94% of enterprise executives plan to increase their investments in Answer Engine Optimization and Generative Engine Optimization in 2026, according to a landmark survey published by Conductor and Search Engine Journal. The other 6% are probably still debating whether AI search is a fad.

The report, based on responses from over 250 enterprise leaders, confirms what those of us working in GEO have observed for months: the money is moving. Budgets are shifting from traditional SEO tactics toward the disciplines that determine whether AI engines recommend your brand or skip past you entirely. But not all organizations are approaching this shift with the same rigor, and the gap between leaders and laggards is widening fast.

Here is what the data says, what the high-maturity organizations are doing differently, and what your brand should do next.

The Numbers That Matter

The Conductor/SEJ report contains several data points worth examining closely:

  • 94% of enterprises plan to increase AEO/GEO investments in 2026. This is not a marginal shift. This is a sector-wide repositioning of search budgets.
  • 93% are building AEO/GEO capabilities in-house rather than outsourcing to agencies. That signals permanence. When organizations hire for a capability instead of contracting it, they are betting it will exist long enough to justify headcount.
  • New KPIs are replacing traffic-based metrics. The enterprises getting results are measuring conversions from AI referrals, brand sentiment in AI-generated answers, and AI search market share rather than organic click volume.
  • Data quality remains the number one frustration cited by practitioners. Structured data, clean entity information, and consistent brand signals across platforms are harder to manage than most teams anticipated.

Aleyda Solis, commenting on the report, made a critical observation: “Brand sentiment is one of the key differentiators vs. traditional search. You might get mentioned in an answer, but if it’s negative, the impact on your brand might not be positive.”

This is the nuance most teams miss. Getting cited by ChatGPT or Perplexity is not inherently valuable. The context of that citation matters enormously.

What High-Maturity Organizations Do Differently

The report distinguishes between organizations at different stages of GEO maturity. The leaders share three characteristics that the rest lack.

1. They Publish Original Research and Proprietary Data

High-maturity organizations do not just publish blog posts optimized for AI extraction. They create datasets, run surveys, and produce original research that AI engines cannot find elsewhere.

This matters because AI engines prioritize unique, authoritative sources. When ChatGPT or Perplexity encounters the same recycled statistics across dozens of websites, it treats all of them as redundant. When it encounters a proprietary dataset or an original finding, that source gets cited disproportionately.

At searchless.ai, we have observed this pattern consistently across our tracking data. Brands that publish original research get cited 3 to 5 times more often by AI engines than brands that only produce derivative content. The Conductor report confirms this at the enterprise level.

2. They Treat Structured Data as Infrastructure, Not an Afterthought

Most organizations add schema markup as a final step before publishing. High-maturity organizations treat structured data as a core part of their content infrastructure.

This means:

  • JSON-LD schema on every page, not just product pages or blog posts
  • FAQ schema that directly answers the questions AI engines extract, written in answer-first format
  • Entity-linked internal content using consistent named entities across all pages
  • llms.txt files that give AI engines a structured map of their content

The organizations winning at GEO have engineering resources dedicated to structured data pipelines. They do not ask their content team to manually add schema. They build systems that generate it automatically.

3. They Measure What Matters, Not What Is Easy

The most telling finding in the report is the shift in KPIs. Low-maturity organizations are still tracking impressions, keyword rankings, and organic traffic. High-maturity organizations have moved to an entirely different measurement framework:

  • AI citation rate: How often does your brand appear in AI-generated answers for queries relevant to your business?
  • Citation sentiment: When AI engines mention you, is the context positive, neutral, or negative?
  • AI referral conversion rate: When users click through from an AI answer, do they convert at higher rates than traditional organic traffic? (Early data suggests they do, sometimes 3 to 4 times higher.)
  • Share of Model: What percentage of AI answers in your category mention your brand versus competitors?

If your team is still using Google Search Console as its primary visibility dashboard, you are measuring the past. The organizations investing in GEO are building dashboards that track brand presence across ChatGPT, Perplexity, Gemini, and Google AI Overviews. Tools like the Searchless AI Visibility Score are becoming the new standard for benchmarking.

The Three Signals That Make AI Engines Cite You

Based on the report’s findings and our own analysis at searchless.ai, three signals consistently predict whether a brand gets cited by AI engines:

Signal 1: Entity Authority. AI engines build knowledge graphs from mentions across multiple domains. Brands that are referenced by 6 or more authoritative domains in their category get cited significantly more often. This is not about backlinks in the traditional sense. It is about being part of the conversation across enough trusted sources that AI engines recognize you as an established entity.

Signal 2: Answer-First Structure. AI engines extract answers from the first two sentences of a content block 73% of the time, according to multiple studies. If your key information is buried in the third paragraph, you are invisible to AI. The brands winning at GEO put their answer first, then provide context and supporting evidence below.

