As AI becomes part of how people research companies, compare options and make decisions, organizations need to think beyond traditional search visibility. Just publishing more content is unlikely to create an advantage. Instead, identify the experience, research and expertise your organization already has and make that knowledge easier for both people and AI-assisted discovery tools to find and trust.
As AI takes on a larger role in discovery, businesses need to consider whether they are creating the kind of credible, original information that humans and AI systems have reason to reference. There is a trust dimension here as well. Search rankings have always attracted people trying to game the system, and AI discovery will be no different.
For most of the past two decades, marketers fought for visibility on Google. Being discoverable online meant being discoverable through search. Companies invested heavily in SEO because Google often sat between a potential customer and the business they eventually found. The winners mastered keywords, paid advertising, and useful content to earn their place.
That intermediary is starting to change.
Customers are increasingly asking AI tools like ChatGPT, Copilot, Gemini, Claude, or Perplexity the questions they once typed into Google, particularly when they want help comparing options, understanding an issue, or narrowing down a decision. Instead of sorting through a page of search results themselves, they may receive a synthesized answer without ever visiting a company’s website.
A prospective client might ask an AI tool which consulting firms serve associations. A procurement team could use AI to research potential vendors before anyone reaches out. A job candidate may ask what an employer is known for. An investor could use AI to understand a company or market. A customer can describe a very specific business problem and receive suggestions about where to look next.
For businesses, that introduces a relatively new consideration: what does AI know about us, and what gives it a reason to include us in the answer?
As AI becomes an intermediary between companies and prospective customers, businesses need to think about whether they are creating the kind of credible, original information that both humans and AI systems have reason to trust and reference. There is a trust dimension here, too. Just as search algorithms attracted attempts to game rankings, AI discovery will attract bad actors looking to manipulate what appears in generated answers. Being visible is only part of the equation; organizations also need to give customers—and the systems informing them—credible reasons to trust what they find.
Predictably, an industry is already forming around this question. You'll hear terms such as Generative Engine Optimization (GEO), Answer Engine Optimization (AEO) and several other variations depending on who is selling the service.
The basic idea is fairly simple. Traditional SEO focuses heavily on optimizing content to help webpages rank on search engines. GEO and AEO focus on increasing the likelihood that AI systems will reference, summarize, cite, or recommend a company's content in generated responses.
For years, marketers asked, "How do I rank?"
Increasingly, it is becoming: "How do I become part of the answer?"
That should change how we should think about an organization's digital knowledge footprint. Case studies demonstrate that you've actually done the work. Original research gives the wider ecosystem information it didn't previously have. Expert articles can document observations that come from repeated exposure to a problem. Clear service pages establish what you actually do and who you do it for.
All of that information contributes to the wider knowledge environment that AI-assisted search and research tools draw from.
In some ways, it resembles to early days of SEO. Businesses are once again trying to understand how a new discovery channel finds, interprets and prioritizes information about them. We went through a similar learning curve with search engines, social platforms and online reviews.
The mechanics are different this time, but the marketing instinct is the same: understand how people are finding you, then make sure what they find is useful.
As with any new marketing channel, there is an obvious temptation to look for shortcuts.
Generative AI has dramatically lowered the effort required to produce competent content. Businesses can publish more articles, social posts, guides, reports and commentary without increasing their resources at the same rate. Used well, that can be genuinely helpful. It also makes it remarkably easy to manufacture the appearance of authority.
Imagine a cybersecurity company generating thousands of articles, blog posts, reports, social media posts, and white papers around the topics it wants to be known for. If AI systems ingest enough of that content, the company's views may become increasingly represented in AI-generated answers and flooding that environment with content can start to look like a reasonable strategy.
Let's discuss the obvious flaw in that approach. Artificial intelligence does not create knowledge. It reorganizes it. If your competitor asks an AI tool to write "Five Cybersecurity Trends Mid-Sized Businesses Need to Know" and you ask another AI tool essentially the same question, you're both drawing from a similar pool of existing information. The wording may differ. The underlying insight probably won't.
Producing ten times more of that content doesn't necessarily create ten times more authority.
And as more synthetic material enters the information ecosystem, another problem emerges. AI-generated summaries begin drawing from articles that may themselves have been summarized or generated from other secondary sources. Ideas get repeated without much new information being added.
From a marketing perspective, the implication is fairly straightforward: adding another generic summary of something already covered everywhere is unlikely to differentiate your organization, regardless of whether the reader is a person or an AI tool helping that person conduct research.
When content becomes easier to produce, the source of the insight becomes more valuable.
There is an opportunity here for mid-market organizations…give the market something it doesn’t already have. This might sound like the obvious answer but in a world flooded with AI-generated content, authentic experience becomes more valuable. In practice, many organizations overlook how much useful knowledge is already being created inside the business.
Your consultants notice patterns across engagements. Sales teams hear questions that aren't being answered well in the market. Customer research reveals changing priorities. Internal data can show how an industry is evolving. Project teams learn what works in practice and, often more usefully, what doesn't. Customer research may reveal priorities that are shifting well before those changes show up in an industry report. That is much more interesting source material than another search for “blog ideas for September," and this is where your real expertise becomes a competitive advantage.
Instead of asking, "What should we write about this month?" marketing teams can start asking, "What have we learned recently that our customers would genuinely find useful?"
That's a good content-marketing principle even if GEO disappears tomorrow.
Organizations should be thinking less about producing endless volumes of content and more about becoming primary sources of knowledge.
There are several places to start:
Start With What Your People Know. Interview subject-matter experts about the patterns they're seeing, the difficult problems they've solved and where their experience differs from conventional wisdom. Years spent solving real-world problems add useful nuance that is difficult to recreate from secondary sources alone.
Conduct Original Research Where it Makes Sense. Source information when the market doesn't have it. Industry surveys, benchmarking studies, customer research, and market analysis can answer questions that existing content cannot. If your organization has access to useful data or recurring observations, consider how those insights can be aggregated and shared appropriately.
Document Customer Experience. Use your work as a source of insight. Case studies can do more than prove that a project went well. Real successes, failures, lessons learned, and implementation stories can document what changed, what was learned and what other organizations facing similar problems should consider.
Share Proprietary Data...Within Reason. Unique business metrics, operating statistics, and trend observations are difficult to duplicate.
* There is an important caveat here. Creating original knowledge doesn't mean publishing everything you know. Proprietary information, intellectual property, confidential client information and strategically valuable internal knowledge all require careful consideration before they become content.
Publish Expert Perspectives. Original thinking doesn't help much if it lives in someone's head. Clear service information, expert profiles, structured articles and well-maintained digital content give prospective customers—and increasingly the AI tools helping them research—something concrete to work with. Years of experience solving real-world problems generate knowledge that AI cannot independently create.
The opportunity is to become more deliberate about identifying what your organization knows, what your market would genuinely benefit from knowing, and where the boundary between the two should sit.
For years, marketers have asked how to optimize for Google. Increasingly, they will ask how to optimize for, and influence, AI.
We're still early in understanding how AI-mediated discovery will change buying behaviour, and the mechanics will continue to evolve. Rebuilding an entire marketing strategy around guessing what an AI model might cite next would be premature. But, paying attention to the direction of travel will put you in a better position to react.
If customers increasingly use AI to research problems, compare options and identify potential providers, organizations need a digital presence that gives those systems something credible to find/work with. Publishing more content isn't necessarily the answer. Publishing more of what your organization actually knows is a better place to start.
That may mean interviewing your experts instead of asking AI for another topic. It might mean finally turning your customer research into an industry report, documenting what you've learned from a recurring client challenge, or developing a stronger point of view on something your team understands particularly well.
These were good marketing practices already. They become even more useful as AI increasingly sits between your expertise and the people looking for it.
Your organization already has knowledge worth paying attention to. The question is what you do with it.
AI is changing more than how organizations create content. It is affecting how customers find information, how employees work with organizational knowledge, and how businesses think about the ideas and intellectual property they create along the way.
Stratford works with leadership teams of mid-sized organization to understand those implications and make practical decisions about where AI can create value, where greater capability or governance is needed, and what organizational knowledge should be shared or protected.
If AI is changing how your organization creates, protects or communicates what it knows, let’s talk about what that means for your business.
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Natalie Giroux is the founder and former President of Stratford Intellectual Property. Retiring from that role in 2025, Natalie continues to support Stratford as an executive advisor. With deep expertise in strategic IP management and a business-first approach, Natalie has supported over 100 companies in aligning their IP portfolios with growth objectives. She has been internationally recognized multiple times as a leading IP strategist, including being named to the IAM Strategy 300 list. She is passionate about maximizing the value of innovation. |