How to Use Generative AI to Automate Knowledge Management Tasks

With so much to do unprecedentedly in content generation in an innovative way, Generative AI allows business leaders to harness this unique phenomenon for knowledge management.

Using the right technique of prompt engineering, users are more capable of asking the right question to Generative AI to surface the most relevant and meaningful resource.

Opposed to what could be achieved from Generative AI in augmenting knowledge management, enterprises have information in silos, making accessing information a tough stride and impacting overall employee productivity.

A McKinsey report claimed that the average number of workers waste as much as 28% of their time in a week managing emails, while 20% is spent on finding the right help.

With every passing day, organizations realize the need to capture, analyze, and disseminate proprietary data seamlessly. But, the effectiveness of knowledge management would not scale for leaders continuing to rely on traditional knowledge management techniques.

In addition to the conventional KM approach, knowledge-sharing inefficiency costs companies between $2.7 million and $265 million annually.

Central to organizational effectiveness that drives employee engagement and resilience, which also expands to customer experience, knowledge management must create value through convenient and intuitive accessibility.

This is where Generative AI intervenes.

As Nicola Morini Bianzino said in an interview with Harvard Business Review, Generative AI gives users the ability to quickly retrieve, contextualize, and easily interpret enterprise knowledge, a powerful business application of LLMs. A natural language interface combined with a powerful AI algorithm will help humans in coming up more quickly with a larger number of ideas and solutions that they subsequently can experiment with to eventually reveal more and better creative output.

In essence, Generative AI makes knowledge management more flexible and scalable to improve employee productivity and bottom line cost savings for businesses.

Our article is a rundown on how Generative AI can be a valuable industry tool to automate knowledge management processes. Read along.

Knowledge base automation means using AI tools such as generative AI and machine learning to automate the workflows for processes to create, update, and manage a repository of information such as FAQs, troubleshooting guides, and reports.

Unlike traditional knowledge bases that struggle with outdated information, information silos, and irrelevant search results, an automated knowledge base centralizes information. It uses intelligent search algorithms to deliver accurate, personalized information to user queries and automatically updates KB content.

The traditional knowledge bases have served the purpose of storing information for a long time but have been inefficient in managing and retrieving information. But today, AI has completely changed how companies collect, organize and use information. 

Let’s understand the key differences between traditional knowledge bases and AI-powered knowledge bases: 

Search approach

Traditional knowledge bases are rigid on keyword-specific searches with limited flexibility. So, employees trying to find information from legacy KB must type in the exact terms to get relevant results. If the phrasing of the search term slightly deviates, the system may not understand the query or fetch irrelevant results. This causes frustration among employees and forces them to resort to inefficient ways of accessing knowledge, like asking colleagues. 

AI-powered knowledge base utilizes large language models to understand and interpret queries as humans write or speak. This makes it easy for employees to search for information without struggling to match keywords. They can just type in the query as if they’re talking to a colleague, for example, “How do I reset my email password?” and immediately get relevant answers for the query. 

Content creation

Creating new knowledge in the traditional KB means that knowledge workers and subject matter experts have to spend countless hours creating content from scratch and manually reviewing it for accuracy and correcting mistakes. The process doesn’t stop here. The new knowledge has to be sent to stakeholders and relevant team members for approval and feedback. This process is slow, inefficient, and involves a lot of manual labor.  AI knowledge base has the ability to generate new content by leveraging generative AI. It can analyze your enterprise’s existing information and verify information across multiple data points to create new knowledge with reliability.

Plus, with AI, enterprises can generate content in multiple formats like bar graphs, pie charts, reports, images, and more. This helps companies effortlessly create easy-to-digest content for employees.

Knowledge base maintenance

Legacy knowledge management systems require dedicated workers to update and manage KB, which means enterprises must bear additional costs for hiring knowledge workers as their data grows. This isn’t a scalable approach. 

Another factor is that the traditional approach follows a periodic cycle for KB updates, so if subject matter experts are unavailable in that timeframe, employees will have to work with outdated or erroneous information until the next review cycle. 

This is where AI-powered KB makes a huge difference by automating the process. AI models can continuously learn from user interactions and new data and update the KB with relevant information in real time. Knowledge managers can quickly review suggested updates, approve them, or give feedback. The AI actively implements feedback and continuously monitors the KB for new updates. 

Knowledge gap identification

In the traditional approach, the knowledge workers are overwhelmed by manually creating new content and updating the knowledge base. More than half of their time, energy, and resources are spent on these tasks, which leaves them with little to no bandwidth to identify gaps in the KB. 

The knowledge gap is identified only after several user complaints or frequently unanswered queries.

AI-powered KB, on the other hand, constantly learns from user interactions. It analyzes patterns in the search, categorizes search types and recurring queries to identify the gap. Adding on to this, AI proactively suggests relevant content to bridge the knowledge gaps, enabling organizations to manage knowledge effectively. 

User experience

In the traditional setting, employees get only static and generic answers to their queries. The interaction is only transactional, and the system rarely takes the intent of the query into consideration.

The AI knowledge base analyzes the user roles, behaviors, past searches, recent interactions, and preferences to deliver personalized experiences for every employee.

For example, if an employee from HR is working on updating the employee benefits policy, AI will suggest relevant content like the previous year's policy or employee survey statistics related to benefits policy. 

Scalability

In this age of digital revolution, almost every business is dealing with the challenge of exploding data. Traditional knowledge management systems are inherently flawed at managing large volumes of data due to manual processes, static structures, and limited adaptability. These systems cannot keep up with the influx of search queries.

In contrast, the AI knowledge base can process and learn from massive datasets. So, as your organization grows, AI can synthesize and process growing information to ensure your employees are always working with fresh information.

