As organizations become more reliant on business intelligence, knowledge bases have become an integral part of their data infrastructures. With a centralized location for organizational data, businesses can effectively get information on demand, improving efficiency and productivity. However, even the most robust knowledge base has its limitations.
For starters, knowledge bases are difficult to maintain. They also require extensive employee training and a dedicated management team to keep them running. Ultimately, this leads to several bottlenecks that, if not managed, could make the knowledge base less effective.
However, by integrating artificial intelligence into the knowledge base, organizations can overcome these challenges, paving the way for a more interactive, easy-to-use system that facilitates fast and accurate data sourcing and retrieval.
In this guide, we dive into the intricacies of AI-powered knowledge bases, exploring everything from what they are, to their advantages, the various types, and how to build and implement an AI knowledge base into your organizational data infrastructure.
What is an AI-powered knowledge base?
A conventional knowledge base is a centralized repository of information that structures and organizes information, making it easy to manage and retrieve it. An AI-powered knowledge base takes these capabilities up a notch by incorporating machine learning (ML) and natural language processing (NLP) technologies, enabling the system to evolve as it learns from user interactions and data input.
The fact that it leverages natural language processing capabilities also makes it more dynamic and interactive, ultimately making it easier to use – even for relatively inexperienced staff. Machine learning capabilities also improve the accuracy and relevance of search results and enable the system to meet the specific needs of users.
How does an AI-powered knowledge base work?
An AI-powered knowledge base is basically an enhanced version of a conventional knowledge base. While a conventional knowledge base relies on manual efforts to create, update, and organize content, an AI-powered knowledge base harnesses the power of machine learning and natural language processing (NLP) technologies to automate content and knowledge management.
NLP is a subset of artificial intelligence that leverages machine learning to allow computers to understand, interpret, and generate human language. Essentially, the system leverages NLP capabilities to analyze and comprehend the meaning and context behind text and speech input.
Machine learning, on the other hand, is a field of study in artificial intelligence concerned with the development of computer systems capable of learning and adapting to user needs without following explicit instructions. ML achieves this by leveraging algorithms and statistical models to analyze and draw inferences from input data.
When combined, the ML component analyzes and learns from input data, enabling it to continuously improve the performance of the knowledge base. ML can also help refine results, enhance the understanding of user queries, and provide more accurate and contextually relevant recommendations.
Similarly, the NLP component allows users to interact with the knowledge base using natural language. This makes it easier to interact with the system, making data retrieval faster and more efficient.
Why is an AI-powered knowledge base necessary?
A knowledge base forms the foundational framework for your knowledge management practices. Having a robust system allows you to create, manage, and share knowledge across the organization.
These capabilities are particularly important in customer service, where organizations are under constant pressure to improve customer satisfaction to increase their competitiveness [1]. In today’s business environment, customers are demanding more from the businesses they choose to work with. Recent studies show that 68% of customers appreciate businesses with established channels offering personalized and efficient customer support [2].
Another study by American Express found that 60% of consumers in the US would prefer an automated self-service platform for customer service tasks [3]. Implementing machine learning and NLP capabilities that allow integrations with AI-powered chatbots has, therefore, become integral to enhancing customer service through self-service support.
While conventional knowledge bases facilitate information retrieval, it’s quite difficult to integrate them into chatbot applications. However, when paired with AI-powered technologies like generative AI, these systems can allow businesses to create better interactive systems that have the potential to improve customer service tremendously.
An AI knowledge base is also constantly evolving and figuring out new ways to fulfill business needs. This means that any business still relying on a conventional knowledge base is losing out on critical business advantages.
Advantages of using an AI-powered knowledge base
An AI knowledge base isn’t just an automated repository. It has practical, transformative, and long-lasting benefits that could greatly improve customer service and business operation efforts. These include:
1. Improved customer service
Implementing an AI-driven knowledge base into your customer service platform allows customers to find solutions independently. The system achieves this through intuitive search capabilities and AI-driven recommendations.
This way, customers can access relevant information, including FAQs, tutorials, and troubleshooting guides, and ask questions directly by interacting with integrated chatbots.
