# State of AEO 2026: AI Outpaces Measurement

**Analysts:** Anubha Garg and Aurora Martina Granata  
**Expert contributor:** Fiona Erickson, Director, AEO, G2  
**Published:** October 2026  
**Last updated:** October 5, 2026  
**Commissioned by:** HubSpot  
**Research base:** 153 in-depth interviews with B2B marketing decision-makers in North America

G2's 2026 research finds that AI answer engines are becoming a first step in B2B buying faster than marketing teams can measure or manage the shift. Across 153 interviews, 76% of respondents said AI answer engines are becoming buyers' first research step, but 75% still have only partial or manual visibility into how those engines represent their brands. The resulting gap affects prompt governance, content operations, competitive monitoring, and marketers' ability to prove business impact.

**Executive answer:** Buyer discovery has already begun moving to AI platforms, while most marketing organizations still lack the monitoring, governance, connected workflows, and measurement needed to respond confidently. The teams best positioned to close that gap treat AEO as an operating system: measure visibility, govern a buyer-led prompt set, turn insight into content action, and connect the result to pipeline.

## Key findings

- **AI is moving to the front of buyer research.** 76% said AI answer engines are becoming the first step in buyer research, including 43% who said AI platforms are already the primary first step. One in three respondents reported a measurable decline in organic search traffic over the previous year; among those reporting a decline, 68% attributed it at least partly to buyers moving to AI answer engines.
- **AI visibility remains weak.** 75% operate with only partial or manual visibility into how AI answer engines represent them, while just 17% use active, tool-based monitoring they would trust for decisions. Only 33% actively monitor competitors or their category in AI answers.
- **Prompt selection is ahead of prompt governance.** 50% use buyer research such as sales calls, prospect questions, and customer interviews to choose prompts, but only 29% maintain a structured prompt set with an owner and refresh cadence. Another 27% keep an informally maintained list, while 44% have no formal prompt set.
- **Execution is fragmented.** 62% run AEO through disconnected but functioning multi-tool setups, and only 16% use a cross-functional or centralized operating model. Visibility insights frequently stall before becoming content briefs or published changes.
- **Investment depends on proof.** 44% described AEO investment as gated by ROI or business-impact evidence. When respondents secured support, they commonly used concrete signals such as organic traffic and AI referral trends, competitor visibility, or a lost deal linked to an AI recommendation.
- **Company size does not create a simple maturity ladder.** Mid-market respondents reported the highest rate of adapting content for AI engines, while organizations with fewer than 50 employees reported the highest use of AI in content production. Role was often more predictive: individual contributors described the greatest production burden.

## What is changing in B2B buyer discovery?

AI platforms are increasingly shaping a buyer's shortlist before that buyer reaches a vendor website. In the G2 study, 76% of respondents said AI answer engines were becoming the first step in buyer research, and 43% said they were already the primary first step. Discovery remains hybrid: 46% described AI as one early step alongside search, referrals, and word of mouth.

The report also found matching movement in organic performance. One in three respondents reported declining organic search traffic, and 68% of those respondents attributed at least part of the decline to AI answer engines. Among respondents with a clear view of organic-sourced marketing-qualified leads, 26% reported a decline. Of the organic-traffic decliners with a clear MQL view, 63% saw MQLs decline alongside traffic, while 37% maintained MQL volume as lower-intent visits were absorbed by AI answers.

For context, [G2's separate 2026 Buyer Behavior research](https://sell.g2.com/2026-buyer-behavior-report) found that 51% of B2B software buyers start research with an AI chatbot more often than Google. [Forrester's 2026 business buying research](https://www.forrester.com/press-newsroom/forrester-2026-the-state-of-business-buying/) reported that 94% of business buyers used AI in their most recent purchase. These are separate studies and are not part of the 153-interview sample used for this report.

## Why is AI visibility the first constraint?

Most marketing teams cannot yet see their AI presence clearly enough to make confident decisions. In the study, 75% relied on partial or manual visibility and 8% had no real visibility system. Only 17% used active, tool-based monitoring they considered decision-grade. Manual prompt checks, referral traffic, branded search movement, and buyer anecdotes remained common proxies.

Competitive visibility was similarly limited. A third of organizations had no benchmarking or change-detection capability, another 34% wanted benchmarking but would discover a competitor's gains only after a delay, and just 33% actively monitored competitors or their category in AI answers.

