Most marketing teams are still measuring a version of search that users have already left behind. Some are experimenting with optimizations for the new search era we find ourselves in, but most are still determining how to measure visibility in AI-driven discovery environments.

Survey data research from Adobe for Business found that 98% of marketers have no documented roadmap and no real confidence in their AI optimization strategy. The survey of over 500 marketers also found that three in four (74%) have no measurable approach to AI search or are unaware of one existing. These findings are concerning, given the material business risk that the new distributed decision system for search poses to enterprises.

Traditional search models assumed buyers moved through relatively linear journeys: search, click, evaluate, convert. Now, consumers are interacting with brands through a much wider variety of touchpoints, from AI assistants, generated recommendations, conversation interfaces, community discussions, and zero-click environments.

Yet, teams are still prioritizing measuring clicks when AI systems answer questions without needing to generate any. Brands are being shaped in conversations that leave no trace in your analytics reports, and you can be performing well in SERPs yet remain invisible in AI-generated responses.

Adobe’s Search Everywhere Playbook frames this next step of discovery as an expansion of search itself — not just an SEO update, but a rethink of where discovery lives and what it takes to show up there.

Search has expanded beyond search engines

Expanded consumer touchpoints, from LLM responses to AI assistants, zero-click answer surfaces, and app store discovery, now mean that users often receive synthesized answers rather than a list of links to consider.

A buyer can ask an LLM which vendors to shortlist, get a clear and confident answer, and never visit a single website. That same buyer might also search directly within an app store, evaluate a brand through its listing, ratings, and metadata before ever reaching your website. Neither interaction shows up in your analytics, yet both shaped their consideration.

The fragmentation of search behavior means legacy SEO frameworks need to be optimized and reimagined for this era of search. Marketers need to broaden their focus, as they are no longer optimizing for search engines but for discoverability at every touchpoint where buyers evaluate brands.

Why traditional SEO metrics aren’t the complete picture

It’s important to be clear that traditional SEO metrics still matter. Rankings, impressions, click-through rates, sessions, and keyword positions continue to provide valuable signals, but they only tell part of the story.

Historically, marketing teams questioned whether users were clicking; this now needs to expand to ask whether AI systems have surfaced and accurately represented your brand. Here’s what marketers are measuring now — and what they need to consider:

Traditional SEO measurement

• Rankings
• Organic traffic
• Impressions
• CTR
• Sessions
• Keyword visibility
• Backlinks

AI search (GEO) metrics

• Citation presence and share of voice in AI outputs
• Prompt coverage, topic-level presence
• Branded search lift, zero-click influence
• Citation depth and answer persistence
• Inclusion in authoritative third-party sources
• Topic performance across the funnel
• Competitive citation share in AI answers
• Prompt-level experimentation results
• Sentiment: positive, neutral, and negative themes across platforms

In line with Search Everywhere Optimization, analytics needs to go beyond clicks and rankings to include AI discovery signals such as AI citations, share of voice in generated answers, and accuracy of brand representation to give you the full picture.

The new visibility challenge is representation and retrieval

Competitor performance is already a concern for marketers, with more than one-third (35%) reporting concern about a lack of visibility compared to competitors. The challenge of being seen over competitors is more complex than concerns about appearing infrequently, as representation itself becomes a competitive factor.

Once you have appeared in AI search, marketers need to consider how brands are described and if that positioning is consistent across search, both traditional and in AI discovery. Brands that maintain structured, authoritative, and consistently updated information are better positioned to influence how AI systems interpret and surface content.

Marketers are already adapting to the shift. Adobe for Business research found that 61% now incorporate conversational content formats such as FAQs and direct-answer structures to improve visibility in AI-powered discovery environments. Meanwhile, 49% actively refresh or prune content, while 43% break larger assets into modular content components.

The aim is to prioritize content integrity to ensure AI can reliably find, trust, and surface your content, and this requires refreshed operations.

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AI discovery requires new content operating models

Content operations have historically followed a somewhat straightforward path: creation, publication, and then ranking. A search everywhere strategy takes into consideration AI search marketing tactics that ensure content is accurately represented, trusted, and cited.

This approach has consequences for how teams govern content, maintain taxonomy, and coordinate across functions. AI visibility now depends on structured, findable information, and that changes how content teams need to operate.

• Establish shared sources of truth.

AI-led discovery systems rely on off-domain sources to shape how your brand is described, meaning consistent messaging across touchpoints is essential to omnichannel operations.

• Align teams around discovery outcomes.

LLM optimization is cross-functional and requires shared dashboards, owners, and aligned goals across marketing, SEO, and communications.

• Embed governance and feedback loops.

As AI logic is often invisible, proactive monitoring is key to catching and correcting the instances where your brand is misrepresented.

• Build adaptability into workflows.

Search is and will continue to evolve, a sustainable model needs to be put in place that expects updates to content, delivery, and measurement.

Organizations that establish and maintain discoverable content ecosystems will be best positioned for AI discovery, not those aiming to produce the highest volume of content.

Search Everywhere = Marketing transformation

The implementation of Search Everywhere Optimization reflects a broader market shift: discovery no longer lives exclusively within search engines, and content is no longer optimized solely for clicks.

Visibility increases rely on AI systems that find, understand, cite, and accurately represent brands. The organizations that quickly adapt to AI discovery will likely treat the approach differently, seeing it not as a standalone GEO tactic or a channel initiative, but as an operating model transformation across workflows, governance, measurement, and visibility infrastructure.

Discovery has already moved. The teams that adapt their workflows, measurement, and content operations to match it won’t just perform better in AI search, they’ll have an advantage while competitors are still stalling on adapting.

About The Author Of This Article

Tory is the Senior Director, Web Marketing for Adobe for Business. With over 20 years of experience in B2B marketing within Fortune 250 companies, Tory is a seasoned leader known for driving revenue and margin growth.

About Adobe

Adobe empowers everyone, everywhere to imagine, create, and bring any digital experience to life. From creators and students to small businesses, global enterprises, and nonprofit organizations — customers choose Adobe products to ideate, collaborate, be more productive, drive business growth, and build remarkable experiences.

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