Don’t develop an AI strategy because you view the technology like a stuffy admin assistant who tries to keep unexpected visitors from disturbing a senior executive.
These were the original gatekeepers, and everyone from sales reps to job seekers has tried and failed to get past them.
Like AI, the best admin assistants are focused entirely on the needs of their boss. They understand what that person finds valuable and only let in those who can provide it.
While people have tried to sway admin assistants with sweet talk and humor, AI has spawned an entire discipline of answer engine optimization (AEO) techniques. This includes an increased focus on structured data, answer-first formatting, FAQ sections, and schema markup.
All of those elements are an important part of an AI strategy, but their importance predates it.
AI hasn’t become a gatekeeper so much as a catalyst for reassessing how content is designed for maximum online discoverability.
Instead of seeing AI as an admin assistant, brands should realize they’ve built the equivalent of a long, winding corridor between their content and desired audiences, with several dead ends and a few locked doors.
AI didn’t change what good content looks like
“Good content” seems like a subjective term, but most people look for the same qualities. They want information that is original, unbiased, and genuinely helpful by reflecting their common pain points and aspirations.
AEO (or similar practices such as generative answer optimization or GEO) has emphasized how AI prioritizes proprietary research. That’s because humans do too: 96% of marketers say original data drives engagement and leads, and 97% said thought leadership — where a brand’s leaders offer unique insights — is critical to full-funnel success.
A similar 2026 report found that 33% of B2B marketers cited stronger creative and content as the most critical driver of effectiveness.
When AI is perceived as a threat to content visibility, there’s a risk of focusing on large language models (LLMs) while a brand’s actual audience recedes into the background. A study by LinkedIn and Meltwater, for instance, offered an ‘AI-Citable Content Recipe” based on LinkedIn’s most-scraped posts.
The recipe included the use of bullets and H2 headings (a standard AEO/GEO principle) while noting that 50% of posts included a comparison/evaluation framework and 33% had a “how to choose” decision guide.
Those latter elements may attract AI’s attention, but they’re really intended for human eyes. If people couldn’t already see them before, the impact of AI on referral traffic should come as no surprise.
The real gatekeeper was always the architecture
If AI could laugh, it would howl at a LinkedIn video in which B2B marketing influencer Heike Young acts like a piece of gated content that’s come to life.
“Whoa, hold on there, sport, we’re going to need to see some identification to see these,” she says, blocking a door with a sign marked THOUGHT LEADERSHIP ASSETS. “Oh, just your name, where you work, your title, number of zeros in your bank account. The usual.”
Brands, not AI, have been the biggest gatekeepers of content because doing so has been an established best practice for lead generation. No longer: 73% of buyers say they actively avoid gated content, probably because they realize every download will trigger a wave of outreach from sales reps, whether they’re ready to discuss a purchase or not.
AI is really thinking and acting like those buyers. It not only avoids gated content but also skips over information that gets buried in sub-menus or requires jumping through other hoops. McKinsey’s 2026 B2B marketing research shows nearly half of buyers (48%) switch suppliers due to a poor website experience.
These scenarios offer some examples of where those experiences can go awry:
- A buyer lands on a vendor website already believing they’ve found the best solution to meet their needs. They perform a quick check on the product page but need to relay costs to their boss or buying committee. Instead, all they can find is a “Contact Us” or “See a demo” button. Yet 44% of B2B buyers say transparent pricing is a critical factor in vendor evaluations. AI will favor sites with clear pricing.
- A buyer needs to determine whether a potential vendor has solutions tailored to their vertical industry, or company size, or that it integrates with their existing tech stack. Looking this up on the site is akin to the quest for the Holy Grail. In fact, 69% of B2B buyers say vendor information is scattered and difficult to access, and 40% said they turn to AI instead for faster answers.
- A buyer finds a guide that could help them solve a business problem. They dutifully fill out a form, but instead of getting to see the report on the vendor’s site, a PDF is emailed to them. However, the average time between requesting content and opening it has reached 48 hours, a 24% year-over-year increase.
AI answer engines are tasked with creating summaries and overviews as efficiently as possible. Just like the buyers they’re assisting, they are looking for the quickest and best route to the most relevant content. There’s no point in citing brands that will force buyers to work harder than they should to get the details they need.
How do brands design for AI search visibility?
There is a burgeoning cottage industry of blog posts, YouTube videos, and books promising to teach AI visibility practices. It’s not unlike the early days of SEO: attempts to game the algorithm failed, but using strategic keywords, cross-linking, and well-structured content ranked highly.
Google’s own guidance on winning with AI overviews emphasized that good AEO and GEO are really the same as good SEO. AI search services reward content that recognizes what users are asking, provides an answer with supporting evidence, explains how it arrived at the answer, and offers insight into actionable next steps.
Having an AI agent-ready content infrastructure like an enterprise-grade CMS is another helpful layer. It builds upon quality content via:
- The ability to create taxonomies that classify content to support browsing and filtering while reducing ambiguity for AI search services.
- Structured content models that clearly define titles, authors, related topics, related products, and other fields and relationships that enhance search and simplify LLM parsing.
- Consistent information hierarchy that orients both humans and AI search tools by easing navigation, providing context, and clarifying the role of each web page on a site.
Your CMS is the foundation for treating AI as a researcher working on behalf of humans, rather than as a gatekeeper to be outplayed.
Stop blaming AI for your organic traffic problem
A lot of enterprise content has been enticing audiences with the tip of the iceberg and then asking them to dive deeper. Yet most of us prefer to get past the teasers and hooks and get to the point of a branded asset like a blog post or an ebook as quickly as possible.
AI may appear to ignore or dismiss some sites, but it’s designed to act in users’ best interests. Brands that do the same thing will increase their visibility in both AI tools and traditional search engines.