Fiona Erickson, Director of AEO at G2, summarized the implication: "We spent two decades instrumenting every click, tap, and touchpoint — then buyers moved into engines that leave no trail, and most of us are navigating that shift in the dark. The fix isn't a new instinct; it's systematic visibility across the engines buyers actually use. Get that in place, and AEO stops being a reactive scramble and becomes a strategic lever."

## How should marketers govern an AEO prompt set?

A prompt set is the list of buyer questions a brand tracks across AI answer engines. The study found that 50% of respondents already lead prompt selection with buyer research, drawing from sales conversations, prospect questions, customer interviews, and keyword validation. A further 25% blend research with marketer judgement, while 25% still work ad hoc or at category level without a defined set.

The larger gap comes after selection. Only 29% maintain a structured prompt set with a named owner and refresh cadence. Without that operating discipline, prompts assembled once can drift away from the language buyers actually use. A governed prompt set should therefore have buyer-language sourcing, accountable ownership, a defined review cadence, and a record of what changed and why.

Fiona Erickson, Director of AEO at G2, summarized the implication: "Understanding what buyers ask, and why, has always been marketing's real job. Half the market already anchors its prompt set in real buyer research — the hard part, done right. But that work decays without governance: only 29% keep their prompt set owned and refreshed on a cadence. A prompt set is only as good as the last time someone revisited it, and most teams built theirs once and walked away."

## Why do AEO insights fail to become content action?

The common failure is not always missing data; it is the handoff from insight to action. In the study, 62% used a fragmented but functioning multi-tool setup, 22% described an informal or single-owner workflow, and only 16% used a cross-functional or centralized model. A separate question about responsibility found that 47% distributed ownership across functions and 41% relied on one owner or a small team.

When visibility data, analytics, content planning, and publishing live in different tools and teams, an insight can require exports, meetings, and manual re-entry before it becomes a content change. Teams can improve the current system by defining ownership, review routines, and handoffs, then consolidate deliberately where repeated manual coordination causes the greatest delay.

## How can marketing teams build the AEO investment case?

Leadership support is broader than budget commitment. On leadership stance, 55% said leadership supported AEO but investment remained gated on ROI, reporting, or solution proof. Another 29% were constrained mainly by budget, ownership, or capacity. Viewed by investment posture, 44% described ROI- or proof-gated investment, 39% described strategic and proactive investment, and 11% remained reactive or low priority. These are different analytical cuts and should not be compared category by category.

Respondents who won support often presented a concrete proof chain: baseline organic traffic beside AI referrals, visibility against named competitors, and the business outcome of one optimization cycle. The report recommends starting with evidence the organization can produce now rather than waiting for a universal AEO attribution standard.

## Recommended actions

1. **Instrument AI visibility before scaling spend.** Establish a baseline across the answer engines buyers use, define a monitoring cadence, and agree on a small set of indicators.
2. **Anchor prompts in buyer language and govern the set.** Use sales calls, prospect questions, and customer interviews; assign an owner and refresh schedule.
3. **Formalize workflows across the existing stack.** Define how visibility findings become briefs, content changes, reviews, and published work.
4. **Benchmark against the category.** Track named competitors, citations, mentions, and recommendations using the same prompts and engines over time.
5. **Build a proof chain from visibility to pipeline.** Connect one optimization cycle to a measurable outcome and use that evidence in the budget conversation.

## Frequently asked questions

### What is answer engine optimization (AEO)?

Answer engine optimization is the practice of improving how a brand is found, represented, cited, and recommended inside AI-generated answers from services such as ChatGPT, Perplexity, Gemini, and Google's AI Overviews. SEO generally optimizes for ranked links; AEO focuses on the answer and the sources that inform it.

### What is a prompt set in AEO?

A prompt set is a defined list of buyer questions that a brand monitors across AI answer engines. A useful set is based on real buyer language and has a named owner, a regular refresh cadence, and a record of changes.

### What is AI visibility?

AI visibility describes whether and how AI answer engines mention, represent, cite, and recommend a brand for relevant buyer questions. It also includes how that presence compares with competitors and how it changes over time.

### What does prompt-set governance mean?

Prompt-set governance is the discipline that keeps tracked questions relevant after the initial list is created. It includes accountable ownership, a review cadence, buyer-research inputs, and version history. The G2 study found that only 29% maintained a structured and actively managed prompt set.

