ai software categorie in 2026 If your company bought Salesforce Sales Cloud in 2023, you may find it listed on G2 today as Agentforce Sales. That rename illustrates the central question behind this analysis of AI software categories in 2026: are buyers finding genuinely new products, or are established vendors changing labels around existing tools? In this article, a software re-rating means that a product gains a new position on a buyer’s shortlist because its capabilities, category, or market narrative has changed. We examine the functions moving fastest and show enterprise teams how to distinguish evidence from positioning. G2 category and review data provides a useful market signal, but this analysis is not a complete census of every business, app, platform, or product on the web.
The AI SDRs category did not exist when many companies signed their contracts, yet it already lists 400 products. Similar movement appears across tools for coding, content, automation, workflow management, integration, research, and customer work. It also covers specialized applications for text, voice, audio, and video, from a coding assistant such as cursor or claude code to a productivity app connected to google workspace. Buyers comparing the best tools, selecting a best tool for one team, or weighing a free tier against paid tiers may focus on pricing and credits per month. However, category placement can shape the shortlist before anyone tests a model, context, context window, files, or an agent. The purpose is to explain how the market is being reorganized, not to recommend one tool.
Sales and GTM is the function AI is reshaping fastest in 2026, followed by software development, customer support, marketing, security, recruiting, and meeting tools. Established vendors are re-rating themselves through new names and category tags, while newer providers compete for attention with specialized apps and platforms. We assess how teams use agents to complete work, whether an AI workflow saves time, and whether claims withstand independent research. A free tier, pro plan, low price per month, Perplexity listing, voice feature, video feature, audio feature, or AI coding label does not prove better automation. Readers will learn how to evaluate market movement, product evidence, integration requirements, and business fit before adopting AI.
TL;DR
- AI is rewriting the software category map. G2 created 103 categories between September 2025 and September 2026, including 41 AI-native categories, compared with 28 of 70 the year before and 5 of 88 the year before that. “AI-native” describes a category label, not verified product quality, adoption, or business value.
- Sales and GTM is changing fastest, followed by software development, customer support, marketing and search, security and IT operations, and HR and recruiting. This ranking shows where buyers and vendors are reorganizing categories, not which tools are automatically the best tools or which product is the best tool for every team.
- Evidence remains limited. Across 12 AI-native categories, 3,297 of 4,292 listed products have no verified reviews. Review dates, company size, use case, implementation work, and measurable time savings matter more than a label alone.
- Incumbents are often re-rating themselves instead of being displaced by startups. A renamed product may inherit older reviews, while a new agent may have little review history. Buyers should compare current evidence before choosing tools, models, or agents.
A category is the market’s memory, and AI is rewriting it
In practical software buying, a re-rating occurs when a product earns a new place on a buyer’s shortlist because its capabilities, category, or market position has changed. The underlying product may not have changed in a single month, yet buyers may describe its work, competitive set, and expected value differently. That shift can affect which teams discover the product, what tasks they expect it to handle, and how much they are willing to pay.
Financial markets use “re-rating” for a change in the multiple investors assign to a company. Software categories work similarly. A G2 category records what a type of software is expected to do, who buys it, and which products appear relevant. It helps users compare tools, but it is not proof that every listed product works equally well. A category label can influence a business decision before a user tests an app, agent, workflow, model, or code output.
G2’s taxonomy is useful because it shows how the market is being organized, but it does not measure the entire market. G2 creates a category when distinct products and buyer interest make existing labels insufficient. Category creation can therefore signal that a market is forming, while product relabeling shows how vendors want to be found. Neither signal proves adoption, revenue, product quality, or successful automation. A new category may contain established platforms, emerging apps, infrastructure tools, and speculative listings at the same time.
The 2024-to-2025 period established much of the initial AI structure through categories such as Agentic AI, AI Agents, AI Agent Builders, AI SDRs, AI Customer Support Agents, and AI IT Agents. The 2025-to-2026 period extended that structure into departmental agents and the infrastructure connecting them to existing workflows. New categories now describe software for sales, support, coding, content, research, automation, security, recruiting, integration, and governance.
Consider a familiar product that adds an AI assistant, changes its positioning, or receives a new category tag. It may now compete with an AI-native product even when its core database, interface, and business process remain largely unchanged. A productivity platform connected to Google Workspace may be described as an automation tool; a coding assistant may be compared with other coding products; and a search service may be evaluated alongside research tools. Those comparisons can help buyers, but a label alone does not prove that a product can manage context, use a long context window, complete complex work, or deliver reliable model output.
The fair objection is that a new label says little about buyer behavior. A category indicates that a market may be forming; it does not prove that the market has arrived. To separate taxonomy from adoption, this analysis compares category creation with review evidence, review age, company size, use case, and the work buyers describe. It also considers whether agents save time, fit existing workflows, and perform beyond a demonstration. Category growth can reveal positioning before it reveals durable demand.
“Agents are the great leap in the application of AI technology that we’ve all been waiting for, signaling a shift from measuring individual productivity to predicting organizational velocity.”
Tim Sanders
Chief Innovation Officer at G2
Tim Sanders, Chief Innovation Officer at G2, made that statement in G2’s October 2025 AI Agents report. He also predicted that more than 35% of enterprise companies would set agent budgets of $5 million or more in 2026. That forecast is a prediction about future spending, not evidence that every enterprise has adopted agents or that every model can safely perform business-critical tasks. It does show why category placement matters: large budgets require teams to compare products, platforms, integrations, workflows, and expected outcomes before committing.
Read The Answer Economy: How AI Search Is Rewiring B2B Software Buying to see where buyer shortlists form, how software is discovered on the web, and why category placement can influence a decision before a sales conversation.
How we measured the re-rating
We measured the re-rating with three signals from G2 category and review data collected as of September 12, 2026. The analysis covers six business functions: Sales and GTM, software development, customer support, marketing and search, security and IT operations, and HR and recruiting. We selected these functions because each has new AI categories, identifiable flagship AI categories, and enough category or review data to compare market movement. Other business areas may also be changing, but they were outside this analysis or did not provide comparable evidence.
Each signal answers a different question. New AI categories uses a simple count: the number of AI-native categories G2 created for a function between September 2024 and September 2026. Proof gap is calculated as the number of products without a verified review divided by the total number of products listed in that function’s flagship AI category, multiplied by 100. Review velocity is the number of reviews written during the latest 12-month period divided by the product’s lifetime review total, multiplied by 100. These measures indicate category movement and the freshness of buyer attention; they do not score product quality.
