And Why Trusted Knowledge Is the Real Foundation for AI Success

Most AI reliability problems aren’t model problems. They’re knowledge problems.

Consider Millennium Tower, the “Leaning Tower of San Francisco.” When it opened in 2009, it looked flawless from the outside, but homeowners began noticing signs that something wasn’t right. Balls rolled across kitchen floors on their own, and doors didn’t close the way they should. The building wasn’t just settling like normal. It was leaning.

The cause? Some early assumptions about the site didn’t match the real conditions. Trust faded as owners moved out, sold at losses, and faced a growing mess of legal and structural challenges. Once confidence was gone, it became hard to restore.

That’s exactly what happens when organizations build AI on top of assumptions instead of a solid foundation of trusted knowledge. Everything looks fine at first, but the cracks show once people start relying on it.

Early AI results feel promising, but once people rely on AI in their day-‑to-day work, gaps begin to appear. An answer doesn’t match policy, or AI recommends a workflow step that doesn’t reflect how the work is currently done. Something contradicts the experience of someone who has been in the role for years. None of these is that significant by itself, but together they create hesitation and incremental operational costs that add up over time.

When people try to figure out why their AI feels unreliable, they often focus on models or data. The deeper foundation rarely gets a second look, and that’s where the trouble tends to hide. The content and knowledge that AI depends on is usually scattered across old documents, email messages, and outdated folders, or even worse, it’s buried in people’s heads.

Without clean and structured content to act as a foundation, AI has no real sense of what’s current and what’s left over from an older process. That’s when the familiar issues start to show up, including confidently wrong answers, small hallucinations that make headlines, and guidance that doesn’t match how work really happens.

By the time anyone traces the problem back to the knowledge foundation, the business has already absorbed the cost in failed AI pilots, employee time lost to bad answers, and frustrated customers who didn’t get their issue resolved.

The role of knowledge management in AI success

The problem with AI is almost never the model. It’s the knowledge underneath it. Content is everything a company has ever written down. Knowledge is the refined subset people rely on – the policies, regulations, SOPs – the guidance that tells someone what to do and when.

Modern knowledge management isn’t a SharePoint folder or a decade-old content repository. It’s the ongoing process of keeping that guidance clear, current, and trustworthy. When that works, the savings are immediate with less time searching, fewer escalations, and fewer corrections.

What a solid knowledge foundation looks like

The organizations that get AI right don’t start with the model. They start with the questions customers and employees ask. Calls, chats, and tickets reveal the content and questions that matter most to different audiences, the answers provided, and the quality and gaps of those answers. Those questions become the starting point for better guidance.

From there, the focus shifts to turning what people know into something consistent.  It often means comparing the guidance that already exists, resolving contradictions, and deciding which version reflects how the work truly gets done. When different teams have different answers, this process can bring everything together into one reliable version.

Organizations can also tailor this information for different teams and roles. A repair technician in the field needs different details than someone in customer service. A new hire needs more context or guidance than an experienced hire.

As knowledge becomes more reliable, organizations build habits and processes to keep it aligned. Conflicting answers get resolved when they surface, outdated information gets revised, and the guidance stays in step with how the business works today. And because systems are connected, one update spreads to the places where people and AI need that information.

A Solid AI Foundation, Proven in the Real World

Here’s what happens when organizations fix the foundation:

• Liberty Mutual achieved a 10X improvement in speed to answer, a 97% search success rate, and was rated the #1 internal app in their annual employee experience survey across 28,000 contact center users, by building a knowledge operation that could finally keep pace with the business.
• 53% of Solv Energy’s field technicians reported a direct increase in productivity, getting to the right answer in under two clicks and ten seconds, with a unified knowledge foundation that replaced a sprawl of disconnected systems.
• Worldpay now handles over 1 million AI interactions per year with an 87% AI success rate on instant answers, after bringing together 20+ knowledge repositories into a single, consistent source of truth for agents worldwide.

These are different organizations with different stories, but the theme is the same. Issues often showed up in their AI, yet the root cause was a faulty knowledge foundation. Once knowledge became clearer and more consistent, everything else fell into place.

Six steps to a solid AI foundation

Where do you and your organization stand when it comes to AI reliability? Here are a few questions to get you started:

• If your AI gave a wrong answer to a customer today, would you know about it before they did?
• Are your best agents the ones who’ve been around longest, because the documented guidance can’t be trusted?
• If two agents looked up the same policy right now, would they get the same answer?
• When a policy changes, how long before every agent, bot, and self-service channel reflects it?
• If your AI deployment failed an audit tomorrow, could you explain exactly what it said and why?

If any of this sounds familiar, the core issue is probably the knowledge foundation and not the AI model. The good news is that building a trusted knowledge doesn’t require a massive overhaul. Here’s a proven approach that organizations have taken with eGain:

1. Start with the questions people ask most because those highlight where clarity matters.
2. Make sure the best answers are clear, reliable, and easy for anyone to use.
3. Give your knowledge the oversight it needs so conflicting answers get resolved quickly.
4. Deliver answers where people go for help, whether they’re customers or employees.
5. Keep knowledge updated as your processes, policies, and products evolve.
6. Use AI throughout the process to surface gaps and continually keep everything aligned.

The knowledge behind your AI is either an asset or a liability. Which one is yours? Schedule a complimentary AI Content Readiness Assessment to make sure you’re on the right path.