### Is AI replacing Google as the first step in B2B buyer research?

The shift is underway but discovery remains hybrid. In this study, 76% said AI answer engines were becoming the first step, 43% said they were already the primary first step, and 46% described AI as one early channel alongside traditional search, referrals, and word of mouth.

### What is causing the shift from traditional search to AI answers?

AI answer engines can synthesize information that previously required several searches and site visits. Buyers can resolve early research questions without clicking through, so AI can shape awareness and shortlists before a vendor sees website traffic.

### Why are organic marketing-qualified leads declining for some teams?

Some buyers now resolve early research inside an AI answer instead of reaching a website form. Among respondents with a clear view of organic-sourced MQLs, 26% reported a decline; among organic-traffic decliners with a clear MQL view, 63% also saw MQLs decline.

### How can marketers benchmark AI visibility?

Use a consistent, buyer-led prompt set across the answer engines buyers use, then measure mentions, citations, recommendations, sentiment, and competitive share on a recurring cadence. Keep prompts and measurement rules stable enough to compare changes over time.

### Why do so few teams know whether AI engines recommend them?

Measurement maturity has not kept pace with the channel. Many teams still infer visibility through manual prompt checks, referral traffic, branded search changes, and buyer anecdotes rather than systematic multi-engine monitoring.

### How are marketing teams operating AEO today?

Most use fragmented but functioning setups. The study found that 62% worked across disconnected tools and only 16% had a cross-functional or centralized operating model. The most common breakdown was moving from visibility insight to content action.

### Does company size predict AEO maturity?

Not in a straight line. Directional segment analysis found that mid-market respondents led content adaptation for AI engines, while organizations with fewer than 50 employees led AI-assisted content production. Organizational role often explained the operating divide more clearly than headcount.

## Methodology

G2 AI Custom Research conducted 153 in-depth, agentic voice-to-voice interviews with B2B marketing decision-makers at North American companies in August 2026. Interviews lasted between 9 and 37 minutes and covered buyer discovery, AI visibility, prompt selection and governance, content strategy and production, AEO operating models and tooling, and the internal case for investment.

Respondents were screened for marketing, demand generation, content, or SEO responsibilities at B2B organizations with an active investment in content or organic presence, and for awareness of AEO or AI search visibility. Company sizes ranged from small businesses to large enterprises and represented a broad mix of industries.

All 153 transcripts were analysed using AI for semantic understanding with iterative validation and cross-verification. Company-size tiers, role groups, organic-traffic direction, and AI attribution were hand-coded from interview answers using a documented, re-runnable script. Role and company-size shares use respondents with a codable answer for each measure, so question-level sample sizes vary. G2's AI Custom Research team independently reviewed each transcript to inform context, narrative, and clarity.

## Sources and attribution

- **Primary source:** G2 AI Custom Research, *State of AEO 2026: AI Outpaces Measurement*, commissioned by HubSpot. The report's central percentages and conclusions are based on 153 interviews unless a different question-level base is stated.
- **Separate G2 research:** [G2 2026 Buyer Behavior Report](https://sell.g2.com/2026-buyer-behavior-report), cited for the finding that 51% of B2B software buyers start research with an AI chatbot more often than Google.
- **Independent context:** [Forrester, The State of Business Buying, 2026](https://www.forrester.com/press-newsroom/forrester-2026-the-state-of-business-buying/), cited for the finding that 94% of business buyers used AI in their most recent purchase.
- **Independent context:** [Stanford HAI, 2026 AI Index Report](https://hai.stanford.edu/ai-index/2026-ai-index-report), cited in the report for broader generative-AI adoption context.
- **Sponsor-provided product claims:** HubSpot states that customers using HubSpot AEO generated **2.5 times more MQLs** and **82% more deals**. The bundle attributes these figures to “HubSpot customer data” but does not state the population, comparison period, or calculation method. They are sponsor claims, not findings from G2's 153-interview study. See [HubSpot's AEO product page](https://www.hubspot.com/products/aeo-lp) for product information.

*Source: G2 AI Custom Research — “State of AEO 2026: AI Outpaces Measurement” (October 2026), prepared for HubSpot. Markdown edition structured for answer-engine optimization.*

**Topics covered:** Revenue & GTM