By 2030, it’s estimated that the US buyer-to-consumer retail market could see up to $1 trillion in orchestrated revenue from agentic commerce. On a global scale, that projection grows to $5 trillion. 

In a practical sense, while fully autonomous purchasing agents aren’t yet commonplace, agentic commerce already is shaping how buyers discover businesses and how brands market themselves to shoppers. A brand’s identity now has to be conveyed through the language of AI agents: data.

When algorithms curate, recommend and even purchase on behalf of customers, product data in turn becomes a business’s brand voice, storefront, and best salesperson. AI agents don’t “feel” aesthetics or get goosebumps from an Instagram campaign. They read attributes, parse through third-party certifications, and analyze material lists. If data is inconsistent, generic, or simply not available, a brand can become invisible to consumers. 

A study from my firm shows that two-fifths of shoppers say they’re willing to pay more for a product when a company clearly communicates its values. Therefore, it’s crucial to ensure complete, consistent and enriched product information is readily available online. Brand identity is changing in the blink of an eye. As companies prepare for the new agentic commerce playbook, the secret to success lies within product data. 

The power of product data

Already 45% of consumers use AI for at least part of the buying journey. As shopping queries are posed, agents rely on strong contextual information to differentiate products beyond just price. Shoppers want to see information on sustainability, product reviews and even company values. For agents to surface details that align with what a shopper cares about, it needs rich, contextual information. This can identify who a product is for, why it’s relevant to them and what makes it different from other products. Providing all of this information at a second’s notice requires a central system that every agent, no matter the model, can rely on. It should house data validation rules and version history that agents can comb through to understand information. 

The right information can make or break a sale. Two-thirds of global shoppers say they’ve abandoned a purchase entirely because product information was missing or incorrect. More purchasing decisions are being made with the support of agent recommendations and clean product data has the power to drive sales. Not only does product data provide shoppers with the information they need, but it can also enhance personalization – and boost sales.

The product information one shopper cares about could be dramatically different from another, requiring agents to personalize results. Should an agent know a shopper has a trip soon, it may surface brands that detail quick shipping. Another shopper, however, may solely buy from companies with in-store return options. Personalization requires troves of organized product information. When done right, companies can generate 40% more revenue from those personalization initiatives than their peers. 

It’s no question that agentic commerce is disrupting the world of retail. Agents are now evaluating thousands of stock keeping units in seconds to respond to shopping queries. Updated product descriptions and specifications enhance a product’s discoverability while also helping brands continuously showcase who they are in front of prospective buyers. 

In a retail market that’s increasingly shaped by agentic commerce, brands can no longer market just to shoppers. Instead, companies must begin to optimize for the AI agents influencing discovery. Without it, brands risk losing their identity altogether. 

When data is missing

While product data is the lifeblood of retailers, it is far from easy to manage. 45% of small businesses still rely on manual processes, like spreadsheets, to ensure data accuracy. This can lead to outdated product descriptions and missing specifications. 

Whether product data is incomplete or just stuck in siloes, it can harm discoverability, sales and brand identity (all before AI is included in the mix). Between 2023 and 2025, the number of customers unhappy with product data accuracy rose from 13% to 30% – with 40% of shoppers having admitted to returning an item due to incorrect product information. 

Even worse, should product data not be available, it could signal to shoppers that a brand doesn’t care enough about the information to include it. This can impact how a brand is perceived. For instance, an Akeneo survey showed that nearly half of respondents say they are less likely to buy or more likely to switch brands when sustainability details are incomplete. 

The trickle-down impact of missing product information only compounds with AI experiences. Without all the information needed, agents will default to showing shoppers competitors who have clearer signals. This means agents may recommend a competitor in response to a shopper’s search. Or, worse, not include the product at all. If specifications are missing, AI can’t accurately compare products. Missing data now equals missing sales. 

Brand identity as a cross-organizational effort

Over the years, it’s become common for marketing teams to own all things brand identity. Social media teams own showcasing a brand’s image through photos and voice. Creative owns logos and color palettes defining the personality of a brand. So on and so forth, it has become all too frequent that brand identity is siloed to a marketing team effort. 

As agentic AI commerce grows in popularity, brand identity is shifting to a cross-departmental priority. 

Just as brand identity can’t live solely in marketing, product data can’t be siloed to a back-office line item. Product information now shapes discovery, conversion, customer service and even in-store experiences. Fundamentally, this is changing roles across organizations. For example, while customer reviews once lived within the Customer Experience teams’ role, it’s now a necessary element of what agents read to compare products. Similarly, while IT still plays a critical role in architecture, ownership of product content increasingly sits at the intersection of digital commerce and merchandising, where customer impact is felt most directly. 

As organizations adjust to agentic commerce, it’s important to understand how agents are already representing a brand and what work still needs to be done. Teams should run sample queries relevant to the brand through different LLMs, such as ChatGPT or Perplexity. 

Next, determine how different products are showcased and ensure everything from products to attributes to value propositions is accurate.

From there, regularly review and update the structure of data files to ensure compatibility with evolving AI technologies. This includes revising data schemas, updating table formats and ensuring all data fields are accurately and consistently captured to enhance AI search results. 

What’s next

As 2026 continues to ramp up, digital marketing teams have allocated 48% of their budgets towards showing up in AI search. Product information is playing an increasingly important role in how brands show up and communicate their identity across channels. It’s critical for organizations to centralize product data and align every team around a single source of truth to fuel profitability and brand recognition. 

As AI search continues to evolve, the teams that prioritize clean data will have a greater chance of differentiating themselves to both agents and customers. Should agentic commerce one day dominate the retail buying playing field, a brand identity that can shine through data will help retailers stand the test of time.