- New AI categories: We counted categories whose names identify AI, agents, or models. This includes categories connected with sales, coding, content, research, automation, security, recruiting, integration, and related business workflows. “AI-native” describes the category label, not necessarily a new product, autonomous behavior, successful work, or verified adoption. A category may include established platforms, new tools, infrastructure products, and products that G2 has reclassified.
- Proof gap: We calculated the percentage of products in the flagship AI category that have no verified reviews. A verified review is buyer feedback that meets G2’s verification standard; a rated review may contain a rating but does not necessarily provide the same verification status. The proof gap does not measure pricing, implementation effort, model accuracy, security, or the value of an agent. A product may offer a free tier, charge credits, or cost a different amount per month without changing its proof-gap status.
- Review velocity: We calculated the share of a product’s lifetime reviews that were published during the latest 12-month period. Lifetime reviews show accumulated visibility, while recent reviews show where buyer attention may be moving. This measure includes the review totals reported in the source data and should not be confused with a rating or quality score. Teams may review a newly launched tool, coding app, or AI workflow more actively than a mature system of record, and vendors may differ in how they request reviews.
Review velocity helps correct the review-count problem. A category page may show thousands of reviews, but many could have been written years earlier for a different product, workflow, or use case and later carried into the category through re-tagging. A high lifetime total therefore indicates accumulated market visibility, not necessarily current performance. Recent reviews can show whether buyers are discussing the product’s present capabilities, including its models, context handling, files, code, integrations, and automation.
We checked the underlying counts, percentages, and dates against the cited research used in the article. Percentages may be rounded, so the displayed proof gap should be read as the reported category-level figure rather than recalculated from a rounded table value. Before publication, each numerator and denominator should be reconciled with the source, including the 74%, 89%, 76%, 67%, 75%, 52%, and 79% proof-gap figures. The AI SDRs example requires particular care: its 400 listed products and reported 74% proof gap should match the precise number of products without verified reviews. The same check applies to every review count and date in the table.
| Rank Function New AI categories, Sep 2024 to Sep 2026 Flagship AI category: listed products and proof gap Review velocity: share of lifetime reviews written in the past 12 months | ||||
| 1 | Sales and GTM | 5 | AI SDRs: 400 products, 74% proof gap | Salesforce Agentforce 84%; Outreach 3.4% |
| 2 | Software development | 8 | AI Coding Assistants: 369 products, 89% proof gap | Cursor 94%; GitHub Copilot 60% |
| 3 | Customer support | 5 | AI Customer Support Agents: 897 products, 76% proof gap | Agentforce 84%; Fin 9% |
| 4 | Marketing and search | 6 | Answer Engine Optimization: 623 products, 67% proof gap | Semrush 32% |
| 5 | Security and IT operations | 7 | AI IT Agents: 146 products, 75% proof gap; AI SOC Agents: 52 products, 52% proof gap | Splunk Enterprise Security 2.4% |
| 6 | HR and recruiting | 3 | AI Recruiting: 528 products, 79% proof gap | Paradox 57%; Greenhouse 16% |
The table ranks Sales and GTM first because its category creation, proof gap, and review velocity signals point in the same direction. Software development created eight AI categories, the highest total, but its 89% proof gap shows that most listed coding tools still lack public evidence. Customer support has the largest flagship category by product count, while marketing and search show established content and SEO products entering AI visibility categories. Security and IT operations have several new labels but limited fresh judgment, and HR and recruiting has a large AI Recruiting category alongside a more cautious incumbent market.
The percentages in the table are directional and may be rounded. They should not be interpreted as a quality leaderboard or as evidence that one tool is the best tools choice for every organization. Buyers should use the figures to form questions about relevant tasks, company size, security controls, integrations, workflow fit, and total pricing. They should also ask how an agent handles files, text, code, context, and a long context window; how much time teams spend checking its output; and whether the product works across the required platforms. A strong review-velocity figure can show recent attention, but it cannot prove reliable enterprise performance.
For the same reason, the ranking does not identify a single best tool. A product with fresh reviews may serve a narrow use case, while a mature product with slower velocity may offer deeper integration, stronger governance, or more predictable support. The evidence becomes useful when teams combine category movement with current reviews, independent research, pilot results, security diligence, and the actual requirements of their business. Category data can help organize a shortlist, but it cannot replace testing the product in the workflow where it must deliver results.
1. Sales and GTM: The re-rating with a renamed flagship
Sales and GTM ranks first because all three signals move together: category creation, proof gap, and review velocity. G2 added five AI categories to the sales stack between 2024 and 2026: AI SDRs, Agentic GTM Platforms, Revenue AI Platforms, AI Sales Roleplay Tools, and AI Proposal Generator Tools. Together, these categories show how vendors and buyers are reorganizing sales work around agents that research prospects, draft content, manage follow-up tasks, and automate parts of a representative’s workflow. The category growth indicates market movement, not that every product delivers the same results.
The strongest evidence comes from the contrast between a new AI category and established products entering it. Salesforce Agentforce has 1,253 reviews, including 1,051 written during the past 12 months. That produces an 84% review velocity. Outreach, the most-reviewed established product tagged as an AI SDR, has 3,590 reviews, with 123 added during the same period, or 3.4% of its lifetime total. Review velocity is not a performance score. It reflects recent buyer attention, product activity, customer adoption, vendor review programs, and the age of a listing.
These products also represent different software types. A CRM stores customer records and supports sales processes. Sales-engagement software organizes outreach, sequencing, and rep activity. An AI SDR may research accounts, qualify prospects, write messages, or schedule follow-up. An agentic GTM platform can coordinate several tasks across apps and systems. Those labels overlap, but they do not describe identical capabilities, permissions, integration requirements, or risks. A mature CRM with an AI assistant should not automatically be compared with an autonomous prospecting agent.
The difference changes how readers should interpret a category page. Outreach’s larger review base reflects years of experience with sales engagement, including email tracking, sequencing, and rep productivity. Agentforce’s recent reviews are more closely associated with an agent-led approach to sales and service work. A large review total can make a mature tool appear more proven in a new category than it is for the specific AI tasks a team wants to evaluate. Conversely, high review velocity can show momentum without proving reliability across every industry, workflow, or enterprise environment.
