Feature
eBook
Designing an AI-Native Contact Center
Key Takeaways
- Enterprises allocate an average of 12% of digital marketing budgets to AEO and GEO, and 94% plan to increase that investment in 2026.
- Brand mentions, citations and share-of-answer across AI platforms now matter as much as rankings and click-through rates.
- Tracking AI visibility demands combining proxy metrics (citations, share of voice) with hard KPIs like conversions, pipeline influence and revenue.
Search engine optimization isn’t what it used to be. What started as a straightforward effort to rank content on Google has grown into a sprawling discipline full of new acronyms, evolving technologies and shifting user expectations.
Today’s digital leaders are juggling multiple terms that reflect where search is headed:
- SEO (Search Engine Optimization): The traditional practice of optimizing content, technical website elements and user experience to improve organic rankings on search engines, like Google.
- GEO (Generative Engine Optimization): A newer concept focused on optimizing for responses generated by AI search engines and conversational assistants, like ChatGPT, where answers are synthesized rather than simply retrieved.
- AEO (Answer Engine Optimization): Optimization tailored to “answer engines,” systems that prioritize direct answers, rich snippets and knowledge panels over ten-blue-links — like Google's AI Overviews.
These terms represent the different ways people now discover information, engage with content and make decisions online. And while marketers once measured success by keywords and backlinks, the rise of generative AI means we need to redefine what, exactly, "visibility" means.
Now, in 2026, today's leaders are preparing to invest more in AI-centric search visibility and ensure their brands don't get lost in the digital fold.
Table of Contents
- SEO vs. GEO vs. AEO
- 94% of Enterprises Plan to Increase AI Search Budgets
- Traditional SEO Metrics No Longer Tell the Whole Story
- The New AI Search Metrics That Matter
- Designing Content for AI Agents, Not Just Humans
- Why AI Search Measurement Requires Multi-Layer Validation
SEO vs. GEO vs. AEO
You don’t need three isolated programs, but you do need layered optimization.
- SEO still governs crawlability, indexing and authority.
- AEO prioritizes structured clarity (concise answers, schema, FAQ formatting).
- GEO rewards contextual authority, citation consistency and entity relationships.
The most efficient approach is to build structured, authoritative, semantically rich content that supports all three, rather than creating separate content streams.
LLMs don’t “rank” like traditional search engines. Instead, they predict likely answers based on training data patterns, pull from indexed content and reinforce frequently cited, authoritative sources.
Brands with strong digital authority, consistent naming, high citation frequency and clear topical ownership are more likely to appear in AI-generated answers.
The mostly likely content types to be cited by AI, in order, include:
- Blogs
- Videos
- Articles
- News
- Product pages
If you want your content to perform well, you should directly answer specific questions, use clear subheadings and structured formatting, include citations and offer original insights (rather than summarizing information from other sources).
Because AI systems are probabilistic, measurement will never be perfectly deterministic — triangulation matters. As such, a layered model works best:
Early indicators
- AI citations
- Share of answer
- Brand mentions across AI platforms
Mid-funnel signals
- Assisted conversions
- Reduced time to decision
- Higher engagement depth
Business KPIs
- Pipeline influence
- Revenue attribution
- Retention and repeat purchase
94% of Enterprises Plan to Increase AI Search Budgets
The reality is that AI search optimization is moving from an experimental initiative into a formal, budgeted line item within enterprise marketing and digital strategy.
In a 2026 Conductor report, enterprises surveyed said they allocated an average of 12% of their digital marketing budgets to AEO and GEO in 2026. The majority (94%) said they plan to up that budget in 2026 — particularly among organizations that already consider themselves more advanced in AI search optimization.
The report found high-maturity AEO/GEO organizations are 2x as likely as medium-maturity peers, and 3x as likely as low-maturity organizations, to significantly increase investment this year.
Traditional SEO Metrics No Longer Tell the Whole Story
Sudhir Rajagopal, research director for the CMO Advisory Service at IDC, said agentic discovery leads to greater impressions and visibility, tracked through brand mentions and brand name visibility in AI searches. The flipside? Lower click-through rates and lower engagement.
“Essentially many of the top funnel activities and metrics from the SEO era are no longer holding true,” he said.
By the time buyers show up at a brand's digital property, they have mostly made up their mind concerning buyer intent and conversion readiness. “Exploration and evaluation occur outside of the brand's immediate realm of measurement, within the AI environment in tools such as ChatGPT, Perplexity or Gemini,” said Rajagopal.
Related Article: Survive the AI Takeover of Search — 5 Moves Every Brand Must Make
The New AI Search Metrics That Matter
Executives reported that AEO and GEO already produce measurable results, with 97% saying AI search visibility is delivering a positive business impact across the marketing funnel.
The result? AEO/GEO rank as top strategic marketing priorities for 2026, ahead of other digital initiatives.
Patrick Reinhart, VP of services and thought leadership at Conductor, said that when CMOs say AEO and GEO are delivering “measurable business impact,” what they mean by measurable has shifted. “Before traffic enters the picture, leaders are increasingly tracking AI-native visibility metrics, such as brand mentions, citations, and share of answers across AI overviews and answer engines, as proof that their brand is present in AI-driven discovery."
The downstream metrics they point to include:
- Conversions
- Pipeline influence
- Shorter conversion paths
- Direct revenue tied to AI-driven discovery
“The common thread is efficiency,” Reinhart said. “Visitors arriving from AI answer engines tend to convert in fewer sessions because much of the education and trust-building has already occurred.”
Designing Content for AI Agents, Not Just Humans
AI search optimization is increasingly embedded in long-term planning rather than treated as a short-term experiment. Survey respondents said AEO/GEO investments are now reflected in annual planning cycles, budget forecasts and core marketing strategies.
Even though the current throughput for AEO/agentic discovery is low, Rajagopal explainsed, he expects this to steadily grow. “Agentic discovery is poised to grow in usage and prevalence among buyers, and corporate marketers will need cater to this emerging buyer preference."
Consistent brand visibility in the AI environment in these early informational stages shape demand long before a shopping query ever occurs. Rajagopal noted, “Businesses that structure their data, content and knowledge to be discoverable by agents position themselves as authoritative participants in the conversation."
Measurement and data quality emerged as key focus areas as investment accelerates. The leading technology challenge facing AEO/GEO programs, according to enterprise leaders, is the accuracy trustworthiness of AI visibility data. They also voiced concerns that scraping-based measurement approaches may be insufficient for tracking performance in closed or rapidly evolving AI systems.
Related Article: OpenAI’s Ad-Supported ChatGPT: A New Rival to Google’s Search Empire?
Why AI Search Measurement Requires Multi-Layer Validation
Enterprises should treat AEO/GEO measurement as a multi-layer validation problem, not a single metric, said Rajagopal.
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“Early indicators like citations and share-of-voice in answer engines are useful starting points, but they rely on proxies and can be biased or inconsistent due to the probabilistic nature of LLMs [large language models]." Reliable validation requires combining discovery analytics with concrete business KPIs — while recognizing that measurement gaps still exist and will only close as the space matures.
What businesses should do, recommended Rajagopal, is:
- Track brand visibility, citations and share-of-voice across LLMs and answer engines, while recognizing these analytics rely on proxies and may be biased or inconsistent.
- Monitor AI chatbot interactions and measure the value of specific content assets to understand what influences AI-mediated responses and conversions.
- Tie AEO/GEO efforts to hard business KPIs such as CSAT, retention, repeat purchase, CLV and NPS, since third-party answer engines limit direct measurement and the ecosystem is still immature.