How mature is your AI search strategy?

These days, when I share updates on AI’s role in college search, I don’t need to spell out the implications. Higher education leaders know that they need to be actively managing their own AI search strategy. 

But they aren’t sure what to do first. They ask:

“Where do we start?” 

“Where do we have the most control?” 

“Where do we have the greatest opportunities to improve?”

To answer these questions, we developed a new framework: the AI Search Maturity Model. Based on discussions with hundreds of leaders, teams, and experts within and outside of higher education, as well as meta-analyses of AI-generated answers across hundreds of institutions, the maturity model captures EAB’s current perspective on how to prioritize and sequence AI search visibility efforts. The five stages below show how that work can progress, from building basic awareness to using AI search insights to inform broader competitive strategy.

The baseline: AI search awareness

Perhaps it goes without saying, but the starting place for AI Search Maturity is an awareness that it’s an important issue to manage in the first place. 

Compared to the status quo six months or a year ago, many more college enrollment and marketing teams have identified AI search visibility as a strategic priority, but there are still many more who haven’t organized around the effort.

Stage one: Scalable AI visibility measurement

Beyond awareness, the foundation of managing AI search visibility is understanding current performance and future opportunities. Most institutions we speak to today are still at this stage: they don’t yet have a scalable or rigorous practice to assess what happens when students and families are using AI in their college search. 

Our latest insight paper expands on this stage (and the next two), helping leaders achieve what we call “AI search readiness,” or the ability to track and understand the metrics shaping AI search optimization, identify where to focus first, and improve the .edu content and signals influencing AI answers.

If nothing else, we encourage enrollment and marketing teams to test AI discoverability on their own, prompting tools like ChatGPT or Claude with common questions and prompts that they hear from students and families. A “DIY” approach to monitoring can provide nuggets of insight and areas for improvement, although it is by necessity intermittent, subjective, and lacking important input on student AI behavior. 

Our perspective is that one-off “try this in ChatGPT” exercises are no substitute for a consistent approach to AI search readiness. That’s why EAB’s GEO Intelligence Dashboard tracks AI visibility, brand sentiment, competitive position, and more across 12+ AI models, providing a unified view of performance and progress over time.

Stage two: Technical search foundations 

Once a consistent measurement approach is in place, the next step is to prioritize web enhancements that will most directly improve your search strategy.

AI search optimization is still largely built on SEO foundations, and good news—it’s an area where you still have a relatively high degree of control. Start by optimizing key enrollment and program pages across your own .edu site. AI search crawlers look for well-structured, technically sound pages with clear titles, H1s, internal linking, and fast page speed. 

Stage three: Website content improvements

Once your site is technically discoverable, targeted content improvements can make a significant difference in how AI platforms understand, differentiate, and present your institution. 

Ensure your website’s enrollment-specific content clearly addresses common student and family questions such as “Which schools can I afford?” or “What program is best for me?” Write for both humans and AI by adding context, verifiable proof points, and messaging that distinguishes your institution and programs from competitors. 

Stage four: Third-party message management

Because AI models draw from multiple sources when constructing their answers, leaders often wonder whether they need to pay attention to social media posts, rankings, or news articles as they manage their AI visibility in the long term.

The answer is “yes, absolutely.” Social sources, third-party rankings and reviews, and news can be highly influential in affecting how AI—and consequently students—perceive your institution. 

However, it’s more difficult, more time-consuming, and less predictable to influence those sources than it is to improve the foundations and content of your own .edu website. 

Most institutions would be better served by concentrating on their own websites first, where they have maximum control and the most leverage. Over time, we expect that marketing and enrollment teams will begin to use all the levers at their disposal, including media relations, social listening, social influencer strategy, and other external relations approaches, to influence AI answers. 

Stage five: Strategic competitive insight

One of the biggest long-term benefits of monitoring and analyzing AI answers is that this discipline provides a new, powerful source of insight into how your institution’s value proposition is faring in the market.

AI responses aren’t proprietary to any one institution, so you can use these AI-based analyses to understand what AI models are saying about competitors, not just your institution.

For instance, we sometimes observe that our partners’ marquee programs aren’t getting the prominence in AI-generated answers that they deserve. Occasionally, it’s an issue of marketing execution. But it could also reflect a more fundamental issue: competing programs may simply be stronger or better positioned in AI search. 

Few marketing and enrollment leaders have up-to-date market research at their disposal to inform highest-level strategic questions about how to position the institution to win in the market long term. AI visibility data can serve as a timely, informative source of market intelligence—not to replace in-depth market research, but to supplement it with near-real-time input.

So, where does this leave us?

Today, with very few exceptions, college and university leaders are on a relatively level playing field when it comes to AI search. AI itself is still new, and the discipline of managing an AI search strategy is even newer, so few have developed a mature practice yet. That also creates a short window of opportunity to gain a competitive advantage.

For most institutions, the right next step is stage one: establish a scalable measurement practice to identify where you stand, highlight the most actionable opportunities, and track improvements over time.

Use the insights and resources in our new AI Search Readiness Scorecard for Higher Ed to assess your current performance and turn that data into focused action across your .edu, content, brand, and enrollment goals. As the AI landscape evolves, institutions that act on the right priorities now will be better positioned to stay visible, relevant, and competitive.

Michael Koppenheffer

Vice President, Enroll360 Marketing, Analytics and AI Strategy

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