Key takeaways

• AI Overviews reduce clicks on branded and comparison queries, shifting value from “owning the SERP” to being named inside AI answers.

• Review and comparison sites lose some leverage as lists become summaries, but their content still heavily shapes how AI describes brands.

• SaaS and healthcare marketers must protect branded queries with owned answer pages and influence the third-party sources AI cites.

• Comparison content needs a new structure: criteria first, “best for” segments, explicit constraints, and FAQ blocks designed for extraction.

• Success metrics shift from raw sessions to mentions, citations, and downstream pipeline impact.

From click-driven lists to answer-first search

Google’s AI Overviews moved from experiment to mainstream through 2024 and 2025, with coverage expanding across industries and query types. Source: brightedge.com

The impact on clicks is not theoretical. A March 2025 Pew Research analysis found that when an AI summary appears, users click a traditional result only about 8 percent of the time, compared with 15 percent when no summary is present. Seer Interactive’s data, reported in Search Engine Land, shows organic click-through rates on queries with AI Overviews down by roughly 61 percent from mid 2024 baselines. Source: Search Engine Land

Semrush’s 2025 study on 10 million keywords adds another layer: AI Overviews are no longer just for informational questions. The share of AI Overview queries that are purely informational dropped from about 91 percent in January 2025 to 57 percent by October, as commercial and navigational queries began triggering summaries more often. Source: Stan Ventures

Translation: branded and comparison queries that used to reliably drive clicks now often get partially “answered” before anyone visits your site.

What actually changes for branded and comparison queries

SaaS examples

Typical SaaS branded and comparison queries:

• “{Brand} pricing”

• “{Brand} reviews”

• “{Brand} vs {Competitor}”

• “best {category} for {use case}”

• “{Brand} SOC 2”

• “{Brand} Salesforce integration”

With AI Overviews present, buyers may see:

• A short paragraph summarizing what your product does and who it is for

• A rough description of pricing models and tiers

• Pros, cons, and “best for” segments

• A shortlist of comparable alternatives

That language often comes from review sites, G2-style profiles, your own documentation, and community posts, not just your homepage.

Implications for SaaS brands:

• Owned assets: you need branded pricing explainers, comparison hubs, and integration pages that use clear, entity-first language and constraints so AI can quote you accurately.

• Third-party presence: your G2 or Capterra profile hygiene, category placement, and review quality become “AI training data for perception,” not just social proof.

Healthcare examples

Common healthcare queries:

• “{Clinic} reviews”

• “{Hospital} vs {Hospital}”

• “{Treatment} options”

• “is {service} covered by insurance”

• “best {specialist} near me”

AI Overviews tend to:

• Summarize consensus guidance and treatment options

• Surface trust signals like affiliations, accreditations, and review sentiment

• Answer basic insurance and eligibility questions directly in the SERP

Pew’s work on AI summaries confirms that when these answer blocks appear, clicks to underlying sites drop meaningfully. Source: Pew Research Center

Implications for healthcare brands:

• Your medically reviewed pages, disclaimers, and eligibility criteria need to be explicit and up to date so AI has safe, accurate language to reuse.

• Presence in reputable third-party sources (health systems, specialty associations, major review platforms) materially influences how AI frames your brand.

How review and comparison sites need to adapt

Review and comparison sites are under pressure. BrightEdge’s one year AI Overview report shows “ranking-style” and comparison queries declining in prominence, down around 60 percent and 14 percent respectively, as users let AI handle curation instead of asking for explicit “best X” lists. Source: brightedge.com

The adaptation is clear:

• Shift from generic “best X” lists to proprietary value

  • Original testing, first-party benchmarks, real screenshots, implementation stories

  • Transparent methodology and criteria that models can summarize

• Structure for extraction

  • Criteria-first sections, then “best for” segments by use case or company type

  • Short answer blocks at the top that explain recommendation logic

• Build citation-worthiness

  • Clear entity definitions, update timestamps, named experts, and sources

  • Content that looks safe for AI to quote verbatim

And accept a hard reality: some value moves from clicks to influence. The job of review-site SEO becomes “be the place AI cites” as much as “be the site users click.”

Branded visibility: 2022 vs 2025

In 2022, branded SERPs were mostly navigational:

• Homepage, sitelinks, social profiles

• A handful of review results and maybe one or two comparison pages

The model was simple. Protect your own listings and try to keep negative results off page one.

By late 2025, Semrush and others show AI Overviews expanding into more commercial and navigational queries, not just informational ones. At the same time, multiple studies, including Pew’s March 2025 work, show that when AI summaries appear, overall click behavior drops. Source: Pew Research Center

Branded search strategy now has to cover three layers:

• Owned answers: brand, pricing, integration, security, and “Brand vs X” pages that AI can confidently reuse.

• Third-party influence: review sites, directories, forums, and communities that AI leans on for sentiment and comparisons.

• AI-specific visibility: how often you are named or cited in AI Overviews and assistant responses for branded and category queries.

Rebuilding comparison content for AI citation

Classic comparison SEO rewarded “Best X tools for Y” listicles with thin commentary and affiliate links. AI Overviews are hostile to that format.

A modern comparison page outline for AI citation looks more like:

• Clear scope and audience

  • “Comparison of enterprise CRM platforms for 200 to 2,000 seat B2B teams”

• Evaluation criteria

  • Data model, integration depth, admin overhead, security, pricing structure

• “Best for” segments

  • Vendor A: best for deeply customized Salesforce shops

  • Vendor B: best for fast deployment with light customization

• Evidence and constraints

  • Where each product struggles, requirements, and who should not choose it

• FAQ block for objections

  • Integration pain, migration risk, contract lock in, total cost patterns

You can use AI to pressure test or design these structures:

“Generate a modern comparison page outline for [product category] designed to be cited by AI Overviews: evaluation criteria, ‘best for’ segments, evidence, constraints, and an FAQ block for objections.”

The objective is not to trick AI into citing you. It is to make your logic so explicit that summarizing you is the safest option.

Rethinking review-site and directory strategy

Not every review or directory platform influences AI answers equally. Some are heavily cited in snippets and summaries, others barely register.

You can use a prompt like:

“Given this list of review sites and directories for [industry], rank which ones most likely influence AI answers and branded query perception. Output an influence plan including profile hygiene, review strategy, and content syndication.”

Then treat the top tier as strategic infrastructure:

• Profile hygiene: consistent naming, categories, and messaging

• Review strategy: a steady flow of detailed, role-specific reviews instead of sporadic bursts

• Content syndication: repurpose case study language, screenshots, and FAQs into platform-friendly assets

This is review-site SEO for the LLM era. You are optimizing for visibility in models, not just for human readers who happen to click.

Building a defensive branded visibility plan

A practical defensive plan usually includes:

• Mapping your top branded and comparison queries and identifying which already trigger AI Overviews.

• Auditing your owned pages: brand, pricing, integration, and “Brand vs X” content for clarity, constraints, and FAQ coverage.

• Identifying the third-party sites that show up repeatedly in AI summaries and SERPs, then tightening your presence there.

• Adding AI search metrics into reporting: AI Overview presence, citation frequency in assistants, and shifts in branded CTR over time.

If you skip this, you let AI systems define your brand from whatever content happens to be available, whether you control it or not.

An AI Brand Visibility Audit is the logical next move: map your branded and comparison queries, identify which third-party sources are shaping AI answers today, and ship a prioritized plan for owned comparison hubs plus review-site influence improvements that protect both visibility and pipeline.