Ultimately, an AI knowledge base frees up your customer service team’s time so they can focus on more crucial aspects of their job, like helping customers who require more attention, understanding, and sensitivity. It also makes the process faster and more effective, resulting in greater customer satisfaction.
2. Increased efficiency
Human customer service staff are limited to serving one customer at a time, which limits the number of customer service requests the organization can handle. Implementing an AI knowledge base can enable the business to handle large volumes of queries simultaneously.
When properly utilized, this can facilitate a dramatic improvement in operational efficiency, ultimately leading to increased productivity.
3. Consistent Customer Service Experiences
To improve overall customer satisfaction, businesses must be able to provide a uniform, reliable, and high-quality customer service experience at every touchpoint [4]. An AI-driven knowledge base can help deliver precise content in a cohesive tone across all help center articles and FAQ sections. This helps ensure a high degree of accuracy and consistency in all the information shared with customers.
Maintaining a high level of consistency builds trust, fosters positive customer experiences, and strengthens the brand’s reputation. Ultimately, this leads to higher customer retention and brand loyalty.
4. Simplified knowledge discovery
AI simplifies the knowledge discovery process tremendously. Unlike conventional knowledge base systems, where you have to rely on a predetermined set of keywords to look for information, an AI knowledge base allows you to accurately search what you’re looking for with a simple description.
In customer service applications, this capability can come in handy as it allows you to find relevant answers to customer queries seamlessly. It also enables you to discover tangentially related content easily, improving your ability to learn from the information contained in the organizational data infrastructure.
5. Quick response times
An AI knowledge base software is significantly faster at discovering information than a conventional knowledge base. Its ability to integrate with AI-powered chatbots and suggest answers based on the user’s specific needs can speed up response time significantly.
This way, customers don’t have to wait for extended periods to get assistance. Consequently, this leads to greater customer satisfaction. Customers are also less likely to seek better alternatives from your competitors, leading to greater customer retention and brand loyalty.
6. Reduced operational costs
Implementing an AI-driven knowledge base can reduce organizational operation expenses tremendously. For instance, improved self-service capabilities can reduce reliance on support tickets, minimizing the need for extensive customer support teams. The systems are also able to handle more queries and boost overall staff efficiency.
7. Accelerated agent onboarding and training
Organizations often lose a lot of time and effort training new staff. One of the biggest reasons behind this lag in onboarding new employees is the lack of relevant resources that new employees can learn from. An AI knowledge base can allow recruitment agents to find and access documents, training guides, and important customer information by providing a centralized hub.
AI algorithms can also suggest relevant materials to support agents, help in writing content, and recommend articles within tickets. Ultimately, this helps streamline new agent training and onboarding, thus reducing costs and improving organizational efficiency.
Different types of AI-driven knowledge bases
AI knowledge bases come in various types, each designed to perform specific tasks. They also have unique capabilities, making them suitable for specific applications.
Some of the most common types of AI knowledge bases include:
Machine Learning-enhanced knowledge base
Machine learning-enhanced knowledge bases utilize machine learning algorithms to learn and evolve from user interactions. For instance, if a customer frequently asks about a specific product or feature, such as ‘ storage capacity’ on a specific PC model post-update, the AI knowledge base identifies this as a trend.
Consequently, the system prioritizes and enriches the AI knowledge base with detailed information about memory optimization and other known issues specific to that particular PC model and update. Ultimately, this ensures that customer service agents always have relevant information at hand, streamlining the customer service experience.
Natural Language Processing (NLP) knowledge base
NLP knowledge bases are specially designed to interpret and generate human language. When used in customer service applications, these systems can understand and respond to user queries conversationally through chatbots.
Some of the most efficient NLP knowledge bases can accurately decipher customer queries regardless of the language, style, or syntax used. This makes them especially effective in handling a diverse range of query types and customer demographics.
Here is the typical working mechanism of an NLP knowledge base:
- Entity recognition: The system first identifies specific entities like names, products, and places, enabling it to better understand the customer query’s concept.
- Entity linking: Based on the query’s context, the system identifies relevant entities and connects them to corresponding information within the knowledge base.
- Natural Language Understanding (NLU): The NLU interprets the meaning and intent of customer queries, enabling the system to put them into context.
- Natural Language Generation (NLG): The NLG component automatically generates coherent and contextually relevant responses to customer queries using NLP.
