AI is exposing the longstanding Enterprise Website Complexity Problem
Enterprise complexity has become a business risk in the age of AI. Enterprise websites grow through acquisitions, new product launches, CMS migrations, and years of content created by different teams. It’s not unusual for large organizations to manage dozens, or even hundreds, of domains across multiple platforms and languages.
Historically, this complexity created governance challenges. Keeping content, terminology, and structured data consistent across a large digital ecosystem has never been easy.
As AI becomes a primary way people discover information, those governance challenges no longer stay behind the scenes. They directly influence how AI understands and represents your business.
AI systems don’t look at your website one page at a time. They build an understanding of your business from everything they can access across your digital ecosystem.
When those signals are consistent, AI can confidently understand who you are and what you offer. When they’re fragmented or contradictory, AI has to fill in the gaps.
That’s where a longstanding content-governance problem becomes a brand-representation problem in AI-powered discovery.
The challenge with complex websites is governance
After working with enterprise organizations across healthcare, financial services, eCommerce, technology, education, and other industries, we’ve learned that every organization has its own version of “complex.”
For some, it’s managing hundreds of domains after years of acquisitions. For others, it’s operating dozens of regional websites with different languages, regulations, and local marketing teams. We’ve worked with organizations migrating from one CMS to another while old and new websites coexist for months. Others manage content across Adobe Experience Manager, WordPress, Drupal, Sitecore, and custom-built platforms.
None of these situations are inherently problematic, but the challenge is maintaining a single, governed representation of your business across all of them.
Our customer InSinkErator provides a great example of these challenges after Whirlpool acquired the brand from Emerson Electric in 2022. The acquisition was complete from a business perspective, but search engines continued associating InSinkErator with its former parent company, leaving an outdated representation of the brand in search and AI.
In partnership with Schema App, InSinkErator updated its Website structured data to identify Whirlpool as the new parent organization and aligned supporting entity definitions through entity linking to Wikipedia and Wikidata. These clearer, connected signals helped Google recognize the new relationship and update InSinkErator’s identity in its Knowledge Graph and organic listings.
It’s a good example of how enterprise complexity can create a gap between what the business knows to be true and what machines understand. Acquisitions, rebrands, migrations, and other organizational changes need to be reflected in the semantic data behind the website, or outdated relationships can continue shaping how the brand is represented in search and AI.
AI responses depend on the quality of your digital ecosystem
Customers can usually recognize that two pages are talking about the same product, even if the wording is different. AI doesn’t have organizational context. It can’t distinguish between an outdated page, an acquired brand, or a regional variation unless your digital ecosystem provides enough context to connect those pieces together.
It doesn’t know one website came from an acquisition or that another is being phased out during a migration. It doesn’t understand that different regions intentionally adapt messaging for local audiences. It only sees the information that’s available and tries to reconcile it into a single understanding of your business.
Without a governed semantic layer connecting those signals together, AI has to infer how your business information fits together. The more it has to infer, the greater the chance that important details are overlooked, misunderstood, or represented inaccurately.
Why traditional Schema Markup isn’t enough
This is often where enterprise teams get stuck.
They recognize they need structured data, so they begin implementing Schema Markup across key pages. That’s an important first step, but it doesn’t always account for the dynamic nature of large-scale websites.
Enterprise websites are constantly changing, and when governance breaks down at scale, AI doesn’t stop generating answers. It generates answers using whatever information it considers most trustworthy, even if that information is outdated or from a third-party source.
We’ve seen firsthand how quickly this can impact brand representation. Our customer, Wells Fargo, discovered that Google’s AI Overviews were citing a decades-old third-party news article instead of its official branch locator pages when users searched for branch hours. Customers were being directed to outdated information that incorrectly stated a branch was closed because AI lacked strong, authoritative signals pointing to the correct brand-owned source, which at the time did not have accurate structured data.
Just hours after implementing advanced Schema Markup across their locations page, Wells Fargo saw AI Overviews start to correctly source their official branch locator pages. The AI now had the correct data and context to surface accurate and up-to-date, brand-controlled information.
As Wells Fargo put it:
We didn’t know how to fix the AI Overview hallucination problem, but Schema App helped us solve it. AI Overviews went from citing a decades-old news article to referencing our accurate branch locator pages within hours. That’s a huge win.”
The Wells Fargo example makes it clear why enterprises require more than a one-time Schema Markup implementation if they want brand-control in AI. As websites evolve, the semantic data behind them needs to evolve too. A governed semantic data layer keeps that understanding accurate and connected, helping search engines and AI represent your business with greater confidence.
How Schema App helps enterprises manage complexity at scale
This is exactly the challenge Schema App was built to solve.
Schema App creates and maintains a dynamic semantic data layer through a Content Knowledge Graph that evolves alongside your business. Whether you’re managing hundreds of domains, multiple regions, or ongoing website changes, it keeps your semantic data aligned so search engines and AI systems have a consistent understanding of your business.
The result is a trusted, machine-readable foundation that scales with your business, reducing the need for AI to infer meaning by providing a brand-controlled, trusted semantic representation of your business.
Andrea Badder is the Content and SEO Manager at Schema App. She creates educational content that helps marketing teams understand how structured data and Content Knowledge Graphs drive visibility and accuracy in search and AI. Prior to joining Schema App, Andrea worked as a brand strategist and copywriter at a marketing agency. She is a graduate of the University of Guelph.