Signal 3: Machine-Readable Content Architecture. This encompasses llms.txt, comprehensive schema markup, clean HTML structure, and content that is formatted for extraction rather than just human reading. AI engines are not reading your page the way a human does. They are parsing it programmatically. Content that is easy to parse gets cited more often.

Most brands have zero of these three signals in place. The enterprises investing in GEO are systematically building all three.

The In-House Shift and Why It Matters

The fact that 93% of enterprises are building GEO capabilities in-house is significant for two reasons.

First, it means GEO is becoming a recognized discipline inside organizations, not just a line item on an agency invoice. Companies are hiring for GEO-specific roles, creating cross-functional teams that span content, engineering, and data, and building internal tools for tracking AI visibility.

Second, it means the agency model for SEO is under pressure. Agencies that only deliver traditional SEO services (keyword research, on-page optimization, link building) will find their enterprise clients either replacing them or demanding GEO capabilities they cannot provide. We explored this shift in our analysis of how SEO agencies are pivoting to GEO, and the Conductor data reinforces that trend.

Data Quality: The Silent Killer of GEO Programs

The number one frustration cited by GEO practitioners is data quality. This is not surprising if you have ever tried to audit an enterprise website’s structured data.

Common problems include:

  • Inconsistent entity names across pages (your brand is “Acme Corp” on the homepage, “Acme Corporation” on the about page, and “Acme” on the blog)
  • Duplicate or conflicting schema markup from different teams publishing independently
  • Missing or incomplete FAQ schema on pages that are otherwise well-optimized
  • Content management systems that strip or mangle structured data during publishing

Solving data quality at scale requires engineering investment. It is not a content problem. It is an infrastructure problem. The organizations succeeding at GEO have data engineering teams that maintain structured data pipelines with the same rigor they apply to any other data system.

What Your Brand Should Do This Quarter

Based on the report’s findings and the patterns we track at searchless.ai, here is a practical 90-day roadmap:

Month 1: Audit Your AI Visibility

Before you invest in fixes, measure where you stand. Use tools that track your brand’s presence across AI engines, not just Google. Check your AI Visibility Score to establish a baseline. Identify which queries in your category trigger AI answers and whether your brand appears in them.

Month 2: Fix Your Content Architecture

Implement llms.txt if you do not have one. Audit your schema markup for completeness and consistency. Restructure your highest-priority content to use answer-first formatting. Ensure your entity names are consistent across every page on your site.

Month 3: Start Publishing Authority Signals

Begin producing original research, proprietary data, or expert analysis that AI engines cannot find elsewhere. Build relationships with authoritative domains in your category so your brand is mentioned in enough places to register as a recognized entity. Track your citation rate and sentiment monthly.

FAQ

What is GEO and how is it different from SEO?

GEO (Generative Engine Optimization) is the practice of optimizing your brand’s content and digital presence so that AI engines like ChatGPT, Perplexity, Gemini, and Google AI Overviews recommend your brand in their generated answers. Unlike traditional SEO, which focuses on ranking in a list of ten blue links, GEO focuses on being the single answer AI engines provide to users.

Why are enterprises investing in GEO in 2026?

Because 900 million people now use AI search weekly, and zero of them see traditional Google rankings. AI engines are becoming the primary way users discover brands, products, and services. Enterprises that are not visible in AI-generated answers are losing market share to competitors who are.

What KPIs should I track for GEO?

Move beyond traffic-based metrics. Track AI citation rate (how often your brand appears in AI answers), citation sentiment (positive, neutral, or negative context), AI referral conversion rate, and Share of Model (your brand’s percentage of AI mentions versus competitors in your category). These are the metrics the highest-performing enterprises use, according to the Conductor/SEJ report.

Is building GEO capabilities in-house better than using an agency?

The data suggests enterprises prefer in-house teams for GEO. 93% of enterprise executives surveyed are building these capabilities internally, signaling that GEO requires deep integration with content, engineering, and data teams that external agencies cannot easily replicate.

How long does it take to see results from GEO?

Most organizations see measurable improvements in AI citation rates within 60 to 90 days of implementing structured data, answer-first content formatting, and entity authority building. However, building sustained AI visibility is an ongoing process, not a one-time project.

The Bottom Line

The Conductor/SEJ report confirms that enterprise investment in GEO is no longer speculative. The budgets are committed, the teams are being built, and the KPIs are shifting. The organizations that act on this data now, rather than waiting for another quarter of declining organic traffic, will be the ones AI engines recommend when your customers ask for solutions.

The question is not whether your competitors are investing in GEO. 94% of them are. The question is whether you are investing fast enough to stay visible.

Check your AI visibility in 60 seconds. Get your free AI Visibility Score at audit.searchless.ai and see exactly where your brand stands across ChatGPT, Perplexity, Gemini, and Google AI Overviews.