Do this well, and your input can make its way into vendor evaluation factors, requirements, specifications and, in more formal buying processes, the buyer’s request for proposal (RFP) itself.

That’s always been a powerful sales advantage. If you help establish the vendor selection criteria, your competitors may eventually find themselves responding to an evaluation framework you helped create.

But what happens when AI gets there first?

AI Revenue Institute (AIRI)’s survey of 521 B2B buyers suggests that’s already happening. Buyers aren’t just using AI to find vendors. They’re using it to figure out what they should be looking for in the first place.

AIRI’s research found: 56 percent of B2B buyers surveyed use AI to fill knowledge gaps about what they should look for when selecting a provider.

One survey respondent described the reason plainly: “I use AI to find out what details are the most important in a product so that helps me narrow down options.”

There’s an important difference between asking AI, “Which vendors should I consider?” and asking, “What should I look for when choosing a vendor?” The first question helps build the vendor list, and the second influences how everyone on that list will be judged.

In practice, B2B buyers rarely rely on sellers alone to figure out what they should look for in a solution. They draw on their own experience and a broad information ecosystem that includes vendor content, analyst research, consultants, peers, industry publications, third-party reviews, webinars, events and other sources.

According to research from B2B sales and marketing software company 6sense, most buyers use a range of resources during the buying journey, including:

AI doesn’t replace those sources, but it can compress the work required to make sense of them. Instead of manually collecting, reviewing and collating information across multiple sources, buyers can ask AI to pull all the pieces together for them quickly and efficiently. This can potentially happen in a single chat session, depending on the complexity of the solution they’re planning to buy.

Not surprisingly, speed is one reason B2B buyers are turning to AI to assist them with purchasing. Indeed, according to AIRI’s B2B AI buying behavior survey: 66 percent of B2B buyers said saving time aggregating information that would take hours or days to gather manually influenced their use of AI in purchasing.

This answer hints at the other reason B2B buyers are turning to AI to help them set evaluation criteria for products and services. AI not only aggregates but also synthesizes and analyzes what can be voluminous information from many sources to (hopefully) surface the most relevant insights for buyers.

For sellers, buyers’ use of AI can shrink their window for influencing buying criteria before they harden into requirements. It also raises the risk that the vendor’s differentiators never make it into buyers’ evaluations at all.

As much as it pains sellers, buyers decide when they want to talk to sales. And that seems to be happening later and later in the process. According to the 6sense survey data, in more than 80 percent of purchases, buying teams have already settled on their purchase requirements and a preferred vendor before ever speaking to sellers.

In addition, research from Gartner found that buyers continue to show a strong preference for low-friction, self-directed buying experiences. Sixty-seven percent of buyers prefer a sales-rep-free experience, while 70 percent prefer a completely digital, self-service buying experience.

Current buyer self-service propensities don’t mean you can no longer influence buying criteria. But where and how that influence happens is evolving. As noted, it’s happening before sellers enter the conversation, not only through the information buyers find as they educate themselves, but increasingly through the sources AI draws on to answer buyers’ questions about your category.

AI in the buying loop changes things. Your broader go-to-market motion has to help shape the information ecosystem buyers and AI tap into to understand your category, including which capabilities deserve attention, how they’re framed and what evidence supports them. An important first step is to see how AI is interpreting that information today.

When buyers ask AI what they should look for in your category, you need to understand the answers they’re likely to get. That’s where an AI response audit can help.

A valid AI response audit goes beyond running a handful of prompts through ChatGPT. Done properly, an AI response audit systematically tests the questions buyers are likely to ask across multiple AI platforms and repeated sessions in a defined time period using neutral accounts. This requires methodical research, planning and testing.

Ask questions your prospects might ask AI about your category based on AIRI’s experience and research, such as:

Review the answers for patterns:

Then, compare those answers with how you position and sell your offering.

That’s just the first step. You will also need to do another audit with questions about your company and competitors.

Once you’ve gathered all the data, the real work begins.

After doing all of that research, you need to create and execute a plan to shore up your proof points, or “revenue signals” as we call them at AIRI, both on your website and off-site on third-party sites. Don’t dump this job solely on your SEO team; it requires a companywide effort.

For example:

To be clear, the goal isn’t to stuff the web with content intended to manipulate an AI response, but to make sure there is credible evidence in the market for the value that your company and your solutions deliver to buyers.

Once you’ve made changes, run the audit again. Look for movement in the criteria AI surfaces, the sources it relies on and the way it describes your category and your company. AI responses change as models, sources and information evolve, so this shouldn’t be considered a one-and-done exercise.

Most importantly, don’t think of this as only an AI search optimization project. It’s a criteria-influence strategy. The work spans positioning, content, reputation, customer evidence, analyst relations, PR and sales enablement because all of those functions contribute signals that can shape how buyers understand the category.

You may have fewer opportunities to shape buying criteria through direct sales conversations, but by taking steps to strengthen your revenue signals, you can still influence what buyers look for in your space.