The clearest re-rating signal is a name. Salesforce Sales Cloud now appears on G2 as Agentforce Sales, while Service Cloud and Marketing Cloud have also been renamed with the Agentforce brand. This positioning places the agent at the center of the product story instead of presenting AI as one feature inside a broader CRM. Before treating the rename as evidence of a new product, verify which capabilities changed, which reviews describe the current agent, and whether the current pricing, security controls, integrations, and implementation requirements match the new positioning.
Review highlights
Tommy Söderman, Co-founder of Lito Consulting and a Salesforce Marketing Cloud architect, wrote in September 2026 that “half of an Agentforce project is really a data project, and that should be said more clearly up front.” The observation applies beyond one vendor. An enterprise agent depends on the data model, permissions, process design, content quality, and controls around it. A sales team may purchase an agent for prospect research or follow-up automation, but the system still needs accurate records and usable context before it can produce dependable work.
Kasi Cheaney at Acima, an enterprise financial services company, reported that new administrators cut case age by 25 to 30% because they no longer had to wait for developers. This is a useful example of a measurable operational outcome, but it should not be generalized to every business. A seller should identify the baseline case age, define the tasks the agent performs, measure time saved, and disclose whether the result depended on a particular implementation team, data structure, or integration.
How reviews have changed
In 2023, sales reviewers mainly judged tools on how well they organized a representative’s own outreach. In 2026, reviewers increasingly judge agents on the work they can take off people’s plates. The evaluation has moved from individual productivity to delegation: can the agent find relevant information, prepare an account brief, draft an appropriate message, update the correct record, or route a task without creating more review work? Reviews now also discuss data readiness, model behavior, usage limits, security, and the time required to verify generated output.
What it means for software sellers
The AI SDR category remains a waiting room. Of its 400 listed products, 298 have no verified reviews, leaving a substantial proof gap. Sellers can gain visibility with a relatively small number of relevant, recent reviews, but those reviews must describe the AI capability itself. A new tag on an old product will not provide sufficient evidence if customers continue to discuss only email sequencing or conventional sales engagement.
Use a repeatable evidence checklist when collecting customer feedback. Ask reviewers to identify their company size, industry, sales motion, implementation period, and the specific agent or workflow they used. Then document the data required at launch, the tasks delegated, the actions requiring human approval, and the measurable result. Useful outcomes include account-research time, response time, qualified pipeline, case age, task completion, accepted recommendations, and the percentage of generated text that employees edit.
Recruit reviewers from the teams that operate the product every day: sales operations, revenue operations, administrators, managers, and executive sponsors. Reviews from companies similar in size and industry are more useful than generic testimonials. An enterprise deployment with extensive integration work may produce a different result from a small team using a lightweight app. Sellers should state that difference rather than present one customer story as universal proof.
Product messaging should make setup visible. Explain which records the agent can read, which actions it can take, what permissions it requires, where human approval remains necessary, and how administrators audit its work. If the product uses credits, usage limits, or a variable price per month, show how those limits affect common workflows. A free tier may support evaluation, while a pro tier or enterprise tier may be required for production integrations, models, administration, and security controls. Buyers need to know whether a successful pilot remains affordable as usage grows.
Describe the product’s technical boundaries with practical examples. State whether it can work with CRM records, files, email, web research, voice or audio inputs, and connected platforms such as Google Workspace. Explain how the agent handles context, what information fits within its context window, and how it responds when required data is missing. If the product supports coding or connects to other apps, identify the permissions and actions available to each user role.
Sellers should also demonstrate failure, not only success. Show what happens when the agent selects the wrong prospect, cites incomplete research, drafts unsuitable text, updates the wrong record, or reaches a usage limit. Provide audit logs, approval controls, model information, version history, and a method for reversing an action. The best tool for one sales team may be the product with the strongest CRM integration; another team may prioritize model control, context handling, automation, or auditability.
What it means for software buyers
Read the AI SDR category in two layers. First, filter for reviews written during the past 12 months by companies comparable to yours. Second, determine which reviews describe the agent and which were carried over from an older sales product. A high rating does not validate current AI functionality, and high review velocity does not equal technical accuracy. Treat category placement as a discovery signal, then verify the product in your own business process.
Use a 90-day pilot with one owner, one customer segment, one workflow, and a defined set of tasks. Before launch, record the baseline for preparation time, response time, employee editing time, qualified opportunities, errors, and task completion. During the pilot, require approval before the agent sends external messages, changes records, or takes other consequential actions. At the end of 90 days, compare the results with the baseline and include the data-cleaning, training, integration, and verification work in the assessment.
Ask three operational questions before signing: Who owns the agent day to day? What data does it need before it works? How will the team measure it after 90 days? Add questions about permissions, integrations, security, model behavior, escalation, and review requirements. Confirm whether the agent can explain why it selected a prospect, generated a recommendation, or changed a record. Check whether administrators can inspect the source context and correct an action without involving the vendor.
Model usage beyond the initial trial. Check the cost of credits, automated tasks, seats, connected apps, and the expected price per month for the number of users and records. Ask what changes between the free tier, pro plan, and enterprise tiers. Determine whether a context window is large enough for the account information the agent must process and whether the vendor supports the files, text, code, voice, or other data formats in the workflow.
The sales category is changing before many representatives encounter a new vendor. That gives teams an opportunity to improve their shortlist, but it also increases the risk of choosing a product because of its name rather than its evidence. Compare the current product with the inherited review history, run a controlled pilot, assign ownership, and require measurable outcomes before expanding automation.
2. Software development: The most crowded shelf with the least evidence
Software development produced eight new AI categories in two years, the highest total among the functions in this analysis: AI Coding Assistants, AI Software Testing Tools, AI App Builder, AI SRE Tools, AI Documentation Generators, AI AppSec Assistants, AI Engineering Accelerators, and AI SDK. The range covers coding, testing, documentation, application development, security, reliability engineering, and the platforms that connect models to developer workflows. It also shows how quickly a general coding assistant can expand into an engineering product.
AI Coding Assistants lists 369 products, including 328 with no verified reviews. That 89% proof gap means most of the category remains a claim rather than documented customer evidence. A crowded shelf does not necessarily indicate a mature market. Products may use similar language about copilots, agents, autonomous coding, or developer productivity while differing substantially in repository access, context, testing, security, model selection, and usage limits.
