Given AI’s prominence across business operations, it’s no surprise that AI-enhanced knowledge bases are the next must-have tool. Companies strive to exceed customer expectations throughout the user journey, and knowledge management is essential to their success. After all, customer expectations are becoming harder to meet without innovative technology. 72% of customers expect immediate service. Meanwhile, 62% of CX leaders feel they can’t keep up with the level of instant experiences that consumers expect. Say goodbye to fumbling customer demands. Build consistent experiences, provide faster service, and deliver relevant information to every customer with an AI knowledge base.
Definition of an AI Knowledge Base
An AI knowledge base is a self-service repository of centralized content. It includes relevant information for users to access on-demand knowledge about a company’s products and services: blogs, how-to’s, user guides, FAQs, videos, installation manuals, etc. This differs from a traditional knowledge base because it leverages Natural Language Processing (NLP), Machine Learning (ML), and Generative AI (GenAI) to deliver a more accurate, relevant, and engaging content experience. These technologies enhance support agent productivity and improve customer self-service.
Importance of AI in Knowledge Management
It shouldn’t come as a surprise that AI is poised to disrupt the knowledge management process. Company leaders seem to agree with 80% of organizations viewing GenAI as one of their main levers for reinvention. For good reason too! AI automates the extraction of information, resulting in accelerated search and response times. This improves knowledge discovery, helping customers access the right information faster.
This relationship isn’t just one-sided either. Information and knowledge documentation are equally crucial for AI. The effectiveness of AI in knowledge management directly depends on the quality of the data. Accurate, comprehensive, and up-to-date information is crucial for AI systems to perform optimally and provide reliable content.
However, companies can’t rely on Large Language Models (LLMs) alone. Training data for LLMs is often significantly outdated and LLMs don’t have access to your specific product information. So, when they don’t have the facts, they tend to extrapolate and produce false yet plausible-sounding statements (also called hallucinations) to fill the gaps in their knowledge.
That’s where techniques like Retrieval Augmented Generation (RAG) come in. RAG allows you to use your own data sources to enhance the LLM’s knowledge on-the-fly and generate more contextual and accurate responses. And this is a game-changer for creating AI-powered knowledge bases and intelligent chatbots.
Benefits of Using an AI Knowledge Base
An AI knowledge base isn’t just a way to access simple automations and specific answers. It offers concrete benefits to customers, support teams, and business operations beyond the value of traditional content repositories.
- Consistent Content Experience: Customer touchpoints with a company often have different content available, leading to a confusing and disjointed experience. Any touchpoints attached to the AI-enabled hub will connect to all relevant information. As a result, users will receive accurate and consistent search results and AI-generated responses whatever the channel they use.
- Enhanced Self-Service: 67% of consumers prefer self-service options over speaking to a company representative. An AI-enabled knowledge base offers a search engine that recognizes the intent of natural language inputs to understand each user’s query. Then it scans the knowledge base for relevant information and delivers contextually relevant answers using RAG. Hubs with AI-enabled chatbots also sustain back-and-forth dialogues with customers by remembering and expanding on past questions to enhance accuracy.
- Reduced Support Costs: Support teams are already flooded with tickets, yet 81% of customers expect service to become even faster as technology advances. Meet expectations while reducing costs by automating answers to simple, repetitive user questions. AI knowledge bases are available 24/7 to answer customers, leading to better response times without putting the burden on the support team. In addition, AI can seamlessly help multiple users at once. Thus, it offers a scalable support tool to handle a growing volume of basic customer questions without increasing personnel costs.
- Increased Agent Efficiency: AI-enabled knowledge bases support both new and seasoned agents. New agents spend more time looking for answers, leading to high resolution times. Experienced agents waste precious time responding to simple, repetitive questions. With this AI resource, fresh hires are autonomous at finding information and sharing the best answers with clients. As a result, experienced agents focus on more complex level 2 and 3 help desk tickets.
Pre-Requisites for an AI Knowledge Base
Natural Language Processing and Machine Learning are indispensable prerequisites for developing an effective AI knowledge base.
Natural Language Processing
Natural Language Processing refers to how a computer program understands spoken and written human language. This allows humans to fluidly interact with computers using regular sentences.
