Expert Insights

Q1: What does visibility actually mean today as search results continue to evolve?

For most of the past two decades, visibility meant one thing: where you ranked. That is no longer the full picture. Visibility today operates across four distinct dimensions:

• Traditional search rankings
• Presence in AI overviews and large language models like ChatGPT, Google Gemini, and Claude.
• Brand recognition across third-party publications and digital PR,
• And, citations in community platforms such as Reddit and LinkedIn.

Each of these channels plays a different role, and increasingly, the most consequential of them is the one marketers have the least experience managing: the LLMs. When someone opens an AI tool to research a purchasing decision or solve a business problem, they are often getting a complete answer without ever visiting a brand’s website. The brand that gets referenced in that answer has influence.

The question marketers should be asking has shifted. It used to be: what are we ranking for? Now, the more important question is: are we showing up in the answer?

Q2: In a zero-click world, how are AI-generated responses changing how users discover and engage with content?

The numbers tell a striking story. Roughly 60% of Google searches now result in no click-through at all. In Google’s AI mode, that figure rises to 93%. The traditional funnel, where a user searches, lands on a page, and progresses through a defined journey, is being compressed.

More and more of the research and decision-making process is happening inside the LLM itself, before the user ever reaches a brand’s owned properties.

This does not mean website traffic is irrelevant. But it does mean that traffic is no longer a reliable proxy for influence. Prospects are showing up to discovery calls already well-informed, having spent significant time conversing with an AI about a company’s methodology, approach, or positioning. That too without having downloaded a single asset or visited a single page.

The top of the funnel has, in many cases, moved into the AI answers prompt.

One framework worth adopting here is the three C’s: content, citations, and community.

• Content is the foundation, as what you are producing is being picked up and referenced by LLMs?
• Citations are about where your brand appears across the broader internet ecosystem: the publications, review platforms, and directories that AI tools draw from when constructing answers.
• Community covers the spaces where your buyers are having real conversations. Reddit, LinkedIn, and increasingly, niche forums relevant to your industry.

Brands that are active and credible across all three are the ones getting surfaced.

Q3: How can marketers stay on top of their goals with the rise of AI-generated search experiences?

The underlying goal of organic marketing has not changed, earning trust and driving qualified interest remain the core objectives. What has changed is the playbook for getting there.

There are three priorities that stand out for marketers navigating this shift.

1. The first is auditing existing content for answer readiness. The practical test is simple: can an LLM extract a clear, citable answer from it? A useful exercise is to query an AI tool directly about your brand or company and see what comes back. What gets surfaced, what gets missed, and what gets attributed to a competitor instead tells you a great deal about where your content is falling short.
2. The second priority is shifting toward question-based, intent-driven content. Rather than building content around keyword lists, the more effective approach is to identify the questions your buyers are actually asking and build directly around those. A how-to guide, a decision checklist, or an executive summary structured around a specific buyer question is far more likely to be referenced by an AI than a generic blog post.
3. The third is doubling down on proprietary point of view. LLMs can summarize widely available information, but they cannot replicate a brand’s unique data, client experience, or expert perspective. Original research, comparison content, FAQ pages grounded in real customer language, and executive thought leadership are significantly more likely to be surfaced by AI than content that covers ground already well-trodden elsewhere. That is a meaningful competitive moat for brands willing to invest in it.

On the tactical side, how content is structured matters as much as what it says. Clear topic framing at the top of every article, proper schema markup, and FAQ formatting all improve the likelihood that AI systems can parse and cite the content accurately.

Q4: How can marketers adapt their strategy to an AI-driven discovery pattern?

One of the more interesting findings from this environment is that some of the old optimization instincts are still at play, just in new contexts.

Analysis of how brands are referenced in LLM outputs has revealed patterns that resemble keyword weighting: brands whose content consistently and naturally uses the language their buyers use tend to be recommended more frequently. The implication is not to stuff content with terms, but to be genuinely precise about the language of the category and the customer.

The more important adaptation, though, is structural. Marketers need to approach content with an answer-first mindset. This means leading with clarity, stating what a piece of content is about immediately and unambiguously, rather than building to a point. It means using the formats AI systems can most easily process: structured headers, FAQ sections, and schema data. And it means thinking about where the content will be cited, not just where it will rank.

Brands whose content teams are currently siloed across functions, with different groups producing blogs, support documentation, product pages, and thought leadership independently, are at a disadvantage here. Bringing those creators together to align on content pillars, avoid duplication, and build a coherent body of work across channels is one of the highest-leverage operational changes a marketing team can make right now.

Running ongoing content audits, and measuring performance across LLMs rather than just Google is also becoming standard practice for teams ahead of this curve.

Q5: What signals and metrics can be used to assess content impact beyond clicks?

Clicks were always a proxy metric. The organizations that treated click-through rate as a measure of content value were really measuring something adjacent to what they cared about, and in an AI search environment, that proxy has become even less reliable.

The metrics that paint a more accurate picture today include:

1. Citation frequency, meaning how often and across how many different AI channels a brand’s content is being surfaced.
2. Branded search lift following AI exposure, which indicates that AI-driven awareness is translating into direct interest.
3. Content depth scores, capturing whether users are consuming enough to signal genuine intent.
4. and pipeline influence at earlier funnel stages, tracking which content was being consumed before a prospect converted.
5. Share of voice in AI-generated answers, compared not just in absolute terms but relative to competitors, is also increasingly valuable. A brand may see traffic decline while its presence in AI answers grows. That is not a sign that the strategy is failing. It is a sign that the measurement model needs to catch up.

The discipline that makes all of this actionable is connecting content metrics to revenue indicators. Building leading indicators around pipeline influence tracking what content a customer was engaging with before they converted.