Big SaaS providers have jumped headfirst onto the generative AI (Gen AI) bandwagon. Marc Benioff, CEO of Salesforce, is perhaps the most vocal advocate among industry leaders. He argues that Gen AI holds the promise of growth without the pain of costly acquisitions. He’s not alone, many believe customer support will reap the biggest rewards. Established players like ServiceNow and SAP are promoting their Gen AI capabilities, while new entrants such as Bret Taylor’s Sierra are making bold moves of their own. Beyond customer support, optimization projects are spreading across supply chains: demand forecasting, automated reordering, distribution orchestration, and more.

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On the Shopper Experience front, brands have been experimenting with AI for several years. Voice recognition features appeared on Amazon and Walmart apps, but these attempts fizzled out. In contrast, virtual try-on experiences have taken root, pioneered by L’Oréal and now common in the cosmetics industry.

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Now that Gen AI is everywhere, the question arises: Will brands use it to communicate directly with shoppers? They have reasons to be cautious.

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Unknown Content

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Every brand leader should test Gen AI tools like ChatGPT to understand the scope of their content. The breadth is impressive, comprehensive, even, but the sources raise concerns. Reliable and unreliable information often sit side by side. The U.S. perspective dominates. Some useful content comes from retailer FAQs, but many retailers have blocked scraping, creating gaps.

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Because the pool of information is so vast, Gen AI sometimes struggles with consistency and accuracy. Hallucinations creep in. For example, when I asked Microsoft Copilot about my New Balance shoes, it confidently identified them as a gray, fluffy animal!

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Misunderstood Processes

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The quality of Gen AI output depends heavily on prompts. Unfortunately, query frameworks differ across services, and the logic behind responses is opaque. One thing is certain: the results vary.

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At the same time, brands and retailers are racing to provide richer data in hopes of attracting traffic. But the risk is that both are bypassed, as Gen AI providers work to keep users within their own walled gardens.

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Zero Feedback

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Gen AI tools constantly pull in brand data but provide little to no feedback about quality, consistency, or usage. A new discipline, Generative Engine Optimization (GEO), is emerging, but its rules are still unclear. Reporting back to sources is nonexistent.

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On the usage side, the situation is just as murky. Brands don’t know how many queries their names generate or what questions people are asking. Today, Gen AI accounts for 2–3% of web traffic, but it’s growing rapidly.

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What’s Next for Brands?

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It’s too early to say whether brands, retailers, or Gen AI platforms will be the main winners of this shift. But one sensible path for brands is to take control of their own Gen AI experience.

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Solutions like Ask Mona make this possible. The brand decides which content the AI engine should index, which engine to use, and how prompts are structured. It can review results for accuracy and consistency, then share the experience directly with shoppers, via QR codes on packaging or shelves, or a simple “rainbow button” online.

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Most importantly, brands can monitor shopper activity: how many people use it, what they ask, and where gaps exist. This creates a feedback loop that enriches the index and ensures that visitors get a premium experience.

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In short, it’s a safer and smarter strategy than doing nothing and letting foundation models capture the traffic unchecked.