Implementing AI Knowledge Base into Self-service portal transforms user experience and significantly elevates user satisfaction.
Despite significant implementation of chatbots and knowledge bases into self-service portals for customers and employees, businesses continue to struggle with the high demand for human-assisted services. Research shows that self-service portals could fulfill only less than 10% of user requests â a clear signal that something is missing on those portals.
One primary missing link is the availability of up-to-date, accurate information in an easy-to-consume format. Users seek human assistance when the self-service portals do not provide the right information. Your knowledge base plays an essential part in delivering high quality service to your customers or employees. However, knowledge base maintenance is not an easy task with traditional approaches.
In this blog, we will explain why your self-service portals with traditional knowledge bases are failing, highlight key challenges, and explain how AI Knowledge bases transform efficiency and its use cases.
Traditional Knowledge Base Vs. AI Knowledge Base
Traditional knowledge bases rely on keyword-based search, which fetches results based on the keyword presence than the intent and context. In most cases, the search brings irrelevant or incomplete results. As the conventional knowledge bases lack contextual awareness and self-learning, they do not recognize the keyword variations and evolving user issues.
Traditional practices require manual repository maintenance, where subject matter experts (SMEs) must verify and validate content for consistency and accuracy. When the content volume is high, keeping track of the missing pieces is a tedious and inefficient task.
For example, a bankâs self-service portal still lists an old process for credit card dispute resolution, instructing customers to fax a dispute form, even though the bank has moved to an online submission process. As a result, customers follow outdated steps, causing delays and frustration, leading to higher call volumes for customer support and lower customer satisfaction.
How AI-powered knowledge bases transform search & responses process
On the other hand, an intelligent knowledge base when enhanced with cognitive search and Generative AI, can understand the context, user intent and evaluate past interactions. Cognitive search enables data retrieval to surface by using AI-driven indexing, semantic understanding and NLP, while Generative AI uses that data to provide personalized and contextual responses.
The self-service portal with AIâpowered knowledge base delivers prompt responses that help the user solve the issue without waiting for a human agent. And the RAG (Retrieval-augmented Generation) ensures accuracy reducing misinformation risks.
AI-powered Knowledge bases aren’t just about storing data. They can also think, learn, and deliver answers instantly in real-time.
How does the AI Knowledge base work?
AI knowledge bases goes beyond traditional knowledge bases by using cognitive search integrated with Generative AI, Natural Language Processing (NLP) and ML algorithms, enabling contextual and dynamic responses. Instead of forcing users to go through FAQs or user manuals, this system analyzes queries in real time and provides personalized responses based on the latest available data.
These smart knowledge bases integrate both structured content (User manuals, FAQs, help articles, etc.) and unstructured content (live chat conversations emails, social media and multimedia files) to improve the response efficiency. Using Retrieval -Augment Generation (RAG), this system retrieves and synthesizes relevant data dynamically from multiple sources including previous conversations to provide highly accurate responses.
The AI knowledge base can transform this paradigm by
- Extracting Transactional Knowledge from user interactions in real-time and learning from it. This data will be automatically added to the knowledge base.
- Enhancing accuracy and relevance in responses as they are AI-driven.
- Reducing the workload of SMEs in reviewing, analyzing, and sorting data.
- Keeping the knowledge base updated, precise, and actionable.
Types of Knowledge and How are they sourced
| Knowledge Type | Examples | Source |
| Transient Knowledge | Service outages, product shortages. | Situational based, short-living and quickly changes |
| Transactional Knowledge | Use cases, Quick tips, How To’s, workarounds, innovations and insights | Curated from the process of answering questions, and thorough insights analysis. |
| Periodic Knowledge | User guides or manuals, promos, notices | From Product campaigns, and its life cycle |
| Static Knowledge | Billing terms, compliance terms, return or refund policies, partner information | Tied to policies, procedures and regulations |
Source: Gartner
Amongst all the types of knowledge bases, Transactional knowledge is the hardest to capture but has the most reusable value. With AI doing the job, tracking and updating the transactional knowledge happens seamlessly.
Benefits of AI-Driven Knowledge Bases
AI-powered Knowledge base is not just an automated system, it helps users and business operations with a wealth of benefits, like,
Enables Self-service:
AI-powered knowledge bases enable customers or employees to find solutions independently. With AI and ML-driven search capabilities, users can swiftly access the right and relevant information. Self-service integrated with an AI knowledge base can enhance user satisfaction rates.
Streamlines Content Management
AI-powered automation allows content to be sorted based on sources, situations, and relevancy. This intelligent knowledge base identifies information gaps and outdated resources and suggests updating the subject matter experts. Customer or employee conversations over live chat would be evaluated in detail and pinpointed by AI any spike in requests like password resets, outages, etc. This approach saves time for the content management team for a regular health check. Â
Reduces Operational Costs
As mentioned above, an intelligent knowledge base reduces the workload on Content management teams and Subject matter experts and lets them focus on business growth-oriented activities. On the other hand, an exceptional way of handling user requests decreases support tickets and minimizes the workload on support teams. Hence, there are fewer costs and high efficiency.
Enhanced User Experience
Compared to a human agent working on a request, an AI-powered self-service portal is faster and more accurate. It can deliver answers in a cohesive tone with precision. AI collects all types of knowledge bases depending on the situation and utilizes them to answer user queries across various touchpoints with accuracy. Fostering User experience builds trust and strengthens brand reputation.
Use Cases of AI Knowledge Bases in Self-Service
AI-driven knowledge bases are transforming industries by enabling intelligent self-service. Here are a few industries specific use cases:
- Customer Support & IT Helpdesks: Handle user queries tickets and provide resolution seamlessly.
- E-Commerce & Retail: Intelligent self-service portals with AI can address real-time product recommendations, availability information, and issue resolutions.
- Banking: From account balances to payment-related issues, a self-service banking portal with an AI knowledge base can consistently handle them.
- Healthcare & Insurance: AI knowledge-base-powered patient portals allow patients and doctors to track appointment schedules, medical condition inquiries, and medications and reduce their dependency on a call center.
- Enterprise Knowledge Management: AI-powered enterprise portals allow employees to access policies, training materials, and workflows using AI-assisted search tools.
Future Trends in AI-Powered Self-Service Portals
AI transformation has already begun, and the future of Self-service is not restricted to just automation; it is intelligent automation. With Hyper-personalization, multi-modal and autonomous experiences, AI Self-service portals go beyond just answering the queries to understanding user emotions through sentiment analysis and executing tasks autonomously. Enhanced security, compliance and seamless AI-human collaboration will ensure enterprise-ready deployments. As AI evolves, self-service portals also evolve as intelligent and dynamic hubs that boosts efficiency and user satisfaction.
AI-driven knowledge bases are bridging the gap between your organization’s digital capabilities and growing user expectations. Now, the question is not whether you should implement AI or not, it is how soon you can start.
Are you ready to transform your self-service portal with an AI knowledge base? Let’s discuss how we can elevate your user experience.
