UpTrajectory Review

Forbes contributor Michelle Evans flags a structural shift in how products get discovered: as shoppers increasingly ask AI assistants to compare options and recommend purchases, the old hierarchy of brands is being scrambled. Euromonitor data she cites shows that AI visibility does not track sales rank — a brand can be a category leader in revenue yet nearly invisible when a chatbot answers 'what's the best option for X?' That decoupling is the story. For years, retail success followed a predictable loop: distribution, shelf placement, advertising share, sales. AI intermediation inserts a new gatekeeper between the shopper and the shelf, and that gatekeeper reads the web differently than a human browsing a results page.

For a small-business seller, this is both a threat and an opening. The threat: you can hold solid market share and still lose the recommendation moment if AI models surface competitors with stronger digital footprints — better reviews, more structured product data, clearer comparisons, more third-party mentions. The opening: AI visibility is not bought the way shelf placement was. A niche brand with rich, well-organized, credible content can outrank a giant in a chatbot's answer, because the model rewards specificity and consensus signals, not ad budget. Evans's point, correctly, is that the playbook for discoverability is being rewritten while most operators are still optimizing for Google.

What is genuinely new here is the empirical claim that visibility and sales have diverged — Euromonitor's finding that the brands AI recommends are not simply the best-sellers. That challenges a lazy assumption that market leadership automatically translates into algorithmic endorsement. Where we would urge skepticism: the piece is thin on mechanics. It does not specify which AI surfaces, which categories, or how visibility was measured, and 'AI visibility' as a metric is still young and methodology-sensitive. Sellers should treat the finding as directionally important rather than precisely quantified. Still, the direction aligns with what operators are already seeing anecdotally: referral traffic patterns shifting, comparison queries migrating into chat interfaces, and review ecosystems mattering more.

The second-order effects cut unevenly. Large brands with deep content libraries and PR machines will adapt fastest — they can flood the zone with structured data, comparison pages, and influencer mentions that models ingest. Small brands face a quieter burden: the work of being legible to machines is unglamorous and ongoing, and it competes with the daily demands of running the business. There is also a cost asymmetry in verification: a single bad review cycle or inaccurate listing can propagate across AI answers in ways that are harder to audit and correct than a wrong Google snippet. Downstream, expect agencies and tools to emerge selling 'AI optimization,' some legitimate, some snake oil — the tell will be whether they can point to measurable changes in how models describe your product.

What to do now, without overhauling everything: audit how AI tools currently describe your products by asking the same questions your customers would, across a few major assistants. Note inaccuracies, omissions, and which competitors get recommended instead. Then fix the inputs — complete and consistent product data, genuine customer reviews on platforms models trust, clear differentiation language on your own site, and third-party coverage that gives models something to cite. Watch for Euromonitor and others to publish category-level visibility benchmarks; those will become the new scorecards. The sellers who treat AI legibility as a core operating function, not a marketing afterthought, will be the ones the machine recommends.

“Euromonitor data shows AI visibility doesn't always follow sales.” — Forbes Business

Takeaway: Ask AI assistants how they'd recommend your product, fix the data and reviews they see, and treat machine legibility as a core operating task.