The G2 AI Hub: Insights, Innovations, and Verified Buyer Data
Why Trust G2 on AI
G2 not only catalogs AI tools but also harnesses AI to enhance its platform. Our data is sourced from millions of verified users who actively use these tools daily.
- Over 3 million verified reviews, including more than 48,000 AI software reviews submitted in the last year. G2 Year in Review →
- G2 is the leading B2B source cited in large language models (LLMs), holding 22.4% share across ChatGPT, Perplexity, and Google AI Overviews. How G2 leads AEO →
- G2.ai, our AI software buying assistant, helps buyers articulate their needs in natural language for quicker decisions. Try G2.ai →
- All AI research on G2 is first-party, including Enterprise AI Agents, Buyer Behavior, and Answer Economy reports. G2 Research Hub →
Browse the full G2 AI category here →
What Is Artificial Intelligence?
Artificial intelligence (AI) refers to software that performs tasks typically requiring human intelligence, such as language comprehension, image recognition, prediction, and content generation. Most modern AI depends on machine learning, where systems improve by analyzing data rather than following predefined rules.
Full G2 explainer and AI terminology A-Z are available online.
A Brief History of AI
- 2024-2026: AI operates at scale; over 75% of organizations use AI; agentic AI transitions from pilots to production.
- 2022-2023: Generative AI becomes mainstream with ChatGPT achieving 100 million users in 2 months; GPT-4, Claude, Bard, and Gemini released.
- 1993-2020: Key milestones include Deep Blue beating chess champion (1997), Siri launch (2011), Google’s neural network recognizing cats (2012), AlphaGo defeating Go champion (2016), and GPT-3 testing (2020).
- 1957-1987: LISP creation (1958), first industrial robot (196
Why trust G2 on AI
G2 doesn’t just index AI tools. We use AI to power our own platform, and our data comes from millions of verified buyers who use these tools daily.
3M+
Verified reviews
3M+ verified G2 reviews total. 48K+ AI software reviews submitted in the past year.
#1
Most cited B2B source in LLMs
G2 leads AI citation share with 22.4% across ChatGPT, Perplexity, and Google AI Overviews.
G2.ai
G2’s AI software buying assistant
G2.ai helps buyers describe their needs in natural language and make faster software decisions.
1st-party
AI research from G2’s own data
Enterprise AI Agents, Buyer Behavior, and Answer Economy reports. All first-party G2 research.
What is artificial intelligence?
Artificial intelligence is software that performs tasks normally requiring human intelligence, such as understanding language, recognizing images, making predictions, and generating content.
Most modern AI relies on machine learning, in which systems improve by processing data rather than following hand-coded rules.
Full G2 explainer → | AI terminology A-Z →
A brief history of AI
2024-2026 · AI at operating scale
Over 75% of organizations use AI. Agentic AI moves from pilot to production.
2022-2023 · Generative AI goes mainstream
ChatGPT launches and reaches 100M users in 2 months, the fastest consumer tech adoption ever (2022). GPT-4, Claude, Bard, and Gemini follow (2023).
1993-2020 · Agents and deep learning
Deep Blue beats the chess world champion (1997). Apple launches Siri (2011). Google’s neural network learns to recognize cats from unlabeled images (2012). AlphaGo defeats Go’s world champion (2016). GPT-3 enters testing (2020).
1957-1987 · Maturation and boom
McCarthy creates LISP (1958). The first industrial robot debuts at GM (1961). ELIZA, the first chatbot, launches (1966). Expert systems hit commercial markets (1980); Japan invests $850M in AI (1981).
1950-1956 · Birth of AI
Alan Turing publishes “Computing Machinery and Intelligence” (1950), proposing the Imitation Game. John McCarthy coins “artificial intelligence” at the Dartmouth workshop (1956).
What are the different types of artificial intelligence?
By capability, it includes Narrow AI, AGI, and Superintelligence. Narrow AI is the only form that exists today, while AGI and Superintelligence remain theoretical. By function, AI includes Generative AI for content creation, Agentic AI for autonomous task execution, Predictive AI for forecasting outcomes, and reasoning models for solving complex problems step by step.
