As generative AI reshapes commerce, securing product data is becoming as critical as protecting networks. Clean, governed information will be the foundation of digital trust, says Justin Thomas, VP Sales EMEA North, Akeneo.
As cybersecurity stories dominate today’s tech headlines, there’s another data-driven issue that deserves equal attention: product information. As generative AI reshapes how consumers discover, evaluate and buy products, the integrity of product data is becoming just as critical as network firewalls or endpoint protection. In the age of misinformation, the ability of businesses to ensure clean, secure and trustworthy product information across all distribution channels, including digital, will determine whether customers buy with confidence or turn elsewhere.
We understand well enough that while consumers have unprecedented access to choice, data and personalised recommendations, they are increasingly sceptical of what they see online. The rise of deepfakes, manipulated reviews and AI-generated misinformation has made trust the most valuable as well as fragile currency in digital commerce.
Nowhere is this more visible than in the way people shop. More consumers are turning to conversational interfaces including chatbots, generative AI assistants and voice search to inform their buying decisions. Instead of scrolling through pages of results, they expect an AI agent to do the heavy lifting and present the right recommendation instantly.
But the catch is that AI systems are only as good as the data they are trained on. If product information is incomplete, inconsistent or misleading, AI amplifies those flaws at scale. The result is poor recommendations, eroded consumer confidence and lost sales.
This is where product information management (PIM) systems become important. These platforms act as central hubs for collecting, enriching and distributing product data across all distribution channels, including ecommerce sites, marketplaces, mobile apps and now AI-driven interfaces.
As product content now flows through so many channels, governance and access control are more essential than ever. Secure PIM platforms enforce data governance policies, control user access and ensure compliance with evolving regulations. They create a chain of trust that extends from manufacturers/suppliers to retailers to consumers.
When product information is centralised and governed properly, businesses can be confident that what reaches the customer, whether through a product page, a chatbot response or a digital product passport, is accurate, secure and consistent.
The current wave of generative AI has thrown this issue into sharper relief. On the one hand, AI tools can take structured product data and automatically generate useful descriptions, localised content or safety warnings, and so dramatically reducing manual effort. On the other hand, without guardrails, AI can just as easily fabricate details, misinterpret specifications or introduce bias.
The answer is not to avoid AI but to constrain it. When AI models are supplied with clean, well-structured product data, the outputs become more reliable and valuable. For example, using PIM as the foundation, generative AI can enrich catalogues at speed, create consistent multilingual experiences or provide personalised recommendations, all while reducing the risk of false information or hallucination.
Retailers experimenting with AI-powered chatbots are learning this lesson fast. The difference between a chatbot that inspires confidence and one that frustrates users often comes down to the quality of the underlying product information. If the AI agent can’t answer a simple question (does this jacket contain recycled materials?) then trust is broken instantly.
Things are made worse by the decline of traditional keyword search. As more consumers rely on conversational AI, search engine optimisation (SEO) is giving way to GenAI optimisation (GEO). Instead of tuning content for search rankings, businesses will need to structure product data so that AI agents can interpret and recommend effectively.
This is a profound change in digital marketing strategy. Product descriptions, attributes and metadata once prioritised for ranking on Google, are now about supporting algorithms that will increasingly mediate consumer choice. In this context, PIM platforms are becoming the backbone of discoverability.
Too often, businesses treat product content as an afterthought. They assume a basic description or a handful of attributes will suffice. In reality, the richness and precision of product information often determine the difference between winning or losing a sale. Two companies may sell the same smartphone, but the one that provides clearer, more detailed and trustworthy information is more likely to capture the customer.
Another mistake is thinking AI is a silver bullet. There won’t be a single killer use case for AI in commerce; instead, there will be many incremental ones, from auto-generating descriptions to supporting sustainability reporting. All these apps are complementary.
The next frontier is what some call Agentic AI, systems capable of taking autonomous actions based on product information. Here, the AI agent not only recommends a product but manages inventory, adjusts pricing or generates compliance documentation in real time.
For such systems to be trusted, they must be grounded in product information under tight governance. Without a reliable foundation, the risk of compounding errors grows exponentially. As commerce becomes more automated, the stakes for data governance rise accordingly.
As the different types of cybersecurity grow, businesses cannot overlook the threat of product misinformation. A mislabelled ingredient or an inaccurate sustainability claim may not trigger a data breach, but it can damage trust just as severely and invite regulatory scrutiny.
The solution is a disciplined approach to product information governance. By investing in specialised tools that centralise, secure and enrich product data, businesses can ensure that every consumer touchpoint, from websites to chatbots and brick-and-mortars, delivers information consumers trust.
In the end, building trust in the digital economy is about ensuring that what customers see, read and hear about a product is accurate, consistent and reliable.
