When Allbirds, Inc. announced in April that it would exit its footwear business and reemerge as “NewBird AI,” an aspiring player in the AI compute infrastructure space, the reaction was pretty predictable: equal parts intrigue, confusion, and skepticism. Writing for Reuters, tech columnist Robert Cyran said the company “lacks footing for [a] dramatic AI pivot,” pointing to its unclear place in an already crowded, capital-intensive market.
The skepticism extends beyond Allbirds’ ability to execute. “AI” has become closely associated with future growth and valuation in certain segments of the market, giving the term strategic weight well beyond its technical meaning. Companies are using it to describe their products, operations, and broader business strategies, putting more attention on what those claims actually mean.
Enter: AI Washing
The proliferation of retail companies touting themselves as AI-forward may be new, but companies have faced similar questions over sustainability claims and “innovation” narratives. Those narratives sometimes ran ahead of what businesses could substantiate, eventually prompting regulatory enforcement actions and consumer-initiated litigation over how the claims were framed.
AI is following a similar path. Across the retail sector, brands invoke AI to describe everything from personalization and inventory management to customer service and marketing. Some of those references reflect meaningful deployment of machine learning tools. Others repackage automation, analytics, or rules-based workflows using terminology that may suggest capabilities the underlying systems do not have. That gap can create legal exposure under consumer protection and securities laws.
The practice has become known as “AI washing.” At its core, this involves companies overstating their AI capabilities, use of AI, or the technological or business impacts of AI. U.S. authorities, including the Securities and Exchange Commission, have made clear that AI-related representations fall within existing disclosure and anti-fraud frameworks. And there is no separate standard for emerging technology. So, claims about AI – whether in investor materials or consumer-facing marketing – must be accurate, supportable, and not misleading.
Where AI Meets Accountability
AI claims now regularly appear in earnings calls, investor decks, and brand messaging, often as shorthand for efficiency, scalability, and future growth, and that creates a basic substantiation question. If a company says that AI is reducing costs, improving margins, automating a process, or changing the economics of its business, what supports the claim?
The answer can become complicated inside retail organizations. AI initiatives may sit across product, logistics, marketing, and other functions, while external messaging packages those projects into a single company-wide narrative. An “AI-powered” business may in practice rely on a combination of AI tools, conventional software, and human decision-making. For public companies, the stakes can extend beyond marketing. Statements about AI may bear directly on expected costs, margins, growth, and other factors investors use to assess a company. The SEC’s focus on AI-related disclosures reflects its application of existing securities rules to claims about the technology.
Consumer-facing claims present their own questions. Terms such as “AI-powered” and “AI-driven” can cover a wide range of technologies and levels of human involvement. The broader the claim, the more important the underlying support becomes. For retail companies, that makes the wording important. What technology is actually being used? What does it do? How much of the process is automated? And can the company support the benefits it attributes to AI?
THE BOTTOM LINE: “AI-forward” may still function as useful positioning in the market. But when companies use AI to make claims about their products, operations, or financial prospects, those claims need support.
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