Autocomplete completes a line or predicts a short code fragment. A coding assistant can explain code, generate functions, edit files, and answer questions inside an IDE. A coding agent may plan and execute several tasks across a repository. An app builder can generate parts of an application, while an engineering platform may combine coding, testing, deployment, documentation, and governance. These categories overlap, but each requires a different evaluation. A product that performs well at autocomplete is not automatically ready to change a production codebase.
Cursor has 315 reviews, including 297 written during the past 12 months, producing 94% review velocity. GitHub Copilot has 394 rated reviews, with 238 in the same period, or about 60%. These figures show recent attention to coding tools, but they do not establish that Cursor, GitHub Copilot, or any other product produces secure, maintainable code in every repository. Review velocity measures the recency of buyer discussion, not code quality, defect rates, or developer productivity.
The audience represented in the reviews also matters. The source data indicates that 301 of Copilot’s lifetime reviews come from India, while 206 of Cursor’s 288 recent reviewers who disclosed company size work at firms with 50 or fewer employees. Those patterns may reflect genuine adoption, but they limit how confidently an enterprise team can generalize the results. Before choosing a tool, filter evidence by company size, development environment, industry, repository complexity, programming language, compliance requirements, and deployment model. The GitHub Copilot denominator should also be checked carefully because the source distinguishes rated reviews from total lifetime reviews.
Review highlights
Hitarth Anand Rohra, a software engineer at Matrix One, wrote in July 2026 that Cursor “feels like an IDE first and an AI tool second,” then said it sometimes makes broad changes when a small edit was wanted. That distinction matters when teams evaluate agentic coding. An assistant that understands repository context and proposes changes may be more useful than basic autocomplete, but broader actions require stronger review boundaries, version control, testing, and rollback procedures.
Muhammed Aamer, technical project manager at logistics firm CargoX, wrote that verification against real documentation “remains necessary rather than optional” because the assistant still invents library APIs. Fluent output can contain incorrect functions, outdated dependencies, insecure patterns, or APIs that do not exist. Developers need a workflow that checks generated code against current documentation, approved dependencies, automated tests, static analysis, and security controls.
Antonio Tirado Peña, director of IT and strategy at Instituto de Innovación Ciencia y Empresa, recovered the subscription cost in the first few days but flagged usage limits that only appeared after the fact. The example connects productivity with pricing. A tool may save time on routine coding, documentation, or testing while becoming more expensive when teams use higher-capability models, larger context windows, background agents, or additional credits. Buyers should calculate the cost of normal development work, not only the advertised entry price.
How reviews have changed
In 2023, developers mainly reviewed autocomplete: all 126 of GitHub Copilot’s reviews that year were filed under AI Code Generation, and reviewers praised suggestions that appeared as they typed. In 2026, they increasingly review something closer to a coworker. The question has shifted from “Can the tool complete this line?” to “Can the tool help plan, edit, test, document, and verify a change without increasing technical risk?” Reviewers now discuss how much work they can delegate, how they check model output, which tasks remain human-owned, and what the product costs when usage scales.
What it means for software sellers
The bar remains low: only 41 reviewed products out of 369 in AI Coding Assistants. Sellers should build evidence with professional development teams rather than relying only on individual or hobbyist use. Ask customers to describe the repository, languages, team size, development workflow, implementation period, and tasks completed. Report baseline and post-deployment results such as review-cycle time, defect rates, test coverage, documentation completion, issue-resolution time, developer satisfaction, and the amount of human editing required.
Explain the product’s boundaries before users encounter them. Publish clear information about usage limits, credits, model access, supported languages, repository indexing, data retention, training policies, context windows, and security controls. If a pro plan or enterprise plan changes access to models, agents, files, code execution, or team administration, describe those differences precisely. A free tier may support exploration but exclude the integrations, governance, context, or security features needed for production use.
Show how the product handles failure. Demonstrate what happens when the model invents a library API, misunderstands a request, makes a broad change, exposes an incorrect answer, or reaches a usage limit. Provide audit logs, permission controls, version-control integration, test integration, and a clear method for reversing an agent’s actions. For organizations with sensitive code, explain how the product isolates customer files and whether administrators can control repository, model, and external-service access.
Strong evidence should measure more than lines of code generated. Sellers can report changes in review-cycle time, escaped defects, test coverage, documentation quality, time to resolve issues, and human acceptance or editing rates. If the product supports app building, testing, or engineering automation, show the baseline and the post-deployment result. A coding tool may accelerate one task while adding review work elsewhere, so the complete workflow matters.
What it means for software buyers
Run a pilot on representative code rather than a sample repository chosen by the vendor. Test a matrix of tasks: write tests, update dependencies, fix a known bug, document an unfamiliar module, implement a small feature, and review a security-sensitive change. Include monorepos, private dependencies, legacy code, multiple languages, and the team’s existing code-review process where relevant. Measure both generation time and verification time; a faster first draft does not save time if developers spend longer repairing inaccurate output.
Evaluate repository context and the context window separately. Ask what the product can index, how it selects relevant files, how it handles stale documentation, and whether its context is sufficient for the codebase. Test whether the agent cites source files, follows project conventions, respects permissions, and preserves unrelated code. Require human approval for generated code, dependency changes, file deletion, deployment actions, and any commit or merge into a protected branch.
Request a transparent pricing model before the pilot begins. Ask what counts toward credits, whether background agents consume additional credits, how higher-capability models are priced, and what happens when the team reaches a limit. Compare the expected price per month with time saved, verification effort, defect risk, and administration. A free tier can help with exploration, while a paid tier or enterprise tier may be necessary for private repositories, team controls, larger context, and production integrations.
Finally, define a rollback and accountability policy. Developers remain responsible for reviewing generated code, checking dependencies, running tests, and assessing security. An agent should not silently merge changes, expose confidential files, or introduce unverified model output into production. The best tool is the one that fits the team’s codebase, platforms, controls, workflow, and risk tolerance—not necessarily the product with the highest review velocity.
3. Customer support: The largest AI category on G2
AI Customer Support Agents is the largest AI-native category in this analysis, with 897 listed products. Of those products, 678 have no verified reviews, creating a 76% proof gap. The category’s size shows intense supply, but it does not prove maturity or identify the best tools. Customer-support software now includes help-desk systems, chatbots, conversational apps, voice assistants, and agents that answer questions, classify requests, update records, route cases, and escalate work. Buyers need to distinguish a product that generates a response from one that can safely complete a business task.