By capability
Narrow AI (weak AI)
Narrow AI powers everything from spam filters to ChatGPT and Claude. “Narrow” refers to the scope of capability, not sophistication; even advanced LLMs operate within defined domains rather than possessing general reasoning.
Artificial general intelligence (AGI)
A system that could switch contexts and reason across any domain a human can. Researchers disagree on both definition and timeline, with serious predictions ranging from a few years away to never.
Artificial superintelligence (ASI)
A system that exceeds human cognitive ability across every field, including science, creativity, and social intelligence. Most often discussed in AI safety and alignment contexts. No scientific consensus on whether it is achievable.
By function
Generative AI
Trained on vast datasets to produce new outputs across text, images, code, audio, and video. Power tools like GPT, Claude, and Midjourney.
Agentic AI
It goes beyond generating responses by taking actions such as querying databases via tool calls and API integrations.
Predictive AI
Built on statistical models and machine learning, Predictive AI identifies patterns in historical data to forecast outcomes.
Reasoning models
A newer class of AI that reasons step by step before responding, designed for complex coding, math, and analysis tasks.
What are the applications of artificial intelligence?
Artificial intelligence is used across marketing, customer support, sales, engineering, and HR to automate tasks and improve efficiency. Common AI applications include content creation, chatbots, meeting transcription, sales prospecting, code generation, recruiting, and AI agent development.
Ratings shown are G2 reviewers’ average likelihood to recommend on a 0–10 scale.
Marketing
7 categories · 14K reviews
G2’s largest AI vertical by review volume. AI Content Creation Platforms top the vertical at 9.53/10, ahead of AI Presentation Tools at 9.47/10 and AI Avatar Generators at 9.43/10. AI Writing Assistants lead by volume with 7K reviews; AI Content Creation Platforms also grew +110% YoY.
9.53/10
Average likelihood to recommend for AI Content Creation Platforms, the highest-rated marketing AI category
G2 AI Review Data
Productivity
5 categories · 12.5K reviews
Highest-rated AI vertical among the seven core business functions at 9.35/10. Spans meeting capture (AI Meeting Assistants 9.40/10), note-taking, writing assistance, document generation, and general business agents. AI Agents for Business Operations lead by volume at 4.8K reviews; AI Note-Taking Software grew +1,263% YoY.
+1,263%
YoY review growth for AI Note-Taking Software, the fastest-growing AI category on G2
G2 AI Review Data
Customer experience
5 categories · 8.8K reviews
AI Chatbots dominate by volume at 3.8K reviews. AI Customer Support Agents grew +432% YoY as buyers replace traditional helpdesk tools with agentic AI. The vertical averages 9.09/10, among the lowest in AI on G2, with Conversational Interface Agents pulling the average down at 8.67/10.
+432%
YoY review growth for AI Customer Support Agents
G2 AI Review Data
Sales
4 categories · 5.9K reviews
AI Sales Assistants dominate at 4K reviews with a 9.33/10 average. Revenue AI Platforms hold 1.6K reviews. AI SDRs are the fastest-growing sales AI at +259% YoY and rate 9.58/10, the highest in the vertical. The sales vertical averages 9.27/10 across all 4 categories.
+259%
YoY review growth for AI SDRs
G2 AI Review Data
Platform infrastructure
6 categories · 5K reviews
The foundational layer powering all other AI verticals: agent builders, large language models, ML platforms, and orchestration. AI Agent Builders lead adoption at 2.6K reviews and grew +719% YoY. Large Language Models score 9.20/10; legacy Machine Learning sits lowest at 8.86/10.
+719%
YoY review growth for AI Agent Builders
G2 AI Review Data
Engineering
6 categories · 2.6K reviews
Spans code generation, coding assistants, app builders, software testing, documentation generation, and IT operations. AI Code Generation leads at 1K reviews and grew +109% YoY. AI Software Testing Tools sit lowest in the vertical at 8.95/10.
