The headline statistic is worth anchoring to before anything else: Google AI Mode has reached 75 million users, and ads now appear in 25% of AI Mode query responses. That combination — massive user scale and a material ad inclusion rate — means AI Mode is no longer a product experiment. It is a live ad surface that B2B SaaS advertisers are either showing up in or not, based on their existing campaign assets and account health, whether they realise it or not.
Most of the advertiser conversation about Google's AI products has focused on AI Overviews and the well-documented CTR suppression on standard search pages. That is a real problem and one covered in our post on AI Overviews' impact on B2B SaaS paid CTR. AI Mode is a different mechanism. Rather than sitting on top of the standard SERP and competing for attention with existing ad placements, AI Mode is a separate conversational search environment where ads appear within the generated response itself. Understanding the difference determines how you respond to each.
What Google AI Mode is and how it works
Google AI Mode is a dedicated tab in Google Search — accessible alongside the All, Images, News, and Shopping tabs — that provides a conversational, AI-generated answer experience powered by Gemini. Unlike standard search, which returns links, AI Mode synthesizes information from across the web into a direct answer, supports multi-turn follow-up questions, and can access user-specific information from Gmail and Calendar for logged-in Google accounts. For product-related queries, it connects to Google's Shopping Graph.
The user behaviour in AI Mode is structurally different from standard search. Standard search is best understood as a navigation tool — users enter a query, scan results, and click through to a destination. AI Mode is more like a research assistant — users ask questions, get synthesized answers, and follow up conversationally within the same session. That behavioural difference matters for advertisers because the intent signals and engagement patterns of a multi-turn conversation differ from the single-query, click-to-site model that Google Ads has been optimised for over two decades. A user who asks three follow-up questions about enterprise project management software in a single AI Mode session has a very different intent profile from a user who searches the same query once and clicks the first result.
How ads appear in AI Mode: the 25% figure in context
The 25% ad inclusion rate in AI Mode is not a hard threshold — it is an average across query types, and the variance is significant. Commercial and transactional queries have meaningfully higher ad inclusion rates than purely informational ones. A query like "best project management software for remote engineering teams with Jira integration" is more likely to trigger ad placements within the AI Mode response than a query like "how does kanban work." The intent signal is the determining factor, the same way it is in standard search — but in AI Mode, the query context is often richer because the user has provided a more natural, multi-faceted question rather than a keyword string.
The placement experience is also different from standard ads. Rather than appearing as a distinct ad block above or below organic results, AI Mode ads are presented contextually within or alongside the conversational answer. The exact presentation format has been evolving since launch — Google is still determining the optimal integration between generated answers and commercial placements — but the operational implication for advertisers is that the ad is being served into a higher-engagement context than a traditional SERP placement. A user reading an AI-generated answer about software options and seeing an ad for a relevant product within that context has already been pre-qualified by the question they asked. The click-through volume is lower than standard search because AI Mode is used by fewer people, but the intent quality of the user who does click may be higher.
Why B2B SaaS is a natural fit for AI Mode ad placements
B2B SaaS buying behaviour maps onto AI Mode usage patterns more cleanly than most categories. Enterprise software evaluation is inherently research-intensive: buyers spend weeks or months gathering information, comparing options, reading reviews, and building a business case before a purchase. That research behaviour — multiple questions, follow-ups, synthesis across sources — is exactly what AI Mode is built for. The buyer who has found their way to AI Mode to research enterprise software is doing the kind of deep evaluation work that correlates with eventual purchase, not casual browsing.
The implication is that B2B SaaS advertisers have a natural reason to be present on this surface. The challenge is that AI Mode requires a different kind of ad eligibility than standard search. The system selects ads based on relevance to the conversational query, and the queries in AI Mode are often longer, more specific, and more naturally worded than the keyword strings standard search is built around. A keyword portfolio tuned to match short commercial queries may not generate strong signals for the longer, research-oriented queries that dominate AI Mode. The intent-tier campaign structure used in standard B2B SaaS paid search maps onto AI Mode placement eligibility, but the weighting shifts toward mid-funnel, research-oriented content rather than bottom-funnel transactional keywords.
Asset requirements: what you need to qualify for AI Mode placements
Advertisers do not create separate AI Mode campaigns. Eligibility for AI Mode ad placements comes from existing Search and Performance Max campaigns, and the selection mechanism favours accounts with strong responsive search ad assets and high-quality landing pages. The most important preparation is improving the specificity and breadth of responsive search ad headlines and descriptions.
