I’ve been thinking a lot about what happens after a company gets recommended by AI. We spend an enormous amount of time measuring visibility, citations, and recommendation share, but what happens when that buyer starts evaluating the product? Does the demo support what ChatGPT told them? Can someone in security verify the claims? Can the economic buyer find enough information to justify the purchase?

I cite this one often but G2’s 2026 Buyer Behavior Report found that 82% of B2B software buyers sourced recommendations from an AI chatbot in the past two years, and evaluation has overtaken research as the longest stage of the buying journey. Buyers, and increasingly their agents, are building shortlists before sales even knows there’s a deal.

Additionally, TrustRadius found that 94% of buyers who used AI to research a purchase fact-checked its answers at least some of the time. The percentage checking very often or always jumped from 58% to 72% in a single year. AI has made discovering and comparing vendors ridiculously easy, but buyers still need to establish whether the products they’re being recommended can actually do what they need.

They’re checking demos, documentation, pricing, reviews, and whatever third-party sources the AI engine cited. And the stakes get higher as the list gets shorter. TrustRadius found that 83% of buyers shortlisted three or fewer products. G2 found that IT security reviews were the biggest source of delays after vendor selection, cited by 39% of buyers and 50% of enterprise buyers. Nearly half (49%) said a CFO had reversed a purchase their buying team had already approved in the previous year.

A lot of that validation happens without your sales team in the room. Your champion is explaining the product to security, someone in finance is questioning the pricing, and a technical evaluator is trying to figure out whether an integration actually works. All of them need evidence, and what they find needs to line up with what AI originally told them.

I've been spending more and more time with the team at Consensus because I think they're particularly well-positioned for where enterprise buying is headed. As AI takes on more of the discovery process, the demo becomes one of the most important places buyers go to validate what they've been told…and increasingly, that demo needs to work for an entire buying committee, not just the person who originally requested it. This is exactly how sales-led businesses with complex or multi-persona buyers need to be thinking. I lived this first hand as we moved Webflow upmarket and the CMS purchase became more complex, with more buyers, caring about different things.

Your recommendation share might be climbing while buyers are requesting additional validation calls, questioning capabilities, or getting stuck during technical evaluation. These aren’t necessarily caused by AI…but they’re worth investigating alongside visibility gains. I’d want to understand whether the product claims driving those recommendations are creating problems later in the buying process.

Your champion might already be convinced, but security, finance, and the person holding the budget still have their own questions. They’re evaluating different aspects of the same purchase, often using completely different sources of information.

Some of the most important people in a buying decision may never interact directly with your sales team. They might see the product for the first time because someone forwarded them a demo.

Consensus’s 2026 B2B Buyer Behavior Report found that stakeholders who discovered a demo through internal sharing engaged at a 61% rate, compared with 28% for the original recipient. That’s more than 2.2x the engagement from people who weren’t part of the original send.

The person requesting the demo isn’t necessarily the person who’s going to scrutinize it most closely, and that second viewer is arriving with an entirely different set of questions.

Imagine your champion forwards a demo to someone in IT three days after the original conversation. That person has none of the context your rep provided. They don’t particularly care about the high-level workflow that got your champion excited. They want to understand how the product connects to their existing stack, what data gets exchanged, and what administrative controls are available.

If those answers aren’t accessible, someone has to go back to sales, request another meeting, or hunt through documentation. That’s unnecessary friction in a buying process that’s already complicated enough.

Three things I’d prioritize when designing demos for these secondary viewers:

Personalized demo automation can help here. In Consensus, viewers can rate topics based on their importance, and the demo prioritizes what matters to them. It can also ask someone’s role upfront and branch into a more relevant experience.

Going back to the IT example, that viewer could prioritize integrations and admin controls and go directly to those walkthroughs and their supporting documentation, without sitting through the entire workflow tour your champion watched. Meanwhile, your sales team gets visibility into who engaged, what they cared about, and what they skipped.

