Will your AI confidently deliver the right answers or stumble through outdated knowledge while your customers grow increasingly frustrated?
Artificial intelligence may be changing how businesses interact with customers, but there’s a critical element that often gets overlooked: the knowledge that powers it. The quality of AI responses directly depends on the information it can access — a relationship that becomes increasingly important as more organizations deploy AI for customer service.
AI is really good at accessing unstructured and structured data and collating it into a well-packaged natural language response. Unlike when you do a Google search and it comes back with multiple responses (where the level of those answers is largely driven by advertising or other sponsorship), AI looks at the body of knowledge that supports the question being asked.
So when talking about knowledge-driven AI for customer experience, the idea isn’t that AI is accessing the full scope of institutional knowledge, but rather a well-structured knowledge base. This means companies must carefully choose what information AI can leverage, especially when dealing with decades worth of data.
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For example, a customer asking how to make a payment might receive outdated instructions about writing a check if the knowledge base contains too much legacy content. By providing a well-structured database that is rich enough to give as many answers as possible but also limits AI to that particular knowledge base, you can really focus on giving AI the right information to deliver the answers you want customers to receive.
The Specificity Advantage
When building AI knowledge bases, starting small and narrow before expanding works better than beginning with everything and trying to narrow down.
Companies often make the mistake of giving AI access to their entire information universe. This approach typically creates more problems than it solves. Contact centers especially struggle with AI accuracy when the knowledge base contains outdated information or when AI draws from too many different sources at once.
This limitation becomes obvious when you consider AI-generated images. When AI attempts to create images of people, it often produces noticeable errors — too many fingers, oddly positioned hands or unnatural facial features.
AI conversations follow the same pattern. They appear fine at first glance, but closer inspection reveals gaps in understanding, inappropriate tone and mechanical empathy. The information provided might be technically correct but lacks the nuance and specificity that customers need. Just as with images, these conversation models improve over time, but the fundamental challenge remains — AI needs well-structured information to avoid these pitfalls.
Experiential Learning Over Algorithms
Ultimately, AI delivers its most reliable performance when confined to specific knowledge and topics. Unlike human agents, AI performs best when it follows a script.
This creates an interesting contrast with what we’ve learned in the business process outsourcing [BPO] industry. Our experience shows that human agents excel when given freedom to go off-script and apply their natural problem-solving abilities. The best human interactions happen when agents bring their full selves to the conversation.
AI, however, functions more like a trainee who needs clear boundaries. You want to keep AI narrowly focused on approved scripts and content until it develops more sophistication. Human agents can provide answers beyond their formal training. They navigate complex systems, find creative solutions and interpret customer needs in ways that aren’t documented. These skills develop through experience and remain challenging for AI to replicate.
Today’s AI systems can’t navigate through interfaces like humans can. They can’t click through multiple screens, follow complex processes or interact with CRM systems the way human agents do. AI only knows what exists in its knowledge base. This limitation highlights why incorporating the lived experience of human agents into AI knowledge bases delivers such dramatic improvements.
AI also differs from humans in its approach to uncertainty. It never lacks confidence, even when wrong. AI will state incorrect information with complete certainty if its algorithms determine that’s the optimal response.
Human agents learn differently. When customers express frustration or correct a mistake, human agents experience that uncomfortable, “Oh my gosh” moment that embeds the learning in their conversational memory. Even with limited information, humans adapt quickly. Most AI systems lack this emotional feedback loop, which raises an important question: How do we configure AI to incorporate negative feedback into its knowledge in a meaningful way?
Information Architecture is an Investment
Creating effective AI knowledge bases requires ongoing attention across several dimensions. The foundation must consist of structured, up-to-date content that accurately reflects your products and services. This isn’t a one-time effort but a continuous commitment to maintenance and accuracy.
Equally important is establishing appropriate boundaries — giving AI enough knowledge to be helpful while limiting its ability to access irrelevant or outdated information. Improvement must be continuous rather than occasional. By monitoring where AI struggles and systematically addressing those gaps, organizations keep their systems relevant and effective.
Integrating successful human agent interactions represents another critical factor. When you capture what works in human conversations and incorporate those patterns into your AI knowledge base, performance improves significantly.
Finally, robust feedback mechanisms allow AI to learn from customer responses without being susceptible to manipulation, creating a system that improves over time.
AI technology will continue evolving, but its effectiveness will always depend on the quality of its knowledge foundation. Organizations that invest in properly structured, well-maintained knowledge systems will see better results from their AI implementations.
The future isn’t just about deploying more sophisticated AI technologies; it’s about building better knowledge ecosystems that these technologies can leverage. Your AI is only as good as the knowledge base it’s built upon, and getting that foundation right is essential for delivering the customer experience you actually want.
Hans Zachar is the Group Chief Technology and Information Officer at Nutun, a global leader in omnichannel business process outsourcing (BPO) services. With over 19 years of experience in technology across multiple industries, including financial services and telecommunications, Zachar joined Nutun in March 2023. At Nutun, he spearheads the integration of advanced technologies, such as AI and data analytics, to enhance customer interactions and operational efficiency.