+109%
YoY review growth for AI Code Generation
G2 AI Review Data
HR
2 categories · 1.5K reviews
Smallest dedicated AI vertical on G2 but among the fastest-growing. AI Recruiting leads at 871 reviews and grew +651% YoY; the category is now classified as high-risk under the EU AI Act with mandatory bias audits. AI Agents for HR score the highest in the vertical at 9.16/10.
+651%
YoY review growth for AI Recruiting
G2 AI Review Data
Other applications
9 categories · 871 reviews
AI applications extend beyond the seven core business functions. Security has the deepest specialized coverage on G2 with five dedicated AI categories spanning Governance, AI-SPM, AppSec Assistants, AI Security Solutions, and Non-Human Identity Management (438 reviews at 9.14/10). AI in healthcare leads all AI applications on G2 at 9.63/10 across Patient Engagement and Ambient Scribes. AI Assistants for Financial Services hold 335 reviews at 9.17/10.
9.63/10
Average likelihood to recommend for healthcare AI categories, the highest of any AI vertical on G2
G2 AI Review Data
What are the best AI tools and software?
The best AI tools and software depend on the task, but G2 users most commonly adopt AI writing assistants, AI chatbots, AI meeting assistants, AI sales assistants, AI video generators, AI coding tools, AI recruiting software, and AI agent builders to automate repetitive work, improve productivity, and scale business operations.
| Category | Best for | Who uses it | G2 resource |
|---|---|---|---|
| AI writing assistants | Drafting, editing, SEO writing, long-form content | Content writers, marketers, SEO teams | See the list → |
| AI agents for business operations | Multi-step workflow automation without constant human input | Operations, RevOps, and cross-functional teams | See the list → |
| AI sales assistants | Deal coaching, CRM updates, meeting summaries, pipeline intelligence | Account executives and sales managers | See the list → |
| AI chatbots | Automating conversations for support, lead qualification, and internal FAQs | Customer support, marketing ops, IT helpdesks | See the list → |
| AI meeting assistants | Meeting transcription, summarization, and action item capture | Anyone who attends meetings; used across sales, ops, and executive roles | See the list → |
| AI video generators | Producing AI-generated video content, avatars, and explainers from text or scripts | Marketing, sales, L&D, and creator teams | See the list → |
| AI code generation | Generating, completing, and refactoring code with natural language prompts | Software engineers and developer teams | See the list → |
| AI agent builders | Building custom AI agents and orchestrating multi-step workflows | Platform teams, AI engineers, and technical RevOps | See the list → |
| AI recruiting | Sourcing, screening, and scheduling candidates with AI; bias-aware workflows | Talent acquisition, HR ops, recruiting agencies | See the list → |
| Generative AI | Creating text, images, and code from prompts; foundation-model tools like ChatGPT, Claude, and Gemini | Anyone evaluating general-purpose AI assistants | See the list → |
How do you evaluate AI software?
Evaluating AI software comes down to five criteria: output quality, integration, time to ROI, data privacy, and vendor transparency. Patterns across 48K+ verified AI software reviews on G2 between May 2025 and April 2026 show output quality and integration friction as the most-cited dissatisfaction drivers, while vendor transparency consistently separates the highest-rated tools from the rest.
How likely G2 reviewers are to recommend their AI software (0–10):
78.8%
Would recommend their AI tool to a peer
Rated 9 or 10 out of 10
3.0%
Would actively warn a peer off it
Rated 6 or below
9.21/10
Average likelihood to recommend
Across 45,250 unique reviewers
| Criterion | What to look for | What buyers say in reviews |
| Output quality | Does the AI produce results you can actually use? Look for customization controls, domain-specific tuning, and hallucination mitigation. | Generic outputs that do not adapt to context are the most frequently cited complaint across AI sales and marketing tools on G2. |
| Integration | Does it connect to your CRM, data stack, and workflows out of the box? Look for native integrations over API-only connections, which buyers consistently flag as sources of friction. | Integration and setup friction is consistently cited across AI sales assistant reviews on G2; the most commonly cited cause of AI agent workflow failures. |
| Time to ROI | Ask vendors for median time-to-ROI, not averages. The fastest-deploying AI categories on G2 go live in under a month and show ROI within 6 months. | Machine learning software tends to take notably longer than other AI categories to show ROI, a gap vendors rarely disclose upfront. |
| Data privacy | Where is your data processed? Does the AI train on your inputs? Ask vendors directly about data residency and model training policies. | Buyers consistently flag the absence of clear data policies as a reason they delayed or reversed purchase decisions. |
| Vendor transparency | Look for responsive support, honest capability claims, and clear pricing. | Pricing transparency and configuration complexity are among the most common pain points cited across G2’s AI agent reviews. |
G2 research and reports on AI
G2 publishes data-driven reports on AI adoption, buyer behavior, and market trends, backed by G2’s proprietary review and survey data.