Of course, this requires having the underlying content to answer those questions. Personalizing a demo doesn’t accomplish much if the evidence someone needs isn’t there.

Consensus also records what people watched, skipped, rewatched, and forwarded across video demos and click-through product tours. Those signals can make sales follow-up considerably more relevant:

We’re also seeing more vendors introduce AI agents into demos to answer questions in real time. I’m bullish on the direction, although accuracy needs to be a prerequisite. Every answer a demo agent gives becomes another claim a buyer can independently verify.

For enterprise deals, I’d evaluate demo platforms on whether different members of the buying committee can access the evidence relevant to them, and whether the demo still makes sense when shared internally.

Consensus is built around personalized demo experiences and visibility into how those experiences spread throughout an account. If you want to see what that looks like from the buyer’s side, take a personalized Consensus demo.

Quick note: I’m going hard on Consensus here because I genuinely think the tech is that good, not just because they’re a sponsor. I say no to far more sponsors than I say yes to. I need to believe in the product, the team, and that what they’re building aligns with where marketing is headed. Consensus checks those boxes for me. They’re solving a critical piece of the data collection puzzle, and the behavioral signals they capture create some pretty incredible opportunities for next-best-action modeling.

A demo is one place buyers test the claims AI made about your product. The same scrutiny applies to every other source they encounter during evaluation.

A lot of AEO reporting focuses on whether a company gets mentioned, cited, or recommended. Those are important metrics, but they don’t tell you whether AI is accurately representing the product.

That means looking at which capabilities it claims you have, how it describes your integrations, pricing, security, and competitive differentiation, and whether someone can independently verify those claims.

You can have fantastic recommendation share while the underlying product narrative has some pretty significant holes.

When comparing AI answers against verified product information and the evidence available to buyers, I look for three types of gaps.

Prioritize confirmed contradictions first. If a buyer discovers that your product doesn’t support something an AI engine confidently recommended you for, you’ve created a credibility problem that can extend beyond that one capability.

After that, prioritize the remaining gaps based on their potential impact on deals.

Security, pricing, and integrations are where I'd initially focus. G2's findings around security reviews and CFO reversals make the first two obvious priorities, particularly for enterprise software. And integration claims are particularly vulnerable to scrutiny because technical evaluators will eventually discover exactly what works, what doesn't, and where the limitations are.

The same integration claim can land in any of these three gaps, depending on what the product actually supports and what evidence a buyer can access.

A buyer asks ChatGPT which tools in your category integrate with Salesforce, and your company gets recommended.

Great. But what does “integrates with Salesforce” mean?

Start by confirming the claim with your product team: whether the integration exists, whether it’s native or partner-built, whether it’s actively maintained, and what data moves between the systems, and in which direction.

Until you’ve confirmed those details, the AI claim is unsubstantiated. The integration might be real, partially supported, or something a partner built three years ago that nobody has touched since.

There’s a big difference between a comprehensive Salesforce integration and a one-way sync of three fields. Your technical evaluator is going to figure that out eventually, so you might as well make it clear upfront.

Now imagine the integration does exist, but your demo never shows it and the only documentation is buried in a help center behind a login. AI may have gotten the recommendation completely right, but the buyer still can’t independently validate it.

That’s inaccessible proof. Show the integration in your demo and publish an accessible page explaining what syncs, in which direction, and what the known limitations are.

Finally, imagine the integration doesn’t exist at all.

Now you’ve got a contradiction that can create real problems during a trial or technical evaluation. Maybe the engine picked up an outdated partner listing, an incorrect review profile, or documentation describing a feature that was discontinued.

Trace the claim back to its source and work to correct it, whether that’s a publisher, review site, partner directory, integration listing, or your own documentation. Make sure your company clearly explains what it does support and monitor whether AI answers improve over time.