The review comparison reinforces why dates matter. Agentforce has an 84% review velocity, while Fin has 3,911 lifetime reviews and only 346 from the past 12 months. Fin’s review history includes older help-desk and live-chat use cases, so its lifetime total describes a broader product history than its current AI-agent positioning. Low recent review velocity does not mean that Fin is a poor product; it means that buyers should identify which reviews describe the current model, automation, escalation behavior, and explainability.
Support automation has at least three levels. Assistive drafting suggests text for a human representative to review and edit. Knowledge-based answering responds to a customer using approved content, files, or a knowledge base. Action-taking automation uses an agent to complete tasks such as issuing a refund, changing an account, updating a ticket, or routing a case. Each level needs a different control model. Drafting emphasizes content quality and editing time; answering requires source attribution and uncertainty handling; action-taking requires permissions, audit trails, escalation rules, and human approval.
Channel evaluation also matters. A text workflow may perform differently from a voice or audio interaction. A voice agent needs accurate transcription, interruption handling, turn-taking, pronunciation, and a reliable transfer to a person. Audio workflows may require recording controls, speaker identification, and clear retention rules. A web chat app may have different permissions from an agent operating inside a CRM, while a video or messaging channel may introduce additional context and accessibility requirements. Buyers should evaluate each channel separately rather than treating every conversation as the same task.
What it means for software sellers
Sellers should collect fresh reviews about the current agent instead of relying mainly on legacy help-desk reviews. Ask customers to identify the queue, channel, business process, model-supported task, and outcome they evaluated. A useful review should state whether the product drafted text, answered from approved content, completed an action, or escalated the case. It should also identify the human role that remained in the workflow, because many teams achieve better results through agent assistance than through full automation.
Publish evidence for the metrics buyers now compare: resolution rate, escalation rate, case age, first-response time, time to resolution, customer satisfaction, answer accuracy, and the percentage of recommendations accepted or edited by representatives. Explain how the product retrieves information, identifies its sources, and handles uncertainty. If the agent cannot find a reliable answer, it should acknowledge the gap or route the case to a person instead of inventing a response. Source attribution is especially important for enterprise teams handling regulated data or sensitive customer accounts.
Make implementation visible before the demonstration. Describe the required integration with the help desk, CRM, knowledge base, messaging channels, voice systems, web properties, and other business platforms. Explain which content and files the models can access, how permissions are inherited, and whether customer data is retained or used for training. Show how administrators review conversations, correct the knowledge base, change escalation policies, and investigate an incorrect action. Also explain how the product handles context across channels and whether a conversation retains its history when transferred to a human.
Pricing should distinguish conversation volume, model calls, seats, channels, integrations, and automated tasks. State how many credits each workflow uses and what production usage costs per month. A free tier may support experimentation without including the features required for deployment. Explain what changes between lower tiers and enterprise tiers, including model access, voice or audio capacity, retention, administration, support, and security. Buyers need to understand the cost of scaling a successful pilot, not only the entry price shown on a pricing page.
Fresh reviews should come from support leaders, quality managers, administrators, and frontline representatives who operate the product every day. Sellers should report outcomes from a defined workflow rather than claim that one tool replaces the support organization. The strongest evidence shows how the product reduces case age or repetitive work while preserving customer choice, human accountability, and a clear escalation path.
What it means for software buyers
Start with one queue and a controlled pilot lasting long enough to capture normal variation, such as one quarter. Choose a defined, lower-risk request type with a clear volume pattern. Record the baseline before launch, then compare case age, first-response time, resolution time, reopening rate, customer satisfaction, escalation rate, answer accuracy, and representative editing time. Include cases where the source information is incomplete or the customer’s request falls outside the agent’s instructions.
Test the channels customers actually use. Compare text, voice, and audio interactions for transcription quality, response accuracy, interruption handling, context retention, source citations, and transfer quality. Check whether a web chat preserves the complete conversation when handed to a representative. Test the product with conflicting files, incomplete content, ambiguous requests, and a long interaction history. Ask what happens when the model reaches a context limit or cannot find a reliable answer.
Define permission boundaries before connecting the product to production systems. Give the agent only the access required for its assigned tasks, and require approval for high-impact actions where appropriate. Test refunds, account changes, identity checks, data deletion, and other sensitive workflows separately from low-risk information requests. Require an audit record for every action, including the input, retrieved content, response, tool call, approval, and final outcome. A successful demonstration does not prove that automation is safe at scale.
Finally, compare total pricing rather than the advertised plan alone. Ask how many conversations, model calls, seats, integrations, channels, and automated tasks are included each month. Determine whether the free tier or lower tier limits the features needed for the pilot, and calculate credits consumed by voice, audio, or high-volume workflows. The best tool is not necessarily the product with the largest category or the most impressive agent claim. It is the product that improves a defined customer workflow while giving teams enough visibility to correct errors.
4. Marketing and search: incumbents crowd into the new category
G2 created AI Search Visibility Optimization Tools and Answer Engine Optimization, along with AI Marketing Agents, AI Avatar Generators, AI Storyboard Generators, and AI Presentation Tools. These six categories extend AI from content creation into discovery, measurement, campaign planning, and creative production. They also show how established SEO and marketing vendors can enter an AI category without becoming entirely new products. The key question for buyers is which capability has changed: the product itself, the customer workflow, or simply the language used to describe familiar work.
Answer Engine Optimization, or AEO, focuses on how brands appear in responses generated by AI search and answer systems. Traditional SEO emphasizes rankings, organic traffic, backlinks, technical accessibility, and keyword performance. AEO adds questions about prompts, citations, brand mentions, source selection, answer context, and visibility across models. The disciplines overlap, but they are not interchangeable. A keyword-ranking tool may not track prompts, and a product that reports an AI answer mention may not show whether that mention produces qualified traffic, conversions, or business value.
Answer Engine Optimization lists 623 products, including 417 without verified reviews. Among 34 products with at least 40 reviews in AI Search Visibility, at least 18 are established SEO and content tools. That mix indicates incumbent crowding rather than proof that established products outperform specialized competitors. New entrants may offer prompt monitoring or citation tracking, while established platforms already have web indexes, keyword data, customer relationships, integrations, and reporting workflows. Their advantage may be distribution and accumulated data, not necessarily better AI.
