From Product Data Foundations & Supply Chain to AI-Ready Commerce
Coming out of GS1 Connect 2026 in Las Vegas (June 9–11), one theme ran through every session, hallway conversation, and roundtable discussion. From agentic commerce and digital shelf optimization to 2D barcodes, RFID, supply chain traceability, and AI adoption, conversations ultimately circled back to the same foundational requirement: high-quality product data at scale.
Here are the takeaways:
IS YOUR PRODUCT DATA READY FOR AI?
The most prevalent theme across GS1 Connect wasn’t AI itself. It was the quality of the data powering it.
Organizations repeatedly emphasized that AI doesn’t fix data problems. It exposes and amplifies them. As companies race to deploy AI assistants, predictive models, and agentic commerce experiences, many are discovering that data governance has become a prerequisite rather than an afterthought.
Questions Leaders Should Be Asking:
Who owns product content in our organization?
Are stewardship responsibilities clearly defined?
Do we have executive sponsorship for data governance initiatives?
Is product data managed as a cross-functional capability?
Takeaway
AI magnifies data inconsistencies and governance gaps. Standardized data is becoming a competitive differentiator. And you can’t scale AI initiatives on a weak data foundation. Data quality has shifted from an IT issue to a business issue.
PRODUCT CONTENT HAS BECOME BUSINESS INFRASTRUCTURE
Product content is no longer just about syndication, retailer requirements, or compliance. It now sits at the center of customer experience, supply chain operations, AI discovery, and business growth.
The message from the stage was consistent: product content is not a project you finish. The companies pulling ahead are the ones that have internalized that and are building their operations accordingly.
Questions Leaders Should Be Asking:
Are we viewing product data as a strategic asset or an operational requirement?
Can we quantify the business impact of poor product data?
How does product data influence customer experience, returns, and inventory performance?
Takeaway
Product data drives personalization, discoverability, and consumer trust. The organizations making the biggest strides are treating PIM/PXM as the operational layer that everything else depends on. If your product data strategy isn’t a boardroom conversation yet, it’s time.
AGENTIC COMMERCE IS CHANGING HOW PRODUCTS ARE DISCOVERED
One of the most forward-looking discussions at GS1 Connect centered on how consumers are moving from traditional keyword searches to conversational AI interactions.
61% of consumers now start product discovery in AI assistants before ever visiting a retailer’s website. And instead of short search phrases, they’re asking complex, contextual questions and expecting accurate, trustworthy recommendations in return.
This shift has profound implications for how brands structure and govern their product data.
Questions Leaders Should Be Asking:
Will an AI shopping assistant understand and recommend our products?
Do we have the attributes needed to answer contextual customer questions?
Are our product models structured for machine consumption, not just human consumption?
Takeaway
Rich, structured product attributes determine whether an AI agent understands, trusts, and surfaces your products over a competitor’s. Trustworthy data becomes a competitive advantage when AI agents are making recommendations.
OWNERSHIP MODELS MATTER MORE THAN EVER
A surprisingly consistent conversation throughout GS1 focused on accountability and organizational structure. The consensus was clear: While there is no perfect ownership model, unclear ownership is definitely the wrong one.
The organizations that moved away from assigning product data responsibility solely to IT or business teams are making the most progress. They’re building cross-functional governance models with representation across commercial, merchandising, operations, supply chain, IT, and regulatory teams.
Many are formalizing this through Product Data Centers of Excellence.
Questions Leaders Should Be Asking:
Who owns product content in our organization?
Are stewardship responsibilities clearly defined?
Do we have executive sponsorship for data governance initiatives?
Is product data managed as a cross-functional capability?
Takeaway
Shared accountability consistently outperforms siloed ownership. Clear stewardship models improve scalability, sustainability, and the organization’s ability to move fast without losing control.
THE MOST SUCCESSFUL AI PROGRAMS START WITH PEOPLE, NOT TECHNOLOGY
Carhartt held a session about their AI transformation, which was one of the most grounded conversations of the conference. Rather than leading with technology, Carhartt focused on adoption, trust, education, and measurable business outcomes.
Their story reinforced what the most successful organizations at the conference had in common: AI initiatives are fundamentally change management programs. Treating them otherwise is one of the fastest ways to stall progress.
Questions Leaders Should Be Asking:
Are we building AI capabilities or simply buying AI tools?
Do employees understand how AI helps them do their jobs better?
Have we identified champions who can drive adoption across the organization?
Are we measuring business outcomes rather than technology usage?
Takeaway
Champion-first adoption outperformed platform-first approaches. Executive sponsorship tied directly to business value, monthly workshops, peer-led enablement, and a governance-first culture all contributed to faster, more sustainable adoption. The most successful organizations prioritize outcomes over experimentation for its own sake.
Conclusion
- Taken together, the GS1 Connect themes were clear:
- AI readiness starts with data quality, not technology investment
- Product content has evolved from a compliance function to a core business infrastructure
- Agentic commerce is moving from concept to execution requirement
- Governance and speed are no longer trade-offs but simultaneous requirements
- The most successful AI programs are change management programs first
Summarizing GS1 Connect 2026 in a single sentence, the companies that win in AI-driven commerce won’t necessarily have the best AI. They’ll have the most trusted, standardized, and governed product data.
If any of these topics resonate with you, or if you have questions, reach out to us directly.
We’d love to learn more about your go-to-market approach & product data operations.
