Open an AI search chat window. Type the question your best prospect is typing right now: “What’s the best software in [your category]?”
The three or four names that come up are your real competitive set in 2026. If your brand isn’t among them, you have already lost the deal, and you will never see it in your pipeline, because it was decided before a buyer ever reached your site.
For years, discoverability meant ranking. You earned a position on a results page, the buyer clicked, and your website did the persuading. The machine indexed pages and returned links, and the human did the rest.
That sequence has inverted. Buyers no longer arrive to be persuaded. They arrive informed and equipped with a synthesized view shared by a model they trust more than your homepage.
Today’s winning CMOs focus on owning the answers in AI search, apart from driving traffic to their owned pages. This is the third compression of the buyer journey, and as G2’s Chief Innovation Officer, Tim Sanders, puts it in G2’s The Answer Economy report, “The Yellow Pages compressed the market into the big book. Google compressed it into the first page of results. Now, AI chatbots are compressing it into a single answer.”
Let’s dive into how AI search has changed, backed by G2’s proprietary data and the hard numbers from the field. We also discuss the moves we believe every CMO needs to make this quarter to make their brands more discoverable
TL;DR
- Discovery now happens inside an AI answer, not on your site. Most B2B software buyers now begin research with an AI chatbot rather than a search engine, and buyers are doing the same when they look for vendors.
- Being named is the new winning. Buyers think more highly of a vendor simply because an AI included it, and many end up choosing a different vendor than they planned because of what the AI surfaced.
- Reviews are the trust layer AI reads. Models reach for third-party proof they can verify, and peer reviews are that proof. Volume gets you over the threshold.
- G2 review data shows where enterprise momentum actually is. It is concentrating on coding assistants, agentic platforms, and enterprise search, the tools that connect to real systems and produce auditable outcomes.
- Read the distribution, not the badge. Two products with the same star rating can have completely different five-star concentrations, segment mixes, and reviewer composition. That distribution is what separates a vanity profile from a defensible one.
What changed about brand discoverability?
The behavioral data is no longer ambiguous. According to G2’s The Answer Economy, based on a March 2026 survey of 1,076 B2B software buyers, 51% now begin their research in an AI chatbot more often than with Google, up from 29% in April 2025. Seventy-one percent rely on AI chatbots somewhere in the research process, up from roughly 60% just seven months prior. And 53% say AI research is more productive than traditional search, nearly a doubling from 36%.
The shift, as G2’s research frames it, is a move “from reference to inference.”
Buyers used to ask search engines to point them toward sources, then synthesized the answer themselves. Now they tell a model to synthesize everything and return the shortlist in one prompt. The work you used to win, the comparison, the “who should I even consider,” is being done inside a chat window you cannot see.
This is why discoverability is more about being the answer. 69% of buyers told G2 they chose a different vendor than they had originally planned because of what an AI chatbot recommended, and one in three purchased from a vendor they had never previously heard of. Naming is now destiny. As the same research shows, 85% of buyers think more highly of a vendor simply because AI named it in an answer, and the inverse is the part that should keep CMOs up at night: if AI leaves you out, the buyer may never learn you exist.
It is tempting to dismiss this as top-of-funnel noise. The conversion data argues otherwise. In one B2B analysis cited by Seer Interactive, ChatGPT traffic converted at 15.9% against Google organic’s 1.76%. Lower volume, dramatically higher intent. They are buyers who pre-qualified the decision before you ever saw them.
Does quantity or quality of G2 reviews matter more for getting found by AI?
This is the question we get most from marketing leaders, and the honest answer is that the framing is wrong. It is not quantity versus quality. It is both, plus a third variable most teams ignore: velocity.
Start with why reviews matter at all. Large language models (LLM) will not confidently recommend a vendor on the strength of that vendor’s own marketing copy, because taglines do not train the model. They reach for third-party, structured, consensus signals they can trust, and in B2B software, that trust layer runs overwhelmingly through peer reviews.
