As buyers turn to AI chatbots for software recommendations, marketers have a new audience to reach: the systems helping buyers research and evaluate products.
“We are now talking to humans and agents. We’ve never built a playbook for that,” said Alex London, CMO at G2.
A G2 report, “The Evaluation Maze,” found that 82% of B2B buyers had sourced software recommendations from an AI chatbot in the prior two years. Those chatbots and the AI agents behind them, systems that research and recommend products on a buyer’s behalf, are the second “audience” London is referring to.
G2, a software marketplace and review platform, sells vendors advertising and buyer-intent products. The company therefore has a commercial stake in how reviews and AI discovery shape software buying.
Building a Presence Buyers and AI Can Trust
With buyers using AI chatbots to research products before visiting a vendor’s website, a brand’s presence across the web — not just its own site — becomes more important, according to London. AI systems may draw on both company content and third-party sources, including reviews, when generating answers or shortlists.
Marketers should keep brand messaging consistent across those sources and prioritize useful, accessible information, rather than publishing more content to increase volume. “It goes well beyond just the content,” London said. “It’s around that trust layer.”
She recommends reviewing a brand’s digital presence about every 90 days, based on her read of model refresh patterns, though schedules vary by system.
SEO’s Role
London does not see AEO as a replacement for SEO. She said search engine optimization, search engine marketing, answer engine optimization and what G2 calls answer engine marketing should work together. “I don’t think it’s an ‘or.’ I think [that’s] where your investments go,” she said.
Marketers should maintain the foundations of search visibility while adapting content for AI-driven discovery, London said. She also pointed to third-party validation, including reviews, as a potential source of information beyond a company’s own claims.
“If you don’t start to invest in AEO and answer engine marketing, which is really how you’re marketing to these models, then that’s where you’re going to fall behind,” she said.
Measuring Discovery, Not Just Mentions
Marketers should look at whether their brands appear in answers buyers use to build shortlists and whether that visibility is connected to business outcomes, London explained, because a brand mention or citation does not necessarily mean a system is recommending that brand. “You can’t just stop at a mention and a citation. You have to make sure you’re winning the answer,” she said.
AI discovery has become a measurable acquisition channel for G2. Since it began separating organic AI-assistant traffic from conventional referral traffic, G2 has attributed more than 5% of referral sessions, more than 20% of referral leads and over 10% of referral-attributed revenue to AI discovery. The company said paid AEO accounts for less than 1% of its paid-search clicks, leads and revenue.
AI agents accounted for 68% of G2’s audience in Q2 2026, compared with 32% for humans, said London. G2’s total traffic was up 35.5% year over year. The company uses tools including Profound Agent Analytics and Cloudflare traffic data to identify agent visits and distinguish user-triggered activity from ordinary crawlers and background requests.
How G2 uses AI in its Marketing Operations
Separately, G2 has built about 90 agents across its marketing organization, though London cautioned that companies sometimes use “agent” to describe simpler automated workflows.
One example is a paid-search workflow, built by a G2 employee using Claude, that analyzes search-term exports, flags irrelevant queries and identifies potential keyword expansions. G2 says the workflow reduced tasks that previously took seven to 12 hours of team capacity each week to minutes.
London said teams should give AI tools clear context and brand guidelines, test them before relying on them at scale and measure whether they improve efficiency. The goal, she said, should be measurable efficiency gains, not AI use for its own sake.
