A McKinsey report reveals employees spend 1.8 hours of their workday searching for information needed to do their jobs.

Looking beyond the statistical number, that’s 9.8 hours of lost productivity every week. The reason?

Inefficiency in knowledge management.

As a business leader, it is important to realize that having access to a pool of information is only one part of the equation. You must ensure that the data is accessible to your employees right where they need it.

So, how do you overcome the challenge of inefficient knowledge management?

This is where AI comes in to save you.

In this blog, we discuss the challenges in traditional knowledge management systems and how AI addresses them, helping you use your company’s information to its full potential.

Before we dive in, let’s get down to brass tacks.

Knowledge management refers to capturing, organizing, sharing, and managing an organization's proprietary data to help enterprise employees find answers to their questions and increase productivity.

Without effective KM, you fail to retain valuable knowledge when employees leave, your teams struggle to collaborate, and employees waste time searching for information. All this leads to delays, confusion, and a negative impact on your bottom line.

We can broadly categorize knowledge into three types:

• Explicit : This is the knowledge that is easily documented and shared. It includes tangible resources like whitepapers, case studies, guides, and reports.

• Implicit : Knowledge that can’t be documented but can be learned by observation and mentoring is known as implicit knowledge. For example, learning to use a new tool by observing teammates and following best practices to perform a task.

• Tacit : This kind of knowledge comes from personal experiences and intuition. It is deeply ingrained in an individual and is challenging to transfer. Leadership skills, for example, can only be developed through experiences of failure and success.

Effective KM allows employees to quickly find valuable information, reduce inefficiencies, and connect with the right people at the right time. Let's see how:

• Facilitates better decision-making : When your employees have crucial information that is easily accessible at the right time and has a simplified view, they make better decisions.

• Increases productivity : Well-maintained KM helps employees focus on important tasks rather than waste time searching for resources or duplicating efforts.

• Faster problem-solving : When an issue arises, a robust KM system directs your employees to a documented solution or an expert who can guide them in solving the problem.

Legacy KM systems might have served organizations well in the past, but as businesses generate larger volumes of data, these legacy systems have shown their imitations. They struggle to keep up with the complexities and demands of modern-day business operations.

Here are the key challenges that businesses face with Traditional KM systems:

1. Information in silos

Information in silos refers to when the company’s valuable data is kept isolated and inaccessible to the rest of the people in the organization.

This situation happens when:

• Different teams in your organization store information in their preferred channels

• Your employees don’t document information religiously because they are unaware of how their knowledge will benefit others.

• Leaders fail to foster a knowledge-sharing culture.

When data is fragmented, it leads to inefficiencies, such as employees spending excessive time searching for information or duplicating efforts. In a survey, 68% of employees stated their work is negatively impacted because they don’t have visibility into cross-functional projects.

2. Manual categorization of information

Traditional knowledge management systems depend heavily on manual input from your employees to organize and tag data. This process is inefficient, not only due to its time-consuming nature but also because it:

• Lacks uniformity in classification : In an Accenture survey, 42% of employees said they accidentally use incorrect information at least once a week. This is because different teams or employees prefer their own methods for classifying information.

• Increases chances of human error : While manually categorizing data, your employees will make errors that could include mislabeling data, forgetting to categorize information, or placing it in the wrong folder. This untagged or poorly tagged data will get buried and become invisible to those who could benefit from it, defeating the whole purpose of KM systems.

3. Difