Glossy Research: 3 Out of 4 Brand Leaders Are Using AI for Data Analysis, but ROI on AI Spend Remains Modest

Why It Matters

Widespread AI adoption signals a strategic shift in brand operations, but modest ROI warns executives to balance enthusiasm with disciplined spending. The Allbirds episode illustrates how market perception can amplify AI narratives, influencing valuations across sectors.

Key Takeaways

• 75% of leading fashion/beauty brands use AI for data analysis
• Average ROI on AI spend stays below 10% annually
• Allbirds' AI rebrand triggered an immediate share-price jump
• Brands prioritize cost reduction and personalized experiences via AI
• Modest returns temper further AI budget expansions

Pulse Analysis

AI has become a near‑universal tool among premium consumer brands, with Glossy Research finding that roughly 75% of fashion and beauty leaders now deploy machine‑learning models for demand forecasting, inventory optimization, and consumer insights. This rapid diffusion reflects the technology’s ability to process massive data sets faster than traditional analytics, unlocking incremental efficiencies and more granular personalization. However, the same research notes that the average return on AI spend hovers below 10% annually, suggesting that many initiatives are still in experimental phases or suffer from integration challenges that blunt financial impact.

The Allbirds case provides a vivid illustration of market dynamics around AI branding. After announcing a pivot from footwear to an AI computing infrastructure identity, the company’s shares surged, driven by investor optimism that the brand could capture a slice of the burgeoning AI services market. While the move sparked debate about strategic fit, it also highlighted how the perception of AI capability can translate into immediate valuation effects, even when underlying business fundamentals remain unchanged. Analysts caution that such hype‑driven price moves may be short‑lived if the promised AI offerings fail to materialize.

For executives, the dual reality of high adoption rates and modest ROI calls for a disciplined approach to AI investment. Prioritizing projects with clear cost‑saving or revenue‑generation pathways—such as dynamic pricing engines or targeted marketing campaigns—can improve returns. Simultaneously, firms should invest in data governance, talent acquisition, and scalable infrastructure to move beyond pilot programs. As AI matures, brands that balance ambitious experimentation with rigorous performance tracking are likely to convert early enthusiasm into sustainable competitive advantage.