Semrush has 4,035 reviews, with about 1,290 written during the past 12 months, including 373 filed under Answer Engine Optimization and 354 under AI Search Visibility Optimization Tools. These figures show that buyers are placing a mature product inside emerging categories. They do not prove that Semrush’s AEO capabilities are superior to every specialized tool. They show that users increasingly connect its research, content, and search work with prompts, AI visibility, and answer-engine measurement.
Measurement is difficult because AI answers vary by model, location, language, query wording, freshness, user context, and source availability. A brand may appear for one prompt but not another, receive a citation without a recommendation, or gain visibility without qualified visits. Teams should define whether visibility means a brand mention, a cited source, a favorable answer, a referral visit, a lead, or a conversion. Without that definition, a dashboard can create activity without proving marketing impact.
Review highlights
Pedro Martinez, who runs SEO for an agency’s clients, wrote in August 2026 that he now works with the product “primarily outside of its interface,” through chat tools and scheduled workers that call the API. That description points to a workflow change rather than merely a new feature. The product becomes part of an automated research and reporting system that receives data, processes it, and sends the result to another tool or team.
Pasha Leone, a small-business marketer, uses it to research “keywords + prompts” and track answer-engine traffic. This reflects the expanding scope of search work: marketers now monitor both the language people search and the prompts that shape an AI-generated response. A brand can maintain keyword visibility while appearing inconsistently in answers, or gain mentions without generating meaningful visits or conversions.
Jodie Maxwell, senior marketing executive at Xeretec Group, uses it to measure whether SEO and AEO spend is improving visibility. That is the business question behind the category. A dashboard can report mentions, citations, prompts, or rankings, but marketing teams still need to connect those metrics to qualified traffic, leads, revenue, and brand discovery. The product has not changed the category by itself; buyers have changed the job they expect it to perform.
How reviews have changed
In 2023, Semrush reviewers discussed keywords, backlinks, and site audits, and 136 of that year’s 212 reviews were filed under SEO Tools, with none under Answer Engine Optimization. In 2026, 373 of about 1,290 recent reviews sit under AEO, and reviewers describe prompts, AI visibility, and running the tool through their own agents. The change shows how established software can move into an AI category through customer behavior, product positioning, or both.
What it means for software sellers
If you sell SEO or content tools, you may already compete in the AI search category. Customers can place the product there through their review language even if the product team still describes it as a conventional SEO platform. Define AEO capabilities precisely: prompt tracking, answer monitoring, citation analysis, brand-mention measurement, visibility reporting, content recommendations, or workflow automation. Avoid using AEO as a vague label.
Disclose measurement coverage before making performance claims. State which models, search experiences, languages, locations, sources, and prompt types the product supports. Explain how often the system collects data, how it handles answer variation, and whether customers can export raw observations. Provide access to the API, historical data, source records, and integrations needed to validate the reporting independently.
Pricing should reflect the work marketers perform, not only the novelty of the category. State how prompt volume, tracked markets, model coverage, reporting frequency, API access, user seats, integrations, historical data, and automated tasks affect the price per month. A platform may cost more but reduce research and reporting work by combining several tools. A specialized app may be the better choice when a team needs one narrow capability with transparent pricing and a manageable workflow.
AI-native vendors should accept that incumbents usually have more reviews and broader customer relationships. Their advantage should come from evidence in a defined segment. Show how the product performs for a particular industry, market, language, prompt set, or content workflow. Report changes in visibility, citations, qualified traffic, and conversions rather than only the number of tracked answers. Demonstrate how the product handles differences between models and how marketers can investigate an unexpected result.
What it means for software buyers
Ask how the product measures brand visibility in answers and tracks prompts as well as keywords. Confirm which models, search experiences, languages, locations, and sources it covers. Determine whether it distinguishes a brand mention from a meaningful recommendation and whether it shows the content or citation that influenced the answer. Request raw exports or an audit trail so the team can validate the measurement outside the vendor’s interface.
Define the outcome before the demonstration. For one team, visibility may mean appearing in answers to high-value commercial prompts. For another, it may mean being cited by reliable sources, improving product information, increasing qualified web traffic, or supporting research. Compare the product’s workflow with the existing content, analytics, CRM, and campaign processes. Confirm that the app integrates with the platforms and reporting systems the business already uses.
Run a controlled pilot using a fixed prompt set, defined models, consistent locations, and a documented collection schedule. Record mentions, citations, answer position, source changes, referral traffic, conversions, and the time required to review recommendations. Repeat observations because AI answers vary by wording, freshness, context, and user. Before accepting a report, compare a sample of dashboard results with the underlying answer and source. A product that generates more suggestions may create more work if the team cannot prioritize or verify them.
Finally, compare total pricing and workflow fit rather than selecting a tool because it has an AI label. Check the cost per month, available tiers, user permissions, integrations, historical access, and the effort required to validate results. The best tool is the one that connects search visibility with a measurable business outcome—not necessarily the product with the largest category, the most models, or the newest agent claim.
5. Security and IT operations: Many new categories, little fresh judgment
Security and IT operations produced seven AI-native categories, including AI SOC Agents, AI IT Agents, AI Security Solutions, AI Security Posture Management, AI Governance Tools, Autonomous Endpoint Management, and Non-Human Identity Management. The category count signals rapid investment in incident response, governance, identity, endpoint control, and IT service automation. It does not show that buyers are ready to delegate high-risk decisions to autonomous agents or that every listed product can perform safely in an enterprise environment.
AI SOC Agents lists 52 products, while AI IT Agents lists 146. Splunk Enterprise Security has only six reviews from the past 12 months out of 248 lifetime reviews. These figures provide less fresh public judgment than the leading sales, coding, support, and marketing categories. Low review velocity may reflect an early market, longer procurement cycles, confidentiality requirements, or the reluctance of security teams to publish details about incident-response work. It should not be treated as evidence of low product quality or weak demand.
Security automation requires different standards for different tasks. An agent that enriches an alert, summarizes an incident, retrieves evidence, or recommends a response is not equivalent to one that changes access, isolates an endpoint, disables an account, or closes an alert. Teams should begin with low-risk information workflows and evaluate action-taking automation separately. For every use case, define the cost of a false positive, the cost of a false negative, the required human approval, and the rollback process.