Generic headlines — "Try Free", "Book a Demo", "Award-Winning Software" — are less likely to be selected as relevant to a conversational AI Mode response than specific, benefit-led copy that matches the language of a research-stage query. A headline like "Kanban and Scrum for Remote Engineering Teams" is more likely to surface for a relevant AI Mode query than "Project Management Software." The direction is toward more headlines that speak to specific use cases, job roles, pain points, and outcomes — the same specificity that produces high Quality Scores in standard search, amplified. Audit your responsive search ad asset strength in the recommendations panel and push every ad group toward "Excellent" asset strength; below "Good" should be treated as a maintenance priority, not a nice-to- have.
Landing page quality is the second factor. AI Mode's selection system has access to page content signals when assessing relevance. Pages with shallow content, thin copy, or generic messaging are disadvantaged. B2B SaaS landing pages that contain specific use case content, credibility signals like customer logos and case study references, and detailed feature and integration content are more likely to be assessed as high-relevance for a research-oriented query. This is consistent with what good landing page practice already recommends — AI Mode eligibility is an additional reason to invest in landing page depth, not a new kind of page to build.
Conversion tracking for AI Mode placements
AI Mode placements generate standard clicks that flow through the same attribution pipeline as regular Google Ads clicks. The GCLID is appended to the destination URL, conversions are recorded against the originating campaign, and Smart Bidding learns from the conversion outcomes. The tracking setup is not different — but the timing of the conversion may be.
A user who finds a B2B SaaS product through an AI Mode ad during a deep research session may not convert immediately. They are likely in the evaluation phase, not the purchase phase, and the conversion event — demo booking, trial signup, sales call — may happen days or weeks later through a different channel or direct navigation. This is a version of the same problem the conversion window discussion addresses: Google Ads needs to attribute the conversion to the AI Mode click that initiated the evaluation, not just to the final session. The same offline conversion import setup and extended conversion window settings that fix standard B2B SaaS attribution apply here. If your conversion tracking is clean and complete, AI Mode placements are measured correctly. If it is not, the attribution gap is the same problem it always was, just on a newer surface.
How Performance Max interacts with AI Mode
Performance Max campaigns are the most likely vehicle for automated AI Mode placement delivery, because PMax is already designed to serve across Google's inventory surfaces using Google's automated asset selection. An account running Performance Max with strong asset groups — multiple headlines, descriptions, images, and video assets — is already configured for multi-surface delivery, and AI Mode placements are a natural extension of that. The caveat is reporting: as of mid-2026, AI Mode placement-level reporting within Performance Max is limited. Advertisers can see overall PMax performance but not always which specific surfaces drove which conversions within a campaign.
This reporting limitation matters for B2B SaaS accounts trying to evaluate the contribution of AI Mode placements specifically. The practical response is to monitor branded search volume trends alongside PMax performance — if AI Mode is driving awareness and consideration among new audiences, branded search queries tend to increase as a downstream signal. Qualified trial signups and demo requests that originate from users with no prior touchpoint in remarketing audiences are another indicator. The performance measurement sophistication for AI Mode is still developing; the right posture is to maintain strong asset quality and clean conversion tracking, and measure the broader demand generation impact rather than trying to isolate AI Mode placements specifically at this stage.
What B2B SaaS advertisers should do now
The priority actions are grounded in existing best practices, not new ones. First, audit responsive search ad asset strength across all ad groups and push toward "Excellent" ratings with specific, use-case-driven headlines. Second, ensure landing pages have substantive, specific content that serves a research-oriented visitor — not just conversion-rate-optimised pages designed to drive immediate form submission. Third, confirm conversion tracking is complete and that GCLID capture is working across all forms and entry points, so that AI Mode-initiated journeys are attributed correctly when they eventually convert.
The forward-looking action is to monitor Google's AI Mode reporting development and update campaign structures as placement-level data becomes available. Google is actively building out advertiser controls and reporting for AI Mode, and the account that has clean structure and strong assets going in will be better positioned to adapt as the reporting and controls evolve. The surface is growing — 75 million users is a meaningful scale for a product that launched in the last year — and the share of queries with AI Mode placements is expected to increase. Building AI Mode readiness now, before it becomes a major traffic source, is lower-cost than retrofitting a poorly-structured account after the fact. For the broader picture on how to structure B2B SaaS campaigns for automation-first delivery, see the guide on Google Ads bidding strategies for B2B SaaS.