Semantic knowledge base
As the name suggests, semantic knowledge bases excel in understanding the semantics of an input. These systems utilize a semantic search approach that applies user intent, conceptual meaning, and context to link the user’s query to the corresponding information, allowing faster and more efficient knowledge retrieval.
These capabilities are crucial in customer service applications as they allow the AI knowledge base to interpret complex or vague customer requests accurately.
Predictive knowledge base
Predictive knowledge bases work by constantly analyzing and responding to problems. When the system identifies an issue, it triggers the knowledge base software to adjust its content based on the users’ needs. This process typically involves prioritizing content based on anticipated issues or creating new content altogether.
This way, the information can be discriminated by human customer service agents and chatbots, enabling them to offer pre-emptive solutions to customers. These capabilities are especially beneficial in identifying and addressing problems before they become a major concern, facilitating a more proactive customer service approach.
Guidelines for building an AI-powered knowledge base
Building an AI knowledge base requires a systemic approach geared towards providing the system with relevant data and training it to make it more effective and accurate.
Here is a step-by-step guide to building an AI-driven knowledge base:
Step 1: Define Your Objective
What is the scope and purpose of your knowledge base? By answering this question, you’ll be better able to decide on the most suitable type of knowledge-base software to serve your customers.
Step 2: Gather and Organize Data
The effectiveness of any knowledge base comes down to the quality of data utilized. By using informative and well-structured content, you can effectively enable the knowledge base to provide more accurate results.
Some of the most relevant data sources to use include chat logs, call logs, emails, and other customer interactions. While you’re at it, you should also organize the data into a structured format to make it more palatable to the AI algorithms.
Step 3: Choose an Appropriate Model
NLP and ML technologies are some of the most appropriate technologies for powering knowledge bases with AI. Depending on the complexity of your project, you can use a rule-based system or a machine-learning model for more complex applications. You can also opt for a framework or platform with AI capabilities.
Step 4: Train the Model
With the structured data collected in Step 2, you can train the system and adjust its parameters to improve accuracy. Depending on the nature of your knowledge base, training processes may include training the model on text data in NLP models or using historical data in ML models to enable them to predict future outcomes.
You should also refine and retrain the model constantly as new data becomes available. This way, you’ll ensure the model provides accurate, up-to-date information.
Step 5: Conduct Testing
You need to test the AI knowledge base to validate its accuracy and performance. You can use a variety of tests like common queries and edge tests to check the reliability of the system. You should also conduct A\B testing to compare the system’s performance against other systems and human agents.
Step 6: Continuous Monitoring
and identify potential areas for improvement. Having a reliable feedback loop allows you to understand how well the system is meeting user needs and areas where it may need improvements.
In this endeavor, you can use various tools to track response times, usage patterns, and accuracy rates.
Final thoughts
As customers demand faster, more effective customer service, organizations around the world have had to come up with more reliable systems to offer self-service capabilities and improve the effectiveness of human customer service agents.
In that regard, AI-driven knowledge bases have proven to be an effective solution, owing to their AI-driven capabilities. By integrating AI into your knowledge base, you can increase its speed and accuracy, and enjoy other value-adding benefits like predictive analytics to help identify and react to market demands.
[1] Fastercapital.com, How Exceptional Custome Service Can Give You a Competitive Advantage, https://fastercapital.com/content/How-Exceptional-Customer-Service-Can-Give-You-a-Competitive- Advantage.html#:~:text=Exceptional%20customer%20service%20is%20a%20powerful%20tool%20that%20can%20 differentiate,competitive%20advantage%20in%20the%20market. , Accessed on July 24, 2024
[2] Hubspot.net, Customer Service Expectations, https://cdn2.hubspot.net/hubfs/2771217/Content/2018%20Customer%20Service%20Expectations%20Gladly.pdf, Accessed on July 24, 2024
[3] Templatelab.com, American Express Study, https://templatelab.com/american-express-study/, Accessed on July 24, 2024
[4] Fullview.io, Consistency in Customer Service, https://www.fullview.io/blog/consistency-in-customer- service#:~:text=What%20is%20consistency%20in%20customer,its%20customers%20at%20every%20touchpoint., Accessed on July 24, 2024
