Why One-Size-Fits-All AIO Won’t Work for AI Discovery

Companies selling into retail are quickly learning that artificial intelligence discovery is not just another version of search. Retail business buyers are no longer simply scanning a list of links. Increasingly, they're asking AI tools to compare options, summarize categories, explain tradeoffs, and recommend what to consider next.

This shift is already visible in buyer behavior data. In a presentation by Hunter & Bard CEO Shira Abel, “The ABM Marketing Funnel Became a Martini Pipeline,” she highlights that 61 percent of buyers get their short list from AI, and 95 percent ultimately purchase from that AI-generated short list.

That shift has created a rush toward AI optimization, or AIO. The common advice is familiar: structure content clearly, answer common questions, update pages, add schema, publish FAQs, and make brand information easier for AI systems to interpret.

Those steps matter. However, they're no longer enough.

The next phase of AI search strategy will not be defined by one universal checklist. It will be defined by whether brands can prove credibility in the specific ways their buyers expect. In retail, that distinction matters because technology and service decisions vary dramatically across functions. A chief information security officer evaluating fraud prevention software, an e-commerce leader comparing enterprise software platforms, and an operations executive researching energy management systems are not looking for the same proof points.

Recent 10Fold research found that only 4 percent of B2B technology marketers say their content is fully adapted for AI search. More importantly, the data shows that AI search readiness is splintering by vertical. Cybersecurity marketers are highly focused on governance and trust. AppDev and DevOps marketers are more concerned about factual accuracy. Energy technology marketers are focused on citations, accuracy and measurement.

Every Category Has a Different Trust Burden

The lesson for retail technology providers is clear: AI visibility depends on more than being machine-readable. It depends on being credible in the context of the decision a retail buyer is making.

For vendors selling into retailers, AIO should start with buyer expectations, not generic tactics. A cybersecurity provider must show strong governance, third-party validation, comparison content and clear claims about risk reduction. An enterprise software provider must prioritize technical precision, integration details, customer outcomes and expert review. An energy technology company serving retailers must back up claims with citations, reliability data, compliance context, and return on investment proof.

In each case, the content structure may look similar on the surface. The credibility signals should not.

That distinction matters because retail buying committees are evaluating very different kinds of risk. The information a CISO needs to trust a fraud prevention vendor is not the same information an operations leader needs to trust an energy management provider. Each category requires a different evidence base.

The Next AI Advantage is Proof

Companies selling into retail can begin by asking three questions:

1. What does the buyer need to trust before they move forward? For some categories, that may be customer proof. For others, it may be technical accuracy, regulatory clarity, analyst validation or independent media coverage.
2. Where will AI systems look to verify that trust? Owned content is important, but it's only one part of the information environment. Reviews, media coverage, expert commentary, industry reports, customer stories, partner ecosystems and social conversations can all reinforce or weaken the story AI tools summarize.
3. Is the brand’s evidence consistent across those sources? AI systems are built to synthesize. If product pages, articles, reviews and third-party references tell different stories, the brand becomes harder to interpret and easier to overlook.

This is where many organizations need to rethink AI discovery. The goal is not to flood the market with more content. AI has already made content easier to produce, which means volume alone will be a weaker differentiator. The goal is to build a stronger evidence base around the claims that matter most in a specific category.

Companies selling into retail should audit their priority solutions, services and buyer journeys through this lens. Which claims are most important to retail business buyers? Which proof points support them? Which third-party sources reinforce them? Which content is outdated, vague or unsupported? Which questions are decision-makers at retail organizations asking that the brand doesn't yet answer with enough authority?

AI search is changing how brands are discovered, but it's also raising the standard for credibility. The winners will not be the companies that apply the same AIO checklist to every category. They will be the companies selling to retailers that understand what trust looks like in their market and make that trust easier for both people and AI systems to find.

Susan Thomas is the founder and CEO of 10Fold, a communications agency focused exclusively on B2B technology.