81% of European companies lack the structured lifecycle data that Digital Product Passports demand. That number comes from KPMG’s 2026 European Digital Product Passport Readiness Survey, and honestly, I’m surprised it’s not higher.

I’ve been inside 70+ PIM implementations across retail, manufacturing, and distribution. The pattern is always the same: companies invest six figures into a PIM system, fill it with product titles and descriptions, maybe some images and basic attributes - and then call it “structured data.” It’s not. It’s a polished spreadsheet with a login screen.

And now the EU is about to prove that with the Digital Product Passport.

What Does the Digital Product Passport Actually Require?

Let’s cut through the compliance jargon for a second. Battery DPP went live in January 2026 under EU Battery Regulation (EU) 2023/1542. That’s not a proposal. Not a draft. Not a “maybe next year.” It’s enforced right now, and non-compliant products are being physically pulled from the EU market by national surveillance authorities.

Textiles and electronics are next - mandatory enforcement starts in 2027, with delegated acts already finalized. Furniture, construction, and tyres follow in 2028. By 2030, every physical product sold in the EU needs a DPP.

Here’s what that passport must contain:

Data CategoryWhat DPP RequiresWhat Most PIMs Actually Have
Product identificationUnique ID linked to GS1 Digital Link, manufacturer detailsBasic SKU, maybe an EAN
Material compositionFull Bill of Materials, SVHC substances, recycled content %A text field saying “cotton blend”
Environmental footprintCarbon footprint per unit, energy consumption dataNothing, or a link to an annual CSR PDF
Durability and repairLifespan estimates, spare parts availability, repair manualsA PDF manual uploaded in 2019
End-of-life handlingRecycling instructions, disposal guidelines, disassembly infoA sentence on the packaging
Compliance certificatesDeclarations of conformity, test reports, audit trailA filing cabinet. Literally.

The gap between columns two and three? That’s the gap that EUR 14,000 per 1,000 products in manual data onboarding won’t close. Not at this scale. Not with these timelines. And honestly, most companies don’t even realize the gap exists until they actually map their current PIM attributes against the ESPR delegated acts for their product category.

Why Having a PIM Doesn’t Mean You Have DPP-Ready Data

This is where most consultants and vendors get it wrong. They say “just use your PIM for DPP.” Akeneo published a whole blog post about it. Pimberly, KatanaPIM, Inriver - all of them position PIM as the natural backbone for DPP compliance.

They’re not wrong. But they’re skipping the hard part.

A PIM stores and distributes product data. Great. But a DPP requires verified, auditable, continuously updated lifecycle data from sources your PIM has never touched. Material composition from your Tier 2 suppliers? Carbon footprint data from your manufacturing partner’s energy provider? Repair instructions that get updated when you change a component?

That data doesn’t live in your PIM. It lives in ERPs, PLM systems, supplier spreadsheets, email attachments, and - the real kicker - in people’s heads.

According to the KPMG survey, 31% of companies cite collecting data from suppliers and value chain partners as their top challenge. Another 25% struggle with understanding technical requirements. And 23% haven’t started any DPP work at all.

The thing is, your PIM is the distribution layer. It’s not the collection layer. And DPP compliance is fundamentally a collection problem.

So when someone tells you “your PIM handles DPP,” ask them: which of my 200 suppliers will send me structured lifecycle data in a format my PIM can ingest? How many of them even know what ESPR stands for?

The Real Cost of DPP Data Onboarding Without Automation

Let me put some numbers on this. From our experience across 70+ PIM implementations, manual product data onboarding costs roughly EUR 14,000 per 1,000 products for standard e-commerce attributes. That’s titles, descriptions, categories, basic specs.

DPP adds 2.5 to 3.5 times that cost. Because now you’re not just enriching marketing data - you’re collecting and verifying compliance data from multiple upstream sources, translating supplier documents, mapping materials to regulatory classifications, calculating environmental footprints, and maintaining audit trails.

For a mid-size retailer with 5,000 SKUs, we’re looking at EUR 180,000 to 250,000 in manual DPP data onboarding. An enterprise manufacturer with 25,000 products? That’s approaching EUR 1 million.

And here’s what nobody talks about: DPP data isn’t static. The regulation requires that passport data be updateable throughout the product lifecycle. Changed a component? Updated. New supplier? Updated. Reformulated a material? Updated. Regulators must be able to access the full update history - not just a snapshot.

That means DPP isn’t a one-time project. It’s an ongoing operational cost. Every product change triggers a data change that must be verified, documented, and published.

With AI-automated onboarding, those numbers drop by up to 95%. Not because AI magics away the complexity, but because it handles the extraction, classification, mapping, and verification at machine speed - while giving you a cost estimate before you even start the run. That’s the approach we built OpenProd.io around: know what you’re paying before you commit, and get structured output your PIM can actually use.

