Key Takeaways:
- G2 introduces tools like the Model Context Protocol (MCP) to enhance AI-driven buying with trusted data signals.
- New features like structured FAQs and LinkedIn verification improve buyer insights and AI search visibility.
To help MarTech software companies build and benefit from trust in the age of artificial intelligence (AI)-driven buying G2 has released new product capabilities that include richer buyer content, identity verification through LinkedIn, a new model context protocol (MCP) powered by Claude, and expanded market intelligence.
As B2B buyers now rely on AI search to evaluate software, the answers those systems generate increasingly depend on credible, third-party sources. By strengthening verified buyer signals and structuring content for AI engines, G2 helps vendors increase their AI search visibility while giving buyers more credible insights when evaluating solutions, according to company officials.
At the same time, competitive and market intelligence gives go-to-market (GTM) teams actionable buyer signals earlier in the journey. As a result, vendors can make the shift from simply generating visibility to using trusted buyer evidence to drive discovery, competitive advantage, and growth.
Adjusting to AI
AI is transforming how companies analyze markets and make decisions, but those systems need trusted data signals to produce meaningful insights, said Alexis Zheng, Chief Product & Technology Officer, G2.
Demand Gen Report is gathering audience feedback and wants to hear directly from readers—take our one question LinkedIn poll now
“At G2, we take pride in serving as the trust layer powering decisions for the entire software ecosystem….our latest innovations are designed to help software buyers and vendors win with trust in the age of AI,” said Zheng in a statement. “Our new G2 MCP is a prime example of that: connecting AI assistants directly to G2 data, so teams can make faster decisions with greater confidence.”
New Content, Review Experiences
G2 is introducing new ways to capture richer insights from real users, having capabilities to surface deeper buyer insights by structuring user feedback in formats both human buyers and AI systems can easily interpret. They are:
- Structured category FAQs: Authoritative answers across key software categories, designed to help buyers understand common product questions while also making information easier for AI systems to process
- Guided discussion prompts: Conversation prompts that encourage deeper discussions around real use cases, implementation experiences, and product trade-offs. These guided conversations surface deeper, authentic context about how software performs in real environments
- Focused feature list: An improved review experience that uses signals to identify how buyers naturally describe product capabilities in reviews and converts those insights into structured feature comparisons, potentially saving users hours of work
Together, these capabilities help ensure the software buying journey is shaped by authentic user experiences, stated company officials
Additional Offerings
G2 has expanded its partnership with LinkedIn by integrating the Verified on LinkedIn feature directly into its review moderation workflow. This update displays a reviewer’s verified identity, employer, or education to confirm that feedback comes from real professionals rather than anonymous sources.
To make this verified buyer data more accessible, the company launched a new architecture that connects its market intelligence directly to AI workflows, starting with Anthropic’s Claude. Instead of relying on scraped web data, AI search platforms can now reference verified reviews, research behavior, and competitive insights straight from the G2 marketplace. Users can easily identify which accounts are researching competitors, understand common product strengths, and catch early signs of customer churn.
Additionally, G2 introduced expanded market intelligence features to help companies track shifting buyer behavior and software markets. A new Competitive Pulse view combines CRM opportunity data with buyer intent signals to highlight at-risk deals and rising competitors. The platform also added answer engine optimization analytics to measure engagement from AI-driven searches, helping companies see how AI influences software evaluation.
