AI-Ready Teams: The Missing Piece of Luxury's Digital Product Passport Transition

The DPP Deadline Is a People Deadline

The EU's ESPR regulation will make Digital Product Passports mandatory for regulated goods from 2027. Most conversations about this transition focus on infrastructure: which standard, which chain, which schema. Galileo Protocol exists precisely to answer those questions in the open.

But there is a second, quieter deadline hiding inside the first one. A Digital Product Passport is only as good as the data your teams feed into it. Provenance records, material declarations, repair histories, authentication events: every field is captured, checked or enriched by a human being somewhere in the maison, from the atelier to after-sales.

And those human beings are, right now, largely untrained for the tools that will define their next decade.

The Skills Gap Is Measured, Not Anecdotal

A Microsoft France study published in February 2026 puts numbers on what many luxury operations leaders already sense: 61% of employees who use AI at work do so through personal accounts at least once a week, outside any corporate framework, and 71% of non-executive managers have received no AI training at all.

Transpose that to a luxury house preparing DPPs. The teams describing products, verifying supplier declarations and answering client questions are already using generative AI, informally and invisibly. Untrained usage on unmanaged accounts is exactly the wrong setup for an industry whose entire value proposition is trust, confidentiality and provenance.

Where AI Actually Helps a DPP Workflow

Working alongside brands and technical teams in the Origin Labs ecosystem, which supports the Galileo Protocol project, we see four places where AI-literate teams change the economics of a passport programme:

1. Data quality at the source. Large language models are remarkably good at spotting inconsistencies between a supplier certificate, a material declaration and a product sheet, before that inconsistency is sealed into a passport. A trained operator with the right prompts catches in minutes what an audit would catch in months.

2. Authentication operations. Fraud patterns evolve faster than manual review guidelines. Teams that know how to use AI to summarise case files, compare authentication events and draft escalation reports process more volume without diluting judgement. The judgement itself stays human; that is the entire point of training.

3. Client-facing storytelling. A passport is also a narrative surface: craftsmanship, materials, repair history. Client advisors who master AI drafting tools turn raw traceability data into the kind of story a collector actually wants to read, in the brand's voice, in any language.

4. Compliance documentation. ESPR, GDPR and the EU AI Act each come with documentation duties. AI-assisted drafting, with human validation, is the difference between a compliance team that scales and one that drowns.

None of this requires a data science department. It requires structured training on real cases, clear rules about what data may enter which tool, and a governance framework the whole team understands.

What "AI-Ready" Looks Like in Practice

The pattern that works is consistent across industries, and luxury is no exception:

• Start from the workflow, not the tool. Train the after-sales team on their repair documentation, not on generic chatbot demos.
• Put data rules before prompts. Which product data, client data and supplier data may be used with which AI service is a governance decision, taken before the first workshop, and the EU's regulators expect user training as part of any serious deployment.
• Measure adoption at 30 and 90 days. A training that does not change how the passport data is produced was a presentation, not a training.

France concentrates a significant share of the world's luxury production and craftsmanship, which makes the training question concrete rather than theoretical: ateliers, after-sales teams and client advisors need programmes built on their own workflows and data rules, delivered where they work. That is the model behind in-company AI training programmes as practised in the French market: per-role curricula, real cases, and data governance taught as part of the skill rather than as an afterthought. The same philosophy that drives Galileo Protocol, open standards, verifiable claims and human craftsmanship at the centre, applies to how teams should meet AI.

One Ecosystem, Three Kinds of Proof

This conviction did not appear in a vacuum. Origin Labs contributes technical assistance and development support to Galileo Protocol, alongside project steward Galileo Network, and much of that engineering is done with AI-assisted development practices: the productivity behind several of the protocol's features owes a great deal to them. Origin Labs also backs Origin Education, a certified training centre that teaches exactly those practices, AI automation and AI-assisted software engineering, to companies. Galileo Protocol is thus one of three projects in the same ecosystem that all answer the same question, "how do you make a claim verifiable?", in three different domains:

• Proof of product. Galileo Protocol gives luxury goods an open, interoperable passport: provenance and craftsmanship as verifiable claims rather than marketing statements.
• Proof of compliance. AeroCert applies the same logic to aviation and battery compliance records, issuing and verifying them with zero-knowledge privacy, so that a regulator can check a claim without exposing the underlying data.
• Proof of skill. Origin Education closes the loop on the human side: every trainee leaves with an assessed, certificate-backed validation of what they can actually do, from prompt engineering to AI-assisted software development, the "vibe coding" practice used to build these very products.

Three domains, one design principle: a claim that cannot be verified is a liability, whether it is stitched into a handbag, filed with a regulator or written on a CV. Teams trained to work with AI are what keep all three kinds of proof honest, because every passport, record and certificate begins as data produced by a person.

Standards for Products, Skills for People

The luxury industry is converging on a simple truth: authenticity infrastructure and workforce capability are two halves of the same transition. A perfect passport filled by an untrained team will carry perfect-looking errors. A trained team without an open standard will produce beautiful data silos.

Galileo Protocol addresses the second problem in the open. The first one is solved maison by maison, team by team, and it starts earlier than most transformation roadmaps assume: not with the technology choice, but with the people who will use it every day.

Pierre Beunardeau is co-founder of Origin Labs, which provides technical support to the Galileo Protocol project, and of Origin Education, a Qualiopi-certified corporate AI training organisation based in France.