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Unlike traditional knowledge bases that struggle with outdated information, information silos, and irrelevant search results, an automated knowledge base centralizes information.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. 

The effectiveness of knowledge management improves employee engagement, boosts productivity, transforms the customer experience, and expedites business growth.

However, knowledge management efficacy depends on the knowledge management process 一 creation, refinement, storing, and distribution. If companies continue to have inflexible processes, they tend to lose to those with a robust knowledge management system.

Implementing Generative AI to automate knowledge management simplifies KM processes and helps you gain a competitive advantage.

1. Create new knowledge from the ground up

Generative AI takes the world by surprise with its intricate niche of generating new content using any kind of prompt of any length.

This is a useful feature for subject matter experts or authors responsible for creating new knowledge resources for multiple use cases in enterprises.

New content pieces can range from anything between documentation of application software, images, set of codes, songs, etc.

For example, a company that offers Salesforce Consultancy needs to have knowledge resources to help its employees to execute different operations such as integrations, implementation, development, etc. Authors can create intricate knowledge documents with specific image references for each function and enrich its internal database.

The new content created by Generative AI can be used for knowledge management in the Salesforce Environment.

Some more examples of real-world applications

• Product descriptions and features

• Creation of a user guide

• Troubleshooting documents

• Business email creation

• To-do list and presentation development

• Q&A conservation templates

• Monthly work reports

2. Make complicated knowledge into simple materials

Let’s say an organization uses a custom enterprise application for internal project management, which is not accessible outside of the organization. This is a scenario where employees cannot apply the steps common for off-the-shelf applications.

Here in this situation, organizations need to create personalized knowledge articles. If, in some cases, the application guide is lengthy or complicated, organizations can launch precise materials to encourage wider adoption. But time is the largest constraint, while manual maneuvering is another challenge.

Generative AI automates the manual process, saves time for knowledge management workers, and helps summarize the user guide into simple steps.

Sample response for output

Some more examples of real-world applications

• Summary of insurance claim procedures

• Summary of leave application procedures

• Summarizing customer reviews for audits

3. Repurpose existing content into different formats

Transforming is one great ability of Generative AI to augment the process of recreating or repurposing existing knowledge into a transformative state.

There are many instances where organizations need succinct and precise content to tell the product story, company policy, internal DevOps processes, new partnerships, mergers and acquisitions, etc.

For example, a company dedicates itself to CSR activities. As one of their CSR projects, they work to facilitate clean drinking water in interior villages where water is scarce. To successfully do the project, they delegate different activities for each department, and to convey roles and responsibilities, they have intricate knowledge resources. What if their people are reluctant to read through these comprehensive documents 一 active participation may decline.

The solution is to create a brief document, such as Excel Sheets, Slides, One-pagers, and any other succinct versions.

This is where the Transforming application works wonders by reducing manual efforts and recreating an existing version into desired formats. You can create a newer version of the document by providing the existing document into the Generative AI interface and a prompt “transform the document into Excel or Slide.” And you have a newer version of the document.

Below is an example of how an intricate document looks for a CSR project:

Sample format of the above document:

Some more examples of real-world applications

• Using product descriptions to create bulleted FAQs

• Transforming a small review into a customer story

4. Improve search experience for operational efficiency

The effectiveness of knowledge management lies in facilitating knowledge search instantly, which helps solve a problem and get work done.

Content tagging is one such use case of Generative AI to attribute to knowledge resources to differentiate them, create a unique theme for similar documents, and make them easily navigable, searchable, and usable for instant use without delay.

But the real problem arises when knowledge experts need to create tags manually. Other than repetitive and mundane experiences, the proneness of error also increases, impacting search accuracy and knowledge retrieval in real-time for internal employees.

Generative AI helps automate the process of content tagging by assigning new keywords to the documents based on their specificity of functions and purposes.

An LLM-powered interface identifies key concepts of knowledge resources, finds keywords in these documents, and attributes a specific keyword to the document. This is an automatic process, so all you need is upload the documents in the interface, find keywords, and assign tags to the documents.

By helping create content tagging, Generative AI makes it easy to build a faster and more meaningful knowledge base for internal employees and enables them to work faster.

Some more examples of real-world applications

• E-commerce product tagging

• Customer support document tagging

• User manual tagging

5. Enhance knowledge sharing among your people

Knowledge produces value when only it contains resourceful and enriched information. Outdated information can do more harm than good.

A change that occurs to the existing process of troubleshooting any IT issues or HR functionalities, or anything else needs to be updated in the knowledge base.

Whereas it is time-consuming to detect a specific change in the existing CRM, ERP, or ITSM application procedures, Generative AI identifies the pattern by continuously learning from user behavior and solutions provided, thereby by processing billions of data across its large language model or GPT, it establishes connections between the data points to surface the accurate information, even if the information is unavailable in the database.

Not only does this help subject matter experts to update their knowledge, but it enables every employee to work with real-time, relevant, and accurate information.

Another effective use case is Generative AI augments knowledge accessibility for employees by allowing them to classify information instantly.

Support assistants looking to ascertain what a customer’s message means can use Generative AI to suggest the right support.

For example, a customer has paid all of the installments of the BNLP scheme for a purchase. Due to a festivity offer, he is eligible for a cashback once installments are paid.

Here’s how Generative AI rapidly solves the confusion.

Below is a sample response of how Generative AI instantly surfaces the right category using text classification.

Some more examples of real-world applications

• Text extraction

• KM curation

• Brainstorming new ideas

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.

What are the types of knowledge bases?

There are broadly 2 types of knowledge bases:

• Internal knowledge base: This knowledge base is created by organizations to be only used by their employees and internal stakeholders. It consists of proprietary data like technical documents, financial reports and HR policies. 

• External knowledge base: This knowledge base is created for customers or the general public who want to know about a company’s products or services. It contains FAQs, product information, and user manuals generally available for the public to access. 

How can generative AI enhance knowledge management?

Here’s how Generative AI helps in knowledge management:

• Improve information retrieval

• Enhances knowledge sharing

• Simplifies complicated knowledge

• Personalizes knowledge content

• Reduces workload on knowledge workers

• Reduces organizational costs

How do AI knowledge bases work?

AI knowledge bases use a combination of machine learning, natural language processing, and large language models to collect, process, categorize, organize, and retrieve information. 

How to create an AI knowledge base?

• Define your goals and objectives.

• Research and choose knowledge base automation software. Look for AI capabilities, integrations, and ease of use

• Gather and preprocess required data

• Choose the right structure to design knowledge base

• Create and update content with high-quality

• Implement AI model 

• Train and test your AI systems

• Monitor constantly to improve performance