Context also affects reliability. A model may produce a useful summary from structured alerts but fail when files are incomplete, text is misleading, code is malicious, or web data is stale. Buyers should test how agents identify missing information, cite evidence, resist prompt injection, handle conflicting signals, and limit access to connected platforms. A security product needs more than a confident response: it needs traceability from the input and retrieved data to the recommendation and final action.
What it means for software sellers
Sellers should position an agent alongside existing SIEM and SOAR systems rather than imply that it replaces the security stack. Explain which data sources the product reads, how it integrates with current platforms, which tasks it can perform, and where a human must approve an action. Provide evidence from realistic environments, including alert volume, data quality, response time, false-positive rates, false-negative risk, analyst workload, and the time required to verify a recommendation.
Disclose how the product handles uncertainty and adversarial input. Explain whether it uses one or more models, retrieval from customer files, deterministic rules, or controls layered around model output. Demonstrate resistance to prompt injection, malicious content, stale indicators, unauthorized instructions, and attempts to trigger actions outside the user’s permissions. Document data retention, model training, tenant isolation, audit logs, access controls, and the process for reversing an automated action.
Pricing should match the operational model. State whether customers pay by events, data volume, seats, integrations, model calls, automated tasks, or credits, and show the expected cost per month for a representative deployment. Explain what changes between each tier, including retention, analyst seats, automation limits, model access, support, and enterprise controls. A low entry price can be misleading if production usage generates additional event or model charges.
What it means for software buyers
Start with alert enrichment, evidence retrieval, or triage rather than automatic containment. Select a low-risk workflow in which an analyst can verify the result quickly. Establish a baseline for false positives, time to verdict, analyst workload, and escalation quality. During the pilot, record how often analysts accept, reject, or correct the agent’s recommendation. Do not expand from enrichment to access changes, endpoint isolation, or account disabling until the team understands the product’s failure modes.
Test permissions, integrations, audit logs, and escalation paths before connecting the tool to production systems. Confirm that the agent can cite the evidence behind a recommendation, identify missing context, and transfer the case to a human. Run adversarial tests with incomplete files, misleading text, stale indicators, prompt injection, unauthorized requests, and attempts to access restricted systems. Verify that the product cannot bypass established controls simply because an instruction appears in retrieved content.
Review the complete workflow and total pricing with security, IT, legal, and procurement teams. Confirm how the product stores logs, protects sensitive data, supports rollback, and separates customer environments. Model the cost of events, seats, model calls, integrations, and credits at expected production volume per month. Combine the vendor’s claims with independent research, documentation, pilot results, and internal expertise. In this category, cautious deployment can demonstrate responsible governance rather than weak demand.
6. HR and recruiting: The category exists, the incumbents have not moved
AI Recruiting, AI Interview Agent, and AI Agents for HR appeared during the past two years. AI Recruiting lists 528 products, including 415 without verified reviews, leaving a 79% proof gap. The category covers recruiting assistants, scheduling apps, sourcing tools, screening products, interview systems, and workflow agents that automate parts of the hiring process. These products do not perform the same tasks or carry the same legal and operational risk.
A recruiting assistant may draft text, summarize candidate information, or recommend next steps for a human. A workflow agent may schedule interviews, send messages, update records, or coordinate tasks across HR platforms. A system of record stores applicant and employee data, preserves process history, and supports reporting. A product can combine these functions, but a new AI label does not prove that it makes fair, explainable, or legally defensible decisions.
Paradox has a 57% review velocity on a base of 90 reviews. Greenhouse collected 652 reviews during the past 12 months, or 16% of its 4,002 lifetime reviews, and none was filed under an AI category in the source data. These figures show different positioning, not a definitive quality ranking. Paradox’s recent activity may indicate attention around a conversational hiring product, while Greenhouse continues to collect reviews under established categories such as applicant tracking and recruitment platforms. The absence of AI-category reviews does not prove that Greenhouse has not changed its product; it means the available review evidence does not isolate those capabilities.
Recruiting is being re-rated at the edges, while systems of record often retain their established labels. Scheduling and administrative communication are generally easier to test than automated candidate ranking because they involve less judgment about who advances. Screening, interview scoring, and recommendation workflows require stronger controls for bias, accessibility, explainability, privacy, retention, and human review. Buyers should also examine how the product handles text, voice, audio, and video interview data, including files, transcripts, recordings, and the context used to generate a recommendation.
HR automation can affect who receives an invitation, who is screened out, who reaches an interview, and who receives an offer. Sellers and buyers should check applicable employment laws and regulatory guidance with qualified legal and compliance teams. They should also document the model or models used, input data, decision path, confidence signals, user approvals, candidate notices, appeal options, and correction process. A low-cost app or free tier may support exploration, but it does not remove the governance required for production use.
What it means for software sellers
Sellers should collect reviews about the AI capability itself and document where customers file them. Explain whether the product drafts messages, schedules interviews, ranks candidates, summarizes conversations, or takes another action. Identify the data used, the model’s role, the human approval point, the audit record, and the available appeal process. If the product sends text, voice, or video communications, disclose how consent, accessibility, retention, and candidate access are handled.
Lead with a narrow, defensible use case rather than promising fully autonomous recruiting. High-volume scheduling, interview coordination, and administrative communication are usually easier to measure than candidate ranking. Show how the product integrates with the customer’s system of record, identity controls, assessment tools, reporting platforms, and other apps. Explain pricing, data residency, retention, permissions, model training, support, and integration requirements. If credits, seats, tasks, or usage affect the price per month, state the expected cost at realistic hiring volume.
Reviews should come from recruiters, hiring managers, HR operations, administrators, and compliance teams that use the product in daily work. Ask reviewers to describe hiring volume, candidate population, implementation time, workflow, and measurable outcomes. Report time saved alongside error rates, candidate experience, completion rates, human override rates, and discrepancies across relevant groups. A reduction in recruiter workload is useful only when the process remains fair, transparent, accessible, and accurate.
What it means for software buyers
Keep the system of record in place and begin with scheduling or another administrative workflow before testing screening or ranking. Assign an owner, define the agent’s permissions, and require human approval for consequential actions. Establish a baseline for completion time, no-show rate, recruiter workload, candidate satisfaction, and correction requests. Then measure whether the product improves the workflow without creating hidden review or compliance work.
Run bias and accessibility checks using the organization’s own applicant pool before launch and at regular intervals afterward. Test incomplete applications, unusual names, accessibility needs, multiple languages, nontraditional career paths, and requests for human assistance. Compare outcomes across relevant groups, investigate differences, and document remediation. Confirm that candidates receive appropriate information about automated processing and can reach a person when required.
Ask how the product handles text, voice, audio, and video interview data. Confirm what files are stored, who can access them, how long they remain available, whether they are used to train models, and how the product protects sensitive information. Review the context supplied to the model and the integration with existing HR platforms before connecting production records. Compare pricing across the free tier, paid tiers, and enterprise plan, including seats, credits, storage, automation, and support.
Incumbents are re-rating themselves
The expected story is that startups displace incumbents. The evidence often shows incumbents changing names, tags, and positioning instead. A vendor may rename a flagship product, add an AI feature to an established platform, or re-tag an existing listing so buyers searching for agents can find it. These moves can change the category map without proving that the underlying product has become more capable.
Examples in the G2 catalog include monday AI Work Platform, GTM Workspace powered by ZoomInfo, UiPath Agentic Automation, and Gemini Enterprise Agent Platform. Intercom’s listing now leads with Fin. Product names, packaging, and category assignments can change, so each example should be verified against the current vendor and G2 pages. A platform may contain several apps, agents, models, and automation features while continuing to support the same core business process.
Of the 16 products with 150 or more reviews in AI SDRs, at least seven predate the category: monday, Outreach, Close, Qualified, Reply, LivePerson, and Conversica. Their presence demonstrates how established tools bring accumulated buyer memory into a new category. It does not show that older reviews evaluate an autonomous sales agent. A review about email tracking, CRM integration, or conventional outreach should not automatically count as evidence for new AI functionality.
Three in four products in G2’s AI-native categories have no verified reviews yet.
This “three in four” statement must be tied to the correct analyzed denominator; it should not be presented as a universal statistic unless the underlying data supports that scope. A category with thousands of reviews may contain borrowed memory from products that earned those reviews doing something else. Buyers should examine dates, use cases, company size, implementation details, and whether the review describes an AI capability or an older product.
The decision framework is simple: rename when the AI product genuinely is the product now; re-tag when customers already use the AI label; and hold when buyers have not moved their search. In every case, preserve the distinction between inherited product history and fresh evidence. Category placement can improve discovery, but it cannot replace proof of performance, responsible automation, or market leadership.
What to do before your next decision
Use AI software categories in 2026 to discover products, not to validate them. Category placement can reveal where vendors and buyers are changing the market, but it cannot replace a pilot, reference checks, security review, or a business case. Before shortlisting any product, compare current evidence with the work your team needs it to perform.
Apply these questions to each product, not only to the category overall:
- How recent are the reviews, and do they describe the current product, model, agent, or automation rather than an older version or renamed listing?
- Do reviewers work in companies with a similar size, industry, risk profile, and technical maturity? Evidence from a small team may not predict how an enterprise team will use the same tool.
- How many reviews describe the AI capability itself? Look for evidence about the tasks, workflow, integrations, permissions, implementation, and measurable outcomes you plan to evaluate.
Budget for the full implementation project, not just the advertised price. Total cost of ownership may include data cleanup, integration, training, administration, security review, user seats, verification time, and ongoing support. Calculate credits, model usage, storage, and automation costs at expected production volume per month. A product that appears inexpensive may cost more when usage grows, when the team needs a higher tier, or when employees spend significant time checking its output.
Evaluate the complete workflow rather than one impressive feature. Ask whether the app supports the required text, voice, video, audio, code, research, and content tasks. Check its context, context window, supported models, files, integrations, security controls, and user administration. A coding tool such as Cursor or Claude Code may suit one development workflow but fail to meet the governance requirements of a regulated enterprise. A search product such as Perplexity may support research while requiring separate validation for business-critical content. A tool connected to Google Workspace may save time only if it respects the organization’s permissions and governance rules.
Run a controlled pilot with a named owner, a defined use case, representative data, approval rules, and baseline metrics. Measure time saved, output quality, correction rates, task completion, error rates, and the work required to maintain the workflow. For an agent, document which actions it may take independently and which require human approval. For coding tools, review generated code, dependencies, tests, and rollback procedures. For voice, video, or audio systems, assess privacy, accessibility, transcription quality, retention, and escalation.
Compare pricing across the free tier, pro plan, and enterprise tiers. Confirm what each tier includes for models, agents, files, integrations, administration, support, and security. Ask how many users, tasks, model calls, credits, or workflows are included each month and what happens when the organization exceeds those limits. Pricing should reflect the complete operating model, including the time teams spend reviewing outputs and correcting errors.
If you sell software, use the same framework to build evidence. Collect recent reviews from relevant users, describe the actual product and use case, disclose implementation requirements, and report measurable results. Explain how the product handles data, context, permissions, integrations, and failure. Twenty generic testimonials are weaker than a smaller set of detailed reviews from teams that resemble your target customers. Clear evidence helps buyers distinguish a useful product from a well-positioned category label.
Frequently asked questions
Which software categories are seeing the biggest shifts due to AI in 2026?
Sales and GTM is changing fastest in this analysis, followed by software development, customer support, marketing and search, security and IT operations, and HR and recruiting. Software development created the most new categories, while customer support has the largest flagship AI category by listed products. The ranking describes category movement and recent evidence, not a universal list of the best tools. Teams should still compare use cases, pricing, security, integrations, governance, and measurable work completed.
How can I tell whether an AI software category is a real market or just marketing?
Start with the proof gap, then examine review dates and whether current reviews describe AI-native functionality rather than an older product. Compare listed products with relevant reviews, and check whether reviewers represent companies similar to yours. Look for repeated use, implementation detail, workflow fit, pricing transparency, and outcomes. A category with many products but little current evidence may be an emerging market or mainly a positioning exercise. Category discovery is useful, but product validation requires independent research, a controlled pilot, and a clear business result.
Should a software vendor rename or re-tag its product into an AI category?
Rename when the AI capability is now the core product, re-tag when customers already use the AI label, and hold when buyers have not moved their search. A rename or tag can improve discovery, but it does not change product quality or convert older reviews into proof of new functionality. Vendors should collect fresh reviews, explain the agent and models, document permissions and integrations, and disclose pricing. Buyers should test the current product before treating category placement as evidence.
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