As artificial intelligence (AI) becomes a primary interface between buyers and brands, the rules of digital commerce are undergoing a profound shift. Instead of entering keywords into search bars, shoppers are asking conversational agents for queries such as ‘the best hiking boots under £150’ or ‘the top rated laptops for hybrid work.’ These interactions bypass traditional search entirely and rely on AI systems to interpret needs, curate options and make recommendations.
Recent industry analysis shows how rapidly this shift is advancing. AI-generated search results are reducing click-through rates from traditional search listings by up to 64%, and around 60% of searches end without a single site visit as conversational results satisfy the user’s intent. In B2B buying, nearly 30% of decision makers now begin their research on AI platforms rather than on conventional search engines. According to SEMrush projections, AI-driven search will overtake traditional search engines by 2028.
For technology leaders, this creates a new reality: product visibility and profitability increasingly depend on the quality and completeness of the data that fuels AI. When information is fragmented, inconsistent or lacks governance, brands risk becoming invisible in the very conversations where purchase decisions are now being made.
Data governance as the new currency of trust
In an age defined by misinformation, trust begins with structure and accountability. AI systems can only reason with the data they are given. If product information is spread across spreadsheets, ERP fields, legacy catalogues or inconsistent sources, recommendation engines will produce unreliable, incomplete or misleading results.
This challenge is reflected in consumer sentiment. Research across global markets shows that only 45% of consumers trust AI to provide accurate product recommendations. Shoppers are aware of the risks of incomplete or hallucinated AI outputs, and the perceived credibility of the brand is directly tied to the data it supplies.
Strong governance provides the foundation for rebuilding that trust. It ensures that product descriptions, attributes, specifications and regulatory information are verified, enriched and traceable to a reliable source. Governance introduces clear ownership, version control, approval workflows and auditability. It allows organisations to understand where information originated, who updated it, and whether it meets quality and compliance thresholds.
Crucially, governance positions IT not just as a service function but as a protector of brand integrity. In a landscape where AI increasingly speaks for the business, governance is what ensures that the business is being represented truthfully, consistently and responsibly.
The rise of the product information backbone
As AI-driven commerce becomes mainstream, centralised product information infrastructure has become essential. Traditional enterprise systems were designed for operational efficiency, not for supplying the contextual richness required by modern recommendation engines or conversational interfaces. They struggle with the volume, complexity and variability of data that emerging AI models demand.
A dedicated product information backbone provides a single source of truth that centralises, enriches and distributes structured product information. It supports the depth of attributes, contextual details, regional variations and compliance metadata required by intelligent systems.
This approach ensures that data can be accessed in real time by downstream channels, marketplaces, mobile apps, partner systems and AI agents. In the era of intent-driven commerce, where product discovery is conversational and dynamic, data quality becomes a direct driver of revenue opportunities. If product information is not accurate, complete and trustworthy, sales will not be made, regardless of the strength of the brand.
The new priorities for IT
For technology leaders, the value of modern product information infrastructure lies in its ability to address three core priorities: integration, governance and scalability.
AI-driven commerce depends on interoperability. Intelligent agents require access to product, pricing, inventory and fulfilment data in real time, and they need that information to be consistent across systems.
API-connectivity and event-driven architectures ensure that product information flows seamlessly between ERPs, DAMs, eCommerce platforms and AI tools. This avoids the brittle point-to-point connectors that slow down digital operations. It also reduces complexity, enabling IT teams to support dynamic, personalised buying experiences at scale.
Governance is the first line of defence against misinformation and regulatory risk. Structured workflows enforce quality thresholds before product information is published, ensuring that data sent to AI engines is accurate and compliant.
The importance of this cannot be overstated. According to Gartner, “By 2028, enterprises using AI governance platforms will achieve 30% higher customer trust ratings and 25% better regulatory compliance scores than their competitors.”
Good governance protects against the rapid amplification of incorrect data—a risk that increases dramatically when AI agents are involved.
AI-driven commerce is moving fast. Recommendation engines are already giving way to autonomous buying systems, where AI agents research, evaluate and select products on behalf of the consumer or procurement function.
To compete in this environment, businesses need infrastructure that can evolve at the same pace. A scalable foundation allows organisations to expand into new channels without re-engineering data, localise content for global markets, integrate with emerging AI interfaces and support new product taxonomies or attributes
Scalability ensures that product information can grow with the business and with the rapid acceleration of AI innovation.
Trust as a competitive advantage
AI both accelerates commerce and amplifies the cost of bad data. Inaccurate or incomplete information can spread quickly across generative search results, third-party channels and user interactions, undermining brand credibility.
In this environment, trust becomes a measurable competitive advantage. Organisations that invest in data integrity see improvements not only in compliance but also in conversion. Reliable information is more easily surfaced by AI engines, which reward accuracy and completeness in their ranking mechanisms.
Brands that demonstrate consistency and reliability become the sources AI learns to prioritise. This strengthens discoverability, enhances customer confidence and builds loyalty across the entire buying journey.
This shift is redefining the relationship between IT and commercial teams. Product information is no longer a back-office process; it is directly tied to marketing performance, customer experience and revenue. In the age of agentic commerce, IT leaders are responsible for the business outcomes of the digital interactions they enable.
Building an AI-optimised future
Preparing for the next era of commerce does not require rebuilding entire systems. It requires adopting the principles that make a technology stack ready for AI. Openness – ensuring systems can integrate easily and respond to new interfaces. Integration – creating connected ecosystems where product information flows freely. Scalability – preparing for rapid changes in customer behaviour, channels and technology. And, continuous governance – protecting the business from misinformation, duplication and inconsistency.
Organisations that adopt these principles build resilience and agility in an environment where speed and accuracy define competitive strength. They also position themselves to harness the full potential of AI-driven commerce, rather than being disrupted by it.
The interface of commerce is shifting from search bars to intelligent agents, and from clicks to conversations. IT holds the keys to this transition. The central question now is whether the data supporting those agents is ready to lead the change.
For more insight, download our ebook From Product Data to Profit: How AI Commerce Puts IT on the Hook for Revenue.