Correcting a source doesn’t guarantee the answer will change immediately. AI engines can continue surfacing outdated information, which is why ongoing monitoring matters.

Start with the claims your sales team uses to beat competitors. Those are the claims that matter when deals are on the line, and buyers need to be able to verify them.

Once a month, put your product through a structured evaluation to understand what AI is saying and how well that information holds up when someone investigates it.

1. Start with 20–30 actual buying questions.

Pull questions from sales calls, security reviews, customer support conversations, and lost deals. Write them the way a buyer would naturally ask them, such as “best demo automation tool for a 50-person sales team using Salesforce.”

2. Run those questions through the main AI engines.

Test each question multiple times, on different days, across the engines your audience actually uses. Include natural follow-ups such as “Which of these vendors has SOC 2?” or “Which supports bidirectional Salesforce syncing?”

AI answers vary between runs, so don’t make decisions based on a single response. You want to understand which claims and recommendations consistently appear.

3. Log the claims made about your company.

Capture what the engine says, which competitors it mentions, and what sources it cites. Pay particular attention to how the product is being characterized around integrations, security, pricing, and differentiating capabilities.

4. Verify the claims against the actual product.

Check each claim against confirmed product information, then compare it with what a buyer can independently access through the demo, website, pricing page, security documentation, and customer references.

A claim can be accurate and still be nearly impossible for someone outside the company to verify. Both conditions matter.

This is also where demo engagement data earns its keep. If Consensus shows viewers consistently skipping the section that’s supposed to prove a specific claim, or leaving right before they reach it, that’s inaccessible proof showing up in the numbers, not just in theory.

5. Classify the gaps and assign ownership.

Sort issues into unsubstantiated claims, inaccessible proof, and contradictions. Once you’ve investigated an unsubstantiated claim, determine whether it’s accurate, incorrect, or missing accessible evidence.

Identify where each fix needs to happen. That might mean updating documentation, correcting a partner listing, working with a publisher, improving a review profile, or building a new section of the demo.

Give every issue an owner. Otherwise, you’ve just created another spreadsheet full of findings that nobody acts on.

6. Test the buying process through an AI agent.

Give an AI agent a realistic buying task with a requirements list and three to five potential vendors. Ask it to determine which products satisfy the security, integration, and other requirements, without directing it toward your best documentation.

Then evaluate what it finds using four classifications:

The distinction between “not found” and “blocked” matters because the fixes are different. If something can’t be found, you may need to make the evidence easier to discover or publish it. If it’s blocked, you need to decide what can reasonably be made accessible.

G2 found that 61% of buyers use or plan to use AI agents in software buying, with building shortlists among the leading use cases at 51%.

An agent can only evaluate the evidence it can access. If confirming a basic product capability requires scheduling a sales call, you’ve introduced friction into a process buyers are increasingly trying to complete independently.

That doesn’t mean companies should publish sensitive security documents on the open internet. A publicly accessible security summary covering certifications, data storage, subprocessors, and how to request restricted documentation is a reasonable starting point.

For the audit itself, keep one row per claim: the buying question, engine, date, vendors recommended, what AI said about your product, the cited source, where the evidence lives, the gap classification, required fix, and owner.

Take ten evaluation questions your buyers regularly ask, run them through the major AI engines, and compare the answers with what your demo, pricing page, integration documentation, and security resources actually establish. Prioritize contradictions, then look for accurate claims that are difficult for someone outside the company to verify.

Bring those findings into conversations with sales and product marketing. Look at the objections coming up in active deals, the information buyers repeatedly ask for, and where approvals slow down. You won’t establish causation overnight, but you can start identifying where AI-generated expectations and the actual buying experience aren’t lining up.

If you’re investing in AEO, look beyond whether your company gets recommended. Look at what AI is actually promising buyers, whether your product can back it up, and how easily someone can verify those claims. I’d be looking at that alongside recommendation share, particularly for the buying questions that have the most influence on pipeline.