G2 flagship reports
Annual flagship
G2 State of Software: AI growth and buyer sentiment
Primary source for AI software category growth and buyer adoption trends.
2026 industry outlook
G2’s Enterprise AI Agents Report: 2026 outlook
Vendor survey on the maturity, autonomy, and outcomes of AI agents.
Annual survey
Buyer Behavior Report 2025: AI always included
1.1K+ B2B decision-makers; two-thirds factor AI into purchase decisions.
Reach 2025 keynote
AI mega trends: Transforming the future of go-to-market
How AI is reshaping go-to-market across sales and marketing.
G2 research by function
AI decision intelligence in marketing: G2’s 2026 industry report Read →
The state of AI sales intelligence in prospecting: G2’s 2026 report Read →
AI in data integration: G2’s 2026 vendor insights Read →
G2 leadership on AI
Perspectives from G2’s leadership team, grounded in platform data and first-party research.
Software brand visibility in the AI search era · Godard Abel, CEO Read →
G2 and Profound partnership for AEO and AI search · Godard Abel, CEO Read →
How AI agents are delivering real business impact · Tim Sanders, CIO Read →
G2’s product innovations for the AI answer economy · Alexis Zheng, CPTO Read →
Conversational reviews for an AI-first era · Alex David, GM, AI Solutions Read →
AI statistics
Figures from The Answer Economy: How AI Search Is Rewiring B2B Software Buying, G2’s April 2026 research report, based on a survey of 1,000+ B2B software buyers and decision-makers across North America, EMEA, and APAC.
51%
of B2B software buyers now start their research with an AI chatbot more often than with Google.
71%
rely on AI chatbots at some point in their software research process, up from 60%.
69%
chose a different software vendor than initially planned based on AI chatbot guidance
33%
purchased from a vendor they had never previously heard of before AI surfaced them
85%
think more highly of a software vendor when an AI chatbot mentions them in a recommendation
80%
say AI chatbots have accelerated their software purchasing decisions
AI glossary
Short definitions of the terms you will encounter most often when evaluating AI software.
| Term | Definition | Read more |
| Artificial intelligence (AI) | The ability of a computer system to perform tasks that typically require human intelligence, including learning, reasoning, problem-solving, and language understanding. | G2 glossary → |
| Machine learning (ML) | A subset of AI where systems learn from data to improve performance without being explicitly programmed for each scenario. | G2 glossary → |
| Deep learning | A subset of machine learning that uses multi-layered neural networks to identify patterns in large datasets. Powers image recognition, speech recognition, and generative AI models. | G2 glossary → |
| Generative AI | AI models that create new content (text, images, code, audio, or video) by learning patterns from training data. Includes LLMs like GPT-4, Claude, and Gemini. | G2 glossary → |
| Large language model (LLM) | A type of generative AI trained on vast text data to understand and generate human language. The engine behind most modern AI chatbots and writing assistants. | G2 glossary → |
| Natural language processing (NLP) | The AI branch that enables computers to understand, interpret, and generate human language. Powers chatbots, search, translation, and sentiment analysis. | G2 glossary → |
| Prompt engineering | The practice of crafting inputs to AI models to get specific, high-quality outputs. A key skill for anyone deploying LLM-based software in production. | G2 glossary → |
| Algorithmic bias | Systematic errors in AI outputs that produce unfair outcomes for specific groups, usually traced to biased training data or flawed model design. A growing concern in hiring, lending, and healthcare AI. | G2 glossary → |
| Human-in-the-loop (HITL) | An approach where humans review, correct, or approve AI decisions during operation. Common in high-stakes deployments where accuracy or accountability is critical. | G2 glossary → |
FAQs about artificial intelligence
Find answers to some commonly asked questions about AI.