Plus, unlike traditional KB, AI-powered KB can efficiently handle an influx of employee queries.

A survey shows 83% of respondents agree they are more likely to bring digitally focused solutions. Undoubtedly, Generative AI could be part of that trend.

Knowledge management is one area of specialization that can augment knowledge search, information dissemination, and analysis using Generative AI capabilities.

• Customer Perspective

Let’s say a customer has made a new investment, and it is essential to provide that customer with all the necessary documents that validate his investment in the product. With the information in silos, it would take several days to modify and personalize existing documents, such as user guides and other essential communications, including customer onboarding or support content based on the product type.

Till the papers arrive, the customer gets impatient and apprehensive about his investment decision. The user experience takes a toll.

However, retrieving and repurposing similar customer onboarding documents or user guides is easy as you leverage Generative AI. You can come up with a new document or content with a workaround. Also, you can create personalized newsletters to announce new product features, benefits, or new product offerings to facilitate new business opportunities.

• Employee Perspective

In a similar way, Generative AI can create an exceptional employee experience. Usually, what happens with the traditional employee onboarding approach is that organizations create training and upskill documents manually and store them in any of the applications or their personal drive, whereas organizational policy documents rest somewhere else, such as in the HRMS system. So, knowledge bases are not centralized.

This is one specific challenge. Another pain point is users do not know who to contact for the right information.

When using Generative AI, it is easier to integrate any large corpus of databases or knowledge sources into the large language model and provide a faster way for your employees to search for critical information at their fingertips.

Say, a new developer joins your organization. Product knowledge resources are key to helping new employees learn and use their knowledge better while fully engaged with product development or user experience improvement.

Even if the learning and development resources are ready, they are often not readily available during onboarding training, which means a poor training experience, prolonged training, and high costs on the company’s bottom line.

With a Generative AI interface integrated with your repository, users can apply effective search commands to retrieve essential documents seamlessly. Anything that is upgraded or updated later can also be available via direct links on the GenAI-powered interfaces.

As a result, new hire training designed with the power of LLM properties happens to be pleasant and drives successful adoption and employee productivity.

AI-powered knowledge bases accommodate diverse content types in a unified system. This helps enterprises organize, update, and access different content types to improve information search, enhance employee support, and streamline overall operations. 

AI-powered knowledge bases can include content such as: 

Structured content

Structured content refers to data stored in a standardized format. This includes FAQs, articles, troubleshooting guides, and reports. 

The content is stored in an organized format with labels, numbers, and categories, which makes it easier for AI to search, retrieve, and display relevant information. 

Unstructured content

Unstructured content consists of information that doesn’t have a pre-defined format or structure. This includes data stored in email threads, images, videos, and audio. 

AI applies techniques like NLP and ML to extract actionable insights from unstructured data. This is extremely useful for leaders in understanding the critical nuances of a particular matter, improving decision-making. 

AI-generated content

AI-generated content refers to the information produced by AI using massive datasets and learning from your company database. It continuously updates, refines, and generates content based on user interactions and feedback. Plus, AI analyzes your existing data for gaps and bridges them. 

For example, if an employee asks a question on a topic that isn’t directly covered in the existing KB, AI will combine the existing information to produce an answer relevant to the query. 

Automation of KM using Generative AI gives organizations the best way to optimize organizational operations and employee efficiency.

The benefits include,

• Enhanced productivity

Generative AI reduces the time for knowledge creation and enhances knowledge discovery through real-time content tagging or text classification, thus allowing employees to optimize time and unleash effort to accomplish critical tasks.

• Organizational cost savings

KM automation reduces the workload of subject matter experts, support personnel, and internal employees through the augmentation of self-serve capabilities. As a result, bottom-line expenses on critical resources, such as employee training and development or support operations, decline, which add to revenue growth.

• Data-driven decision-making

LLM-powered workflows simplify data visualization, using text classification and extraction, enabling organizations to deliver the right support in real time without much friction.

• Enhanced user experience

Generative AI ensures knowledge resources contain quality information that helps solve problems with minimal impact on operational efficiency. On top of it, automated incident detection and mitigation steps allow for ahead-of-time incident mitigation.

Facilitating effortless knowledge search to automate problem-solving, Workativ makes it happen with its conversational AI builder.

Workativ enables you to leverage the properties of embedded large language models or Generative AI to upload enterprise knowledge data from multiple data repositories and build your own GenAI-powered KM solution.

The rapid development of KM solutions allows you to expand enterprise use cases for HR and IT support and offer the right help to accelerate response and resolution.

With your knowledge articles offering help to solve common IT issues, many critical IT issues, such as password reset,application provisioning, etc, are fast to resolve.

On the HR support side, onboarding new hires, new employee contracts, and other HR requests, such as leave management and salary management, can be automated using Generative AI.

To learn more about implementing the effectiveness of knowledge management with a conversational AI chatbot, get in touch with Workativ sales experts for a demo.

Knowledge management facilitates organizational resilience. Generative AI has significant potential to transform the existing way of building and implementing knowledge management in an automated way.

Multiple use cases of Generative AI that start from content generation to content tagging rewriting to ideating, classification, and organization help make developing knowledge management seamless and fast.

Organizations can raise red flags for ethical concerns around Gen AI.

But, until you use it to adapt to its environment, there is less scope for you to harness the immense possibilities of Generative AI for knowledge management automation.

Besides, learning to know is the best weapon to familiarize yourself with the trends and, of course, the best ways to reduce the implications of a nascent technology like GenAI.

If you are interested in exploring the best side of knowledge management for your organization, try out Workativ virtual assistant for unique HR and IT support. Schedule a demo today to build your knowledge management and tap into the massive potential of Generative AI.