As companies look to AI as their next growth strategy or cost-cutting solution, are they overlooking how their own business unit knowledge is invisible not only to the rest of the company, but to the customers and prospects actively searching for answers right now?

Listen and Learn “Why AI Doesn’t Know You Exist” Deep Dive

Are Most Companies Focusing on the Wrong AI Battle?

If you’ve been following AI over the last several years, you may have noticed that a familiar theme keeps popping up: the companies reaping the greatest benefits are the ones who are the most deliberate about how their data is structured and managed. As Bain notes, the basic rule of “garbage in, garbage out” applies to AI as much as any other digital solution. The biggest legacy roadblock isn’t technology; rather, it’s misalignment, fragmentation and business units that are simply invisible to one another.

“Departments adopting AI independently generate fragmented gains that don’t add up to strategic impact and can even conflict with one another”

In this post, my friend Neil Perkin makes the case for why organizations should treat their knowledge as a living system rather than a static archive. Neil frames this across two dimensions: 1) productive knowledge, which converts internal information like transcripts, Slack conversations and project retrospectives into structured, searchable assets; and 2) generative knowledge, which deliberately connects data across departments so that AI can surface patterns and insights no single business unit would have discovered on its own. The result is an organization where each department’s data makes every other department smarter.

It’s a compelling framework, and it made me wonder, if these principles are this powerful internally, what happens when you apply the same thinking externally, i.e., to the website and every other digital touchpoint where your company either shows up in an AI query or doesn’t?

Is Your Company Raising Its Hand?

We’ve previously shared why an organization’s own website comprises just 5%-10% of the sources AI search references when generating answers. The remaining 90%-95% comes from a broader information ecosystem, including affiliate sites, reviews, forums and third-party content, like YouTube and Reddit.

Now apply Neil’s productive and generative knowledge framework to that reality. What if an organization deliberately structured its content around the actual conversations and perspectives of sales, research and marketing, then curated those assets across websites, landing pages, audio, transcripts and social channels?

The organization isn’t just publishing content; it’s raising its hand every time an AI query is triggered, whether by a human prompt or an agentic workflow. And the numbers suggest that the stakes are real. Webflow reports that 8% of their signups now come from large language model (LLM) traffic, converting at six times the rate of traditional Google search.

This Isn’t a Marketing Problem; It’s an Organizational One

A key takeaway here—and what appears to be the overarching problem with AI adoption—is an organization’s resistance to change. Productive and generative knowledge doesn’t sit within one business unit or another; rather, it needs to be owned by the entire organization. This Harvard Business Review article reinforces the point: departments adopting AI independently generate fragmented gains that don’t add up to strategic impact and can even conflict with one another.

“When sales insights stay locked in call notes, when product expertise never reaches the website and when customer success knowledge lives only in internal reports, the company becomes invisible to the AI queries its prospects are already running.”

Why do more than 70% of business transformations fail? McKinsey believes that adoption of AI faces several operational headwinds, pointing to the lack of business-led consensus from leaders, the slow speed of AI planning and decision-making, and the absence of cross-organizational data from which an LLM can accurately provide recommendations, decisions and actions.

As McKinsey puts it, the AI challenge is not a technology challenge; rather, it’s a business challenge requiring enterprise-wide rewiring. Siloed data may benefit individual business units, but it ultimately works against the company’s strategic impact as a whole. This is the same dynamic playing out externally. When sales insights stay locked in call notes, when product expertise never reaches the website and when customer success knowledge lives only in internal reports, the company becomes invisible to the AI queries its prospects are already running. The organizational problem and the discoverability problem are the same problem, just viewed from different directions.

The Missed Opportunity: From Defensive AI to Strategic Offense

Many companies have defaulted to a defensive AI posture, treating it exclusively as an internal tool to increase efficiency and reduce costs. It’s a reasonable starting point, but it leaves the most valuable half of the AI equation untouched.

The real opportunity lies in going on the offensive. When companies shift from protecting what they know internally to broadcasting what they know externally, their collective data becomes a revenue asset, unlocking unforeseen knowledge, new ways of thinking and customer acquisition channels that simply didn’t exist before.

This is where “it takes a village” becomes more than a saying. Each business unit is a data source: sales intelligence, product expertise, customer success insights, subject matter knowledge. Cross-functional participation isn’t a nice-to-have; it’s mandatory. Without it, the company’s offensive strategy has gaps, and gaps are exactly where competitors become more visible than you.

Consider what happens when a company gets this right. This organization restructured its content around common AWS cost questions, established its leadership team as verified experts, integrated real-time pricing data and implemented citation frameworks. Within four weeks, it secured the top position in Perplexity for enterprise AWS cost queries. Today, 32% of its new consultation requests specifically mention finding them through AI-powered search—a customer acquisition channel that didn’t exist before.

You Have to Start Somewhere

The debate between an offensive and defensive AI strategy is the wrong place to begin. Both matter and neither works without the other. The same balance applies between productive and generative knowledge. The real risk isn’t choosing the wrong approach; rather, it’s delaying the decision entirely, which is just as damaging as doing nothing at all.

Start with a simple question: what does one business unit know that the rest of the organization doesn’t? The answer will be a wake-up call. Knowledge is the strongest strategic asset a company can have. The companies that structure it, curate it and broadcast it externally won’t just know more; they’ll be known.

If you’re wondering where your company stands today, that’s exactly what an AEO assessment is designed to answer. Let’s start the conversation. You can reach me at mike@scheinerinc.com.

As always, thank you for reading and/or listening.

Photo by Marcos Luiz Photograph on Unsplash