating and recommending products. They need complete, structured, machine-readable Master Product data to do it. Fragmented product data does not just slow your team down. In agentic commerce, the era where AI agents search, evaluate, and purchase on behalf of buyers, it makes your products invisible.

Nelly.com understood this. Before restructuring their product data infrastructure around Occtoo, their IT team numbered around 20 people managing the complexity. After: four. The infrastructure stopped being overhead and started being an engine.

The infrastructure ceiling in practice

You know you have hit the ceiling when:

Adding a new destination requires a project, not a configuration
Getting a product live takes weeks when it should take hours
Your teams are waiting on IT for data they should access themselves
Every new use case means rebuilding something that should already exist
Your product data is not complete enough to feed an AI agent confidently

What the right infrastructure makes possible

The companies moving fastest on launches, new destinations, and AI readiness have one thing in common: their product data infrastructure is not a constraint.

They use a Product Orchestration Engine — a layer that sits between their data sources and their destinations. It connects to wherever product data lives: ERPs, PIMs, supplier feeds, and marketing tools. It builds the complete Master Product: the single, trusted view of everything needed to make a product sellable anywhere. And it activates that data to any destination, any team, any AI agent. In real time. Without a project.

The outcome? Speed that compounds.

New integrations that used to take months take days. Alligo moved product data activation from days to hours, and new use cases that previously required months of work can now be enabled in days. Nelly.com replaced their entire commerce stack in six months and reduced IT complexity by 80%. Nordic Nest shortened lead times, launched more products, and maintained their highest quality standards.

"With Occtoo, we've been able to consolidate complex data sets, avoid data silos, reduce unnecessary storage, and build an efficient information architecture."

Emil Björkman, Enterprise Architect, Fenix Outdoor

Infrastructure is strategy

The companies that will win the next phase of commerce are not the ones with the biggest budgets or the boldest AI strategies. They are the ones whose product data infrastructure can keep up with what they are trying to do.

AI agents need complete, structured Master Product data to discover and recommend products. New destinations need data that is ready, not data that needs three months of preparation. Teams need to move without waiting on anyone.

The Product Orchestration Engine

Your product data infrastructure is either an accelerator or a ceiling. Right now, for most companies, it is a ceiling.

Think you need a better data setup? Think bigger than PIM.

Book a 20-minute intro call to see what product orchestration looks like in practice.

Frequently asked questions

What is the difference between a PIM and a Product Orchestration Engine?

A PIM manages structured product attributes and content for a defined set of Destinations. A Product Orchestration Engine manages the complete Master Product — including pricing, inventory, sustainability data, supplier information, and the contextual content AI agents need — and activates it anywhere without integration overhead. Many organisations keep their PIM and add a Product Orchestration Engine above it, as Nelly.com did with Akeneo and Occtoo.

How much does fragmented product data actually cost?

Most organisations only count licence fees. The real cost includes integration projects for every new system or Destination, IT time spent on routine data requests, delayed product launches, and duplicate tools filling gaps. Nelly.com reduced its IT team from 20 to 4 after restructuring its product data infrastructure — an 80% reduction in overhead. When you count everything, fragmented product data is one of the most expensive line items nobody tracks.

How do I reduce IT dependency in product data management?

The shift happens when business teams — category managers, merchandisers, ecommerce teams — can access and act on product data without raising a ticket. That requires a product data layer built for self-service, not just for technical users. With Occtoo, teams work directly in the platform. IT retains control of the Master Product without becoming the bottleneck for every change.

What is a Product Orchestration Engine?

A Product Orchestration Engine connects to wherever product data lives — ERPs, PIMs, supplier feeds, marketing tools — and builds a unified Master Product. It then activates that data to any Destination: web, mobile, B2B portals, smart devices, or AI agents. Unlike a PIM, it manages the complete product picture and activates it without lengthy integration projects.

How long does it take to implement product orchestration?

Faster than most organisations expect. Alligo moved product data activation from days to hours. Nelly.com replaced their entire commerce stack in six months without customers noticing. New integrations that previously required months can now be enabled in days. The right approach is to build from the foundation up — getting the Master Product right before adding new capabilities.

What does my product data need to do for AI agents to find my products?

AI agents evaluate structured data: attributes, certifications, usage scenarios, sustainability context, pricing, and brand narrative. If any of that is missing or incomplete, the agent cannot confidently recommend your product.