G2’s research found that a citation from a review site is the single most confidence-inspiring signal a buyer can see in an AI answer, ranking review sites as the number two influence on shortlists, behind only the chatbots themselves.
Now the data on volume. In his analysis of 30,000 AI citations across 500 software categories, G2 Growth Advisor Kevin Indig found a measurable relationship: a 10% increase in reviews correlates with roughly a 2% increase in citations. Volume is a real lever. Reviews work less like a popularity contest and more like a machine-readable proof of consensus.
Here is where quality and velocity take over, and where G2’s own product data tells the story better than any survey. Look at the AI Coding Assistants category. GitHub Copilot carries 357 reviews at 4.5 stars, and Replit sits at 368 reviews and 4.4 stars. Cursor, with fewer total reviews at 299, holds a higher 4.7 rating. When I pulled Cursor’s recent review stream from G2’s data, the pattern showed that of roughly 276 reviews submitted since the start of December 2025, 230 were five-star, with an average score of 9.4 out of 10. That is a high rating accumulating fast, with recency and sentiment compounding together.
So do not chase a vanity review count. Build a review engine that produces three things at once: enough volume to clear the threshold where AI starts to trust you (Indig’s data suggests the marginal value of each new review is highest when you have fewer than 50), a star rating and sentiment profile that holds up under scrutiny, and a steady velocity so the fresh reviews keep arriving.
Which AI tools are getting the most enterprise momentum, according to G2 data?
When we look across G2’s category data, enterprise momentum is concentrating in two places, and the review signals make it unmistakable.
The first is the AI Coding Assistants category, which has become the clearest proving ground for production-grade enterprise AI. Cursor describes itself as used by 64% of Fortune 500 companies, and its G2 profile backs the claim with a 4.7 rating and the fastest five-star velocity I noted above. Anthropic’s Claude Code, newer to the category, already holds a 4.7 rating across 83 reviews, while Claude itself (355 reviews, 4.6) and GitHub Copilot (357 reviews, 4.5) anchor the established tier. Engineering is where AI is slotted into real workflows first, the outputs are measurable, and the tooling ecosystem is mature.
The second is the Agentic AI category, where momentum looks different. Salesforce Agentforce has gathered 1,197 reviews, positioning itself as an enterprise-agentic platform with governance and guardrails built in. Voice-agent platforms Retell AI (2,639 reviews, 4.8) and Synthflow (1,015 reviews, 4.5) show how fast review volume can accumulate when a category hits commercial product-market fit.
On the enterprise AI chatbot side, the pattern is a barbell. ChatGPT (2,647 reviews, 4.6) dominates on scale, while knowledge-grounded enterprise tools like Glean (4.7) and Moveworks win on depth. Moveworks is a useful momentum proxy on its own: its customer roster includes Toyota, Spotify, GitHub, Marriott, Snowflake, Databricks, and Palo Alto Networks. The signal across all three categories is consistent. Enterprise AI momentum is flowing to tools that connect to real systems and produce auditable, workflow-embedded outcomes, not to standalone novelty.
This matters for discoverability because these are the categories where AI search is most active and most consequential. The AEO Software category on G2 grew more than 2,000% in a single year as brands raced to address their AI visibility gaps. Momentum in the product creates momentum in the buying conversation.
Watch our recent guide for tips to pick the right AI tools in any category.
What AI strategy frameworks are companies actually adopting?
The frameworks that hold up in the real world are not complicated. They come down to a handful of patterns companies repeat.
The most widely cited is the 10-20-70 rule: Roughly 10% of the effort goes to algorithms and models, 20% to data and technology, and 70% to people and process change. As one practitioner puts it, “AI transformation fails when it is done to people rather than with them. The 10-20-70 rule is not a technology equation; it is a change management equation.”
The second is crawl-walk-run, paired with the discipline of stop criteria. Mature teams define what success looks like before a pilot starts, and they set the conditions under which they will stop and reallocate. Stopping a pilot that hits its kill criteria frees budget for the use cases that work.
The third is the 70-30, human-in-the-loop pattern: AI does roughly 70% of the work, a human validates before anything ships. As autonomy increases, this is becoming the default operating model, because the risk shifts from a system saying the wrong thing to a system doing the wrong thing. McKinsey’s 2026 work on AI trust makes the same point: Governance built in from the start accelerates adoption, while governance bolted on at the end stalls it for months.
The fourth, and the most underrated, is the Champion Network Model for adoption: Identify early adopters in each function, give them advanced training and visibility, and let peer-to-peer learning carry the rest. It outperforms top-down mandates because, as the field data shows, peer learning is the single largest source of AI skills inside organizations.
For CMOs specifically, we would add a fifth that the market is only beginning to name: an answer-engine optimization (AEO) framework that treats AI visibility as a measurable channel with its own instrumentation.
Most teams are still measuring page rankings, domain authority, and click-through rates (CTR) while their buyers have moved into AI. The frameworks above all share one premise: AI strategy is an operating-model decision. The same is true of discoverability.
What separates companies winning with AI from the ones stuck in pilot mode?
Two differences separate companies winning with AI from those still in the nascent stage:
First, the winners have already moved from pilot to production, and it is happening faster than anyone predicted. In G2’s 2025 AI Agents report, based on a survey of more than 1,000 B2B software buyers and thousands of G2 reviews, Tim Sanders found that roughly 57% of companies already have AI agents in production, with over half planning to expand their scope or budgets in the next twelve months. The companies winning with AI are not the ones running endless experiments. They start from a specific business pain, work back to the tool, and scale what shows near-immediate results. As Sanders puts it, we are officially past the “fear of missing out” era for AI.
Second, the winners integrate into workflows; the stuck deploy standalone tools. Both MIT and McKinsey’s research land on the same culprit. Generic tools stall in the enterprise because they do not learn or adapt to a specific workflow. McKinsey’s State of AI data shows 88% of organizations now use AI in at least one function, but fewer than 40% have scaled beyond pilot, and only 1% describe their AI strategy as mature. Workflow redesign is repeatedly identified as the number one predictor of measurable ROI.
Companies winning with AI are the ones that pick a sharp, specific point of view, integrate it deeply, and prove it in production. Those are precisely the companies that get named in an AI answer, because AI systems, like buyers, reward specificity and consensus over breadth and noise.
What CMOs must do now
Audit your AI presence the way your buyer experiences it: Open ChatGPT, Gemini, and Claude, and run the prompts your ideal customer would run. If you are not named, that is your baseline, and it is more honest than anything in your current dashboard.
Build a review engine, not a review campaign: Treat volume, quality, and velocity as one system. The freshest, highest-rated, steadily growing review profile is what AI reads as proof, and reviews are the one signal that keeps gaining influence as buyers move from consideration toward decision.
Re-instrument measurement: Add a distinct AI-referral channel to your analytics, watch branded and direct traffic as a proxy for AI influence, and accept that some of these signals will be imprecise. Imprecise and directionally right beats precise and obsolete.
Write for the answer, not the click: AI rewards specific, structured, expertise-rich content that takes a real position. Generic SEO content serves neither the model nor the pre-educated human who arrives after it.
Earn your way into AI search
A weak position in AI search is a verdict on your visibility, and visibility is the one thing on this list you can change this quarter. The buyers have already moved. 84% of CMOs are using AI to discover vendors, half of all software buyers start there, and the shortlist that decides your pipeline is being written in a window you do not control.
You cannot rank your way back into that conversation. You earn your way in with proof, specificity, and a presence in the sources AI trusts. The teams that act now will define their categories inside the answer. The ones that wait will keep optimizing for a search page their buyers have already left.
G2’s data across three software categories reveals a pattern that changes how you should think about your review strategy. Read more.