The KPMG survey paints an even more concerning picture of where European businesses actually stand:

Readiness Level% of Companies
Well-prepared (assigned roles, roadmap, C-suite sponsor)19%
Getting started (preliminary steps, no comprehensive roadmap)53%
Starting soon (planning phase)16%
Unprepared (no senior-level discussions)12%

Look at that middle row. 53% have taken some preliminary steps but have no comprehensive roadmap. That’s the danger zone. These are companies that feel like they’re doing something - maybe they’ve had a meeting, assigned it to the sustainability team, started talking to one supplier - but have no structured plan for actually collecting, verifying, and maintaining DPP data across their entire product portfolio.

And the ownership problem makes it worse. 46% assigned DPP responsibility to Sustainability, 26% to IT, 24% to Procurement. Only 20% involve R&D or Product Development. But DPP is fundamentally a product data problem that cuts across all of these teams. When sustainability owns it, IT doesn’t prioritize the systems integration. When IT owns it, procurement doesn’t engage suppliers. When nobody owns it - which is the case for 23% of companies - nothing happens at all.

The companies that are actually making progress? They’re the ones who recognized early that DPP is not a sustainability project. It’s a data architecture project with compliance outcomes.

DPP Meets Agentic Commerce - The Double Deadline

Here’s where it gets interesting. DPP compliance and agentic commerce readiness are converging on the same deadline, and they require the same thing: structured, machine-readable, continuously verified product data.

Stripe reported from NRF 2026 that 75% of retailers are either implementing or actively planning agentic commerce initiatives. OpenAI’s Yelena Reznikova said the key to capturing AI agent intent is “structured product feeds with clean, up-to-date item descriptions, pricing, and product availability.”

Sound familiar? That’s basically a subset of what DPP requires.

Companies that treat DPP as a standalone compliance checkbox are going to do the work twice. Once for the regulator, once for the AI agents. The smart play is building a single product data foundation that serves both - because the structured, verified, complete data that satisfies ESPR Article 9 is exactly what makes your catalog visible to AI purchasing agents.

We’re already seeing this with our clients. When you run product data through OpenProd.io’s AI onboarding pipeline, the output isn’t just PIM-ready - it’s structured enough to feed a DPP record and an MCP-compatible product feed simultaneously.

That’s not a coincidence. That’s what happens when you build your onboarding around data completeness instead of just marketing attributes.

What to Do Before Your Category’s Deadline Hits

if you’re in batteries, you’re already under enforcement. Skip to “get help immediately.”

For everyone else, here’s the sequence that actually works - based on what we’ve seen across 70+ implementations:

Quarter 1: Audit. Map every product attribute your DPP will require against what you actually have in your PIM, ERP, and PLM today. Use the ESPR delegated acts for your category as the checklist. The gap will be bigger than you expect. Most companies discover they have less than 30% of required DPP fields populated with verified data.

Quarter 2: Supplier engagement. This is where 31% of companies get stuck, per KPMG. Your Tier 1 suppliers need to start providing structured data - not PDFs, not emails, not “call Klaus in procurement.” Set up a standardized data collection template and start with your top 20 suppliers by volume. Use AI-powered data extraction to convert existing supplier documents into structured formats.

Quarter 3: Automate and connect. Plug your PIM into the upstream data sources. Automate the extraction, classification, and verification pipeline. Build the update mechanism so product changes automatically trigger DPP data updates. This is where manual processes permanently break down - you cannot maintain 25,000 living product passports with spreadsheets and good intentions.

Quarter 4: Validate and publish. Run compliance checks against the ESPR requirements for your category. Test your QR code and data carrier infrastructure. Make sure your data is machine-readable, hosted on EU-compliant infrastructure, and carries a full update history. Run a pilot batch of 50-100 products through the entire DPP generation workflow before scaling.

Miss this sequence and you’re looking at a panic scramble that costs 3x more and delivers 3x less. I’ve seen it happen with GDPR in 2018 - the companies that waited until the last six months paid consultants triple rates for half the quality. DPP will be worse because the data requirements are more granular and the penalties include market access, not just fines.

Your PIM is necessary but not sufficient. DPP compliance requires data your PIM has never managed - from sources it has never connected to - at a quality level it has never enforced. Treating this as “just another data field to add” is how you end up with products pulled from the EU market and a CFO asking why nobody flagged this earlier.

The companies that will navigate this well are the ones automating the hard part now: supplier data collection, lifecycle attribute extraction, and continuous verification. Not because automation is trendy, but because the math doesn’t work any other way.

EUR 14,000 per 1,000 products, multiplied by DPP complexity, multiplied by continuous updates, multiplied by regulatory penalties for getting it wrong. That’s the equation your CFO needs to see. And the only variable you can control is whether a human or an AI does the heavy lifting.

The Green Claims Directive adds another layer - fines up to 4% of annual turnover for incomplete or misleading product environmental claims. Your DPP data better be right, because “we didn’t have the data” is not a defense when your competitor’s products are on the shelf and yours are in customs limbo.

If you want to see what automated DPP data onboarding looks like in practice, book a demo and we’ll run your actual product data through the pipeline. You’ll get a cost estimate in minutes - not months. That’s the kind of predictability DPP compliance demands.

Sources and Further Reading