5 Interesting Ways AI Can Transform Knowledge Management Processes

Temps de lecture: 6 minutes

A Zippia Research study suggests that 86% of employees in leadership roles attribute workplace failures to a lack of collaboration. The report also revealed that one out of two employees believe their jobs rely on collaboration. As organizations embrace digitalization to compete and stay ahead of the curve, it is a good time to explore the different ways they can improve team collaboration.

Knowledge management plays a key role in enabling better team collaboration. It helps centralize information in a way that can be extracted and repurposed based on organizational requirements – be it with customer support, human resources management, or sales. And with Artificial Intelligence (AI), it is now possible to automate a lot of these knowledge management processes.

This article explores the role of artificial intelligence (AI) in knowledge management and how it can help organizations boost productivity. 

Before we begin, let us get familiar with both concepts. 

What is Knowledge Management?

Knowledge management is a key process that involves creating, collating, and sharing precious company knowledge to improve internal collaboration and efficiency. 

The idea is to provide the right information to the relevant stakeholders in the correct format at the right time, and without hassles. 

Understanding Artificial Intelligence

The term artificial intelligence (AI) was coined by John McCarthy in 1956, a Dartmouth College professor. Since then, AI has transitioned from many definitions, primarily linking it as a technology that enables machines to leverage data and concepts to solve problems like humans. 

Simply put, AI is a technology that makes machines think like humans by processing large volumes of data within seconds. 

Exploring AI Knowledge Management

AI knowledge management is the integration of AI and related technologies into knowledge management processes to promote informed decision-making and boost productivity. 

Organizations worldwide have realized the need for the confluence of AI and knowledge management to ensure their employees have access to important information without roadblocks. AI knowledge management is tipped to play a key role in unlocking the doors to efficient knowledge processes and improved internal collaboration. 

How Can AI-Powered Knowledge Management Help Organizations?

More than 40% of respondents in McKinsey’s latest Global Survey said that they will increase investment in AI due to the giant strides taken by generative AI over the past few years. While most organizations have started harnessing the benefits of AI to introduce meaningful changes, many companies are yet to explore the potential of AI in knowledge management. 

So, how can AI help organizations improve knowledge management? Let’s take a look. 

1. Keeping Content Up to Date

Ensuring that organizational content is up to date is one of the biggest challenges in knowledge management. Outdated documents and data can result in poor decision-making and contribute to costly errors, thereby hindering productivity and affecting the bottom line. This is where AI can solve the problem by helping organizations track new changes and developments related to a certain topic. 

AI-powered tools can scavenge the depths of the internet to update their knowledge management systems. They can, for instance, study new blog posts to understand new product features to update a product onboarding document. They could also repurpose information from such onboarding documents into transcripts that can be uploaded to the call center software for the support teams to use. 

Additionally, the advent of AI-powered chatbots can help support teams answer frequently asked questions (FAQs) by analyzing the data in the existing or updated knowledge management systems. That’s what Talkspirit’s FAQ chatbot is all about, for example.

2. Collecting and Organizing Data From Different Sources

Another noteworthy roadblock in knowledge management is interlinking or connecting and organizing data collected from multiple sources. Organizations typically collect data from several sources; including their website, social media channels, web pages, etc. However, organizing this data can be challenging and time-consuming. So this is where AI can bring a lot of added value.

Natural Language Processing (NLP) tools powering AI systems are trained to understand complex language structures to interpret it in a way that machines can understand. This information can then be fed into Machine Learning (ML) algorithms that are trained to segregate data based on various parameters like consumer behavior, and intent. This makes it easy to connect and present accurate data to users.

For example, an AI system could study the words used in a customer conversation in real-time to understand the context of the call, and pull out the right documents, and resolutions in order for the agent to be able to provide them to the customer. 

Additionally, generative AI can study existing literature, and repurpose them into documents for specific purposes. For example, an AI tool can help HR managers prepare a culture deck based on information available in the company handbook. Or, the tool could consolidate various sales decks and use them to build assets for the sales team LMS. 

3. Minimizing Support Costs

One of the glaring benefits of AI in knowledge management is the direct impact on support costs. As mentioned before, AI-powered chatbots can provide instant responses to resolve customer queries and issues including password reset, product information, etc. 

This minimizes the dependency on human support, thus saving significant personnel costs. Indeed, these chatbots utilize machine learning to learn from customer interactions and provide apt responses to customer queries accordingly. 

They can empower support teams with useful information and scripts within seconds to tackle complex issues, making them more productive.

4. Better Information Search

Search engine technology has progressed swiftly over the past couple of decades, enabling users to find useful and relevant information in no time. Even then, finding and pulling out the right information from large datasets at an organizational level can be challenging. 

Organizations can leverage AI to improve their search capabilities, making it easy for internal stakeholders and users to find exactly what they are looking for with greater accuracy. It can also help users (internal and external) search for something in their preferred language even if the content they are looking for is in a different language, eliminating language barriers.

Users could also transcribe conversations from video conferences and online meetings so that information disseminated through these calls can be part of information search and knowledge management.

Additionally, AI-driven knowledge management tracks user behavioral patterns to offer tailored search results based on historical data and previous searches. It also suggests alternative search items to ensure users get relevant and comprehensive information. 

5. Tailored Employee Training

AI-driven knowledge management processes also ensure that all employees within an organization are on the same page. The infusion of AI in knowledge management is an excellent way to train new employees, curate their learning paths, and recommend relevant skill development programs.

For instance, AI-powered LMS tools could make employee onboarding more interactive with programatically-derived popup quizzes and case studies. It could also offer personalized learning paths based on the learners’ performance and provide exhaustive guidance to Human Resources on what each newly-onboarded employee excels in, and what they lack.   

The idea here is to replace the conventional “one-size-fits-all” training approach with personalized training to improve employee engagement and retention. This way, organizations can build a skilled workforce that is aligned with their organizational and personal goals. 

Improving Knowledge Management with AI

There is a dazzling array of AI-driven tools and applications that develop and improve various aspects of knowledge management in an organization. It is clear by now that knowledge management is transforming how organizations store, access, and share data. AI-based knowledge management tools like Talkspirit have cleared the path to developing collective intelligence and enhancing knowledge management within organizations. 

Talkspirit empowers organizations to capture and share both “cold” and “hot” data, and create a repository that is accessible to relevant employees. The news feed allows people to share posts on company news and project updates, whereas the library is used to capitalize internal knowledge (including FAQs, glossaries, processes and best practices), and define the target audience for each asset. Moreover, Talkspirit has an organizational chart that can help you document people’s roles and responsibilities. And if you want to save time, guess what? You can automatically generate content for those roles using an AI assistant 😉.

PS: Want to know more about knowledge management and AI features on Talkspirit? Schedule a demo with our team!

Wrap Up

It is safe to say that AI will change the way knowledge management processes are carried out today. The confluence of knowledge management and AI will reshape data sharing, information search, cross-functional collaboration, and other aspects of workforce development. 

While the adoption of AI for knowledge management is expected to gain traction, organizations must tread with caution and address concerns related to data privacy and biased AI algorithms. 

The future of knowledge management is here and it is high time organizations harness the benefits of AI to improve productivity, boost efficiency, and build a collaborative culture. 

AI-powered digital tools can make a huge difference when it comes to improving knowledge management. But the question is: which ones should you leverage? Download our latest white paper to discover the platforms used by next-generation companies:

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