NLP technology is crucial for enhanced knowledge bases because it takes search question-and-answering to the next level and makes sense of the content gathered from all sources. A semantic search engine employs natural language understanding to decipher the precise context and intent behind each user query. This allows the solution to provide each user with high-quality responses that are contextually relevant and applicable.
Machine Learning Capabilities
Machine Learning is a subset of Artificial Intelligence. It consumes data to identify patterns and to imitate how humans learn to improve the accuracy of its outputs.
Within AI-powered knowledge bases, ML works alongside NLP to learn about each user. When someone submits a search request, the model uses Machine Learning to study the user journey. This helps the algorithm determine what each user needs, what they want, and therefore, what information to provide in response. By understanding user behavior patterns, ML enriches personalization functionalities and provides more relevant responses.
Key Features of an AI Knowledge Base
There are several applications of NLP and ML as well as complimentary features that will upgrade your AI knowledge hub. Make sure your knowledge base is future-proof with the following essential features.
Chatbots and AI-Driven Automated Responses
Chatbots aren’t new. Yet, the latest version of Generative AI-enabled chatbot technology offers an elevated user experience. Integrate a conversational question-and-answer layer over your existing knowledge base data to access this human-like upgrade. As a result, users submit questions in natural language to a chatbot (or search bar interface) and receive answers that feel equally human! And all this in mere seconds with accurate, specific information.
Customers can keep the conversation going, as the AI chatbot remembers each previous question during the discussion. Take Fluid Topics’ Question Answering application as an example. It attaches links to the technical documentation to back up its answers and direct users where to go for further details. This empowers users to autonomously solve issues without submitting help desk tickets for every product question.
These AI-driven automated responses introduce a new era of conversational search between businesses and users. Like ChatGPT specialized for your company’s product knowledge.
Relevant Content Recommendations
When companies recommend content that isn’t based on the customer’s queries and behavior, user frustration rises. The importance of relevant content is confirmed with 71% of customers stating they expect a personalized experience and 76% reporting frustration if they don’t get it.
Organizations with an AI-enabled Content Delivery Platform (CDP) offer a solution that easily provides specific knowledge to each user based on their preferences and profiles. Unlike a standard knowledge base, these CDPs prioritize results based on content relevance, context, and user behavior.
Personalized User Experience
By leveraging advanced algorithms such as ML and NLP, AI can tailor content and recommendations based on individual user preferences, past behavior, and profiles.
Additionally, content metadata management elevates this process by ensuring that documentation is correctly classified and structured efficiently. This allows AI algorithms to filter and retrieve only the information that is applicable for each user. Personalized answers ensure users get information based on their specific profiles and needs while minimizing compliance risks.
Easily Configurable UI
Create a fluid customer journey with a low-code, no-code UI. Platforms like Fluid Topics make it easy to customize the knowledge base with a WYSIWYG editor that allows companies to drag and drop components into place.
Custom designs that blend seamlessly with an organization’s branding elevate content delivery to offer an engaging, personalized experience.
Challenges of an AI Knowledge Base
The many valuable features of AI knowledge bases produce various business benefits. However, this isn’t to say that implementing new AI projects is smooth and easy. There are a few key challenges to keep in mind when selecting your knowledge solution.
Freshness of Content
Keeping documentation up to date is challenging with the quick pace of product launches and updates. When content isn’t continuously updated, customers can’t find the information they need — and neither can AI systems. Generative AI tools can only produce text based on something they’ve read. Therefore, without updated documentation, the AI knowledge base will provide inaccurate, outdated answers leading to customer frustration and longer product downtimes.
Provide your AI models with the latest content as soon as it’s ready with a CDP. In a single click, CDPs gather content from all sources. This means any AI-enabled search engines or chatbots automatically access up-to-date content without needing to rely on IT resources.
Robust Security Measures
AI tools are a great source of cost-saving opportunities. However, they also present new security challenges. So new, in fact, that over 90% of privacy and security professionals report companies need new techniques to manage these risks. The biggest risk? Sharing confidential information with external or unauthorized users. So, what should you look for in an AI knowledge base to ensure top-notch security?
- Internal Embeddings Management: Solutions that incorporate embeddings computation and a vector database in their architecture are more secure. This ensures that content in the knowledge base is 100% internally processed by the AI model and never leaked to external systems.
- Content Access Management: Choose solutions that apply content access rights to regulate which information appears in search results and AI-generated responses. That way, only authorized user profiles receive restricted documentation. This is critical for protecting internal information.
Fluid Topics for Your AI Knowledge Base
Tackling these challenges doesn’t have to be complicated and expensive. Fluid Topics positions itself as an AI-powered Content Delivery Platform which ingests all types of product content and unifies them into a smart knowledge hub. Centralizing all content into a single source of truth powers your AI knowledge base by giving AI algorithms the possibility to deliver accurate, relevant, and secured information.
On top of that, Fluid Topics provides an AI Gateway that allows developers to implement AI quickly and independently, without the need to build AI infrastructure or manage the associated cross-functional capabilities. They can effortlessly choose and transition between one or more Large Language Models such as OpenAI, Azure OpenAI, Claude, or Gemini. As an LLM Gateway, Fluid Topics incorporates secure-by-design governance mechanisms into its infrastructure. Connecting easily to all content sources and formats ensures all your enterprise applications obtain the latest content updates in real time.
With Fluid Topics, companies can equip their organizations with out-of-the-box core services for powering RAG scenarios that meet the users where they are. As a result, content is accessible from any customer touchpoint, including inside a chatbot, on your website, within your support channels, from virtual assistants, and via in-app troubleshooting.
AI Application: Enhanced Reading Experience
Furthermore, Fluid Topics gives companies the power to configure ready-to-use GenAI applications to create unique and engaging reading experiences. From customizing AI prompts for generating content summaries and step-by-step instructions, to code translations, lists of tools for field interventions, and product knowledge quizzes, your imagination is the only limit. These drag and drop AI widgets help users extract precise information from their search results for a smooth, efficient content experience.
AI Application: Augmented Support Agent
Within the Fluid Topics AI-powered knowledge base, support teams can run the Augmented Support Agent application. Embedded directly within your help desk apps (like Jira or Zendesk), this smart assistant automates answers to simple customer questions to provide relief to agents overwhelmed with support tickets. Connected to the organization’s centralized hub of product knowledge, this digital assistant generates relevant answers for each query. Agents can even fact-check every answer in a click and modify the response if needed.
Future Trends in AI Knowledge Bases
AI technology has evolved significantly over the past two years and will continue to do so in the coming years. GenAI has certainly spearheaded these advancements, leading to new applications using natural language. New capabilities will continue to emerge. In the world of knowledge management, the growing demand for self-service will drive these developments.
Advances in AI Technology
With AI technology rapidly advancing from one month to the next, it can be hard to keep up. What potential AI developments are positioned to impact knowledge bases?
- Expanding Search Options: LLMs have already changed how we search, and they will continue to do so. Will we ask more questions in natural language? Will customers come to prefer voice-driven interactions? The combination of NLP and ML will unlock new ways for users to search and interact with company support portals.
- Human-Like Responses: Boundaries will continue to blur between AI chatbots and customer support agents. AI models will train on new data sets to expand their natural language understanding and language generation skills. As a result, AI-generated conversations will become as natural-sounding as human agents.
- Predictive Customer Service: As AI continues to improve, platforms will be able to analyze customer behavior patterns to predict their needs throughout the user journey. For example, by identifying a potential product question, the platform may reach out to the user with proactive support.
- Increased Personalization and Contextualization: ML algorithms will continue to study user patterns and behavior. With each user, ML will analyze the success of their interactions. This feedback will allow the model to continue to refine its suggestions to optimize its content delivery.
Integration with New Technologies
Augmented Reality (AR) and Virtual Reality (VR) technologies are still improving and not yet as widespread and user-friendly as GenAI-enabled tools. As they become more prevalent, companies will need new ways for users to reach knowledge content from AR and VR-embedded devices. From voice-enabled search for hands-free interventions, to responsive reading experiences based on new devices, many updates are sure to emerge in this space.
Final Thoughts
Integrating AI technology into traditional knowledge bases will revolutionize how businesses deliver technical documentation. It will allow companies to provide a seamless customer support experience, offer an intuitive self-service platform, and ensure quick access to accurate information. While some challenges persist when deciding how to launch new AI projects, Content Delivery Platforms like Fluid Topics make it an easy, safe process.
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