Boosting How Customers Trust AI
For businesses today, building trust in AI is essential. Customers expect AI systems to deliver accurate, transparent, and fair interactions. According to research by Salesforce, 61% of users expect virtual agents to provide reliable and thorough answers. With such high expectations, companies face a challenge: how can they ensure customers trust AI in every interaction?
The whitepaper, Knowledge Management in the Virtual Agent Era, offers valuable insights on how companies can build customer trust in AI. By combining data accuracy, transparency, and human values, businesses can make virtual agents trustworthy partners in customer interactions. Below, we’ll look at key strategies borrowed from this ebook that foster how to trust AI.
Accuracy: The Core of Trust in AI
Accuracy is essential for customers to trust AI. Knowledge management (KM) lets businesses gather, organize, and deliver information effectively. It makes virtual agents dependable resources. Technical approaches to knowledge management have been enterprise goals for at least the past 3 decades. Today’s AI-driven KM systems aim to deliver the right answers at the right time.
In this regard conversational AI agents play a crucial role. They are meant to deliver accurate answers to customer inquiries, around the clock, and in high volumes. As the global KM market is expected to grow from $773.6 billion in 2024 to $3.5 trillion by 2034, at an annual growth rate of 16.5%, companies recognize the increasing need for high-quality KM systems to boost customer trust in AI.
Reliable Data with Graph Databases
For AI to be trusted, it needs a reliable data structure. Knowledge Graph based-AIs are a solution to this challenge. They offer stability and organization that support accurate information retrieval. Unlike traditional databases, knowledge graph databases link related data points. That enables AI systems to pull relevant information with speed and precision.
The whitepaper explains that “we need the accuracy, scalability, and performance of a knowledge graph databases combined with conversational access” as the sweet spot of what organizations need today. This powerful combination allows AI-driven virtual agents to access verified, well-structured information. It helps customers trust AI as a reliable source of data, that doesn’t need an additional verification.
Reducing AI Errors through Human Oversight
One of the challenges in building trust in AI is addressing AI “hallucinations”—errors in generated responses. Large Language Models (LLMs) like ChatGPT sometimes produce convincing responses that are factually incorrect, which erodes customer trust. According to one study from Oxford University, “LLMs hallucinate at least 58% of the time.” This makes human oversight essential to ensure accuracy.
To address this, kama.ai’s Graph-AI platform introduces what’s known as the Sober Second Mind® concept. This combines AI with human oversight to validate responses before they reach customers. It is a crucial step for situations where accuracy and getting the right answer is vital. Combining Retrieval-Augmented Generation (RAG) and knowledge graphs, kama.ai’s system filters content for accuracy, reducing the risk of misinformation. This approach helps ensure customers can trust AI to provide truthful, reliable answers aligned with the organization’s standards. In other words, you get answers that are on-brand. No concerns about hallucinations, biased answers, or offensive statements.
Transparency
Transparency is key to building trust in AI. Customers value clear, honest communication, and they are likely to distrust “black box” AI systems. Black box solutions are those in which decision-making is hidden. Kama.ai’s whitepaper emphasizes the importance of not accepting such solutions. Rather, it is important to insist on transparency, taking a position that knowledge management systems must be “transparent, accessible, and rooted in truth.”
A “human-in-the-loop” model supports transparency by allowing AI systems to hand over complex or uncertain queries to a human agent when needed. Once a situation becomes too complex for the AI agent to handle independently, it must have the ability to realize this, and to handoff the case to a human agent. This approach not only ensures accurate answers, but also ensures that delicate or sensitive matters are dealt with appropriately. In these cases, it is better dealt with by humans who can assess more complex issues. Using this mechanism effectively, can also give the system a way to learn and improve over time. Combining human and AI efforts in this way balances efficiency with the need for trustworthy interactions.
Personalized AI Through Emotional Intelligence
Yet another powerful way to increase customer trust in AI is to personalize responses. Emotional intelligence (EI) lets AI systems adjust responses based on each customer’s tone and context. The whitepaper highlights the importance of aligning AI responses with human values, creating more meaningful customer interactions.
Virtual agents using EI are better equipped to address customer concerns with empathy, making interactions feel authentic and personal. By prioritizing human values, AI builds deeper customer trust and strengthens brand loyalty.
The Foundation of Successful AI is Trust AI
Building trust in AI requires more than just accurate answers—it demands transparency, human oversight, and alignment with customer needs. When customers trust AI to provide fair, transparent, and human-centered responses, they see AI as a valuable partner. By integrating features like graph databases, emotional intelligence, and human oversight, businesses create AI systems that meet customer expectations and reinforce trust.
For more in-depth insights on building trustworthy AI systems, explore kama.ai’s whitepaper, Knowledge Management in the Virtual Agent Era. This guide offers strategies for creating AI solutions that deliver accurate, transparent, and reliable customer interactions. Just click on the link… there are no forms to fill, and no strings attached.