Modern AI learns patterns from data instead of following hard-coded rules. A model is trained on a large dataset, then applied to new inputs once deployed. The capabilities most people use today (chatbots, code generators, voice assistants, image tools) are built on deep learning, which uses layered neural networks to capture patterns at scale.
Business AI adoption clusters around five functions: content generation (writing, design, video), conversational interfaces (chatbots, helpdesks), workflow automation, data analysis, and software development assistance. Adoption is highest in roles with repetitive text-and-communication work, and growing fastest in operations, engineering, and HR.
Buyers research and shortlist software through AI chatbots before visiting a vendor’s website. By the time a buyer reaches a sales conversation, the vendor has often been evaluated and either included or excluded by an AI model summarizing third-party sources. The shift is from search engine optimization to answer engine optimization (AEO): structuring content so AI models can find, cite, and recommend it accurately. G2’s Answer Economy Report covers this in detail.
The main pattern is a gap between vendor promise and operational reality. Buyers often expect AI to work autonomously out of the box, then learn that consistent results require careful prompting, integration work, and human review. Reviews tend to favor vendors who position AI as a way to accelerate human work with structured oversight, rather than as a hands-off replacement. How well expectations match reality at purchase time often predicts long-term satisfaction.
Growth on G2 shows up in two dimensions: review volume and buyer traffic. By year-over-year review growth, the leaders are Conversational AI Survey Platforms (+1,126%), Answer Engine Optimization (+907%), Agentic AI (+382%), AI Voice Assistants (+150%), and AI Writing Assistants (+59%). By buyer traffic growth, AEO (+496%), Customer Service Automation (+450%), and AI SDRs (+85%) lead. Machine Learning still draws heavy buyer traffic despite slower review growth, suggesting buyers evaluate ML platforms more than they review them.
Two patterns separate the strongest AI rollouts from weaker ones. AI-native products consistently outscore legacy tools that have AI features added on; reviewers describe the latter as stitched together and slow to adapt. Products that fit cleanly into existing workflows show faster time-to-value than ones requiring teams to change how they work. The strongest predictor of ROI is a narrow, well-defined use case, not breadth of features.
The clearest way to see the difference is a workflow example. Ask a generative AI to write a follow-up email and it produces the email. Give an agentic AI the goal “follow up with this lead” and it pulls the prospect record, drafts the email, schedules the follow-up, updates the CRM, and logs the outcome, none of which required step-by-step prompting. The shift is from producing content on demand to executing multi-step work toward a goal.
Four concerns dominate enterprise discussions: bias (particularly in hiring, lending, and healthcare AI), data handling (whose data is used for training, where it is stored, who can access it), explainability (whether decisions can be audited), and accountability (who is responsible when AI produces incorrect outputs). Regulation is catching up; the EU AI Act and emerging US state laws are codifying these into purchase-time requirements. For software buyers, due-diligence on training data, model behavior, and incident response is increasingly standard practice.
About this data. The figures cited on this page come from G2’s proprietary data. Review-based statistics are drawn from 48K+ verified AI software reviews submitted to G2 between May 1, 2025 and April 30, 2026, spanning 85 AI software categories and 2K+ distinct products. Each review is independently approved before publication.
Survey-based statistics come from G2’s published research, including The Answer Economy: How AI Search Is Rewiring B2B Software Buying, the Enterprise AI Agents Report, and the 2025 Buyer Behavior Report.
Aggregations, year-over-year growth rates, category breakdowns, and rating distributions were calculated using G2’s Snowflake data warehouse with AI-assisted analysis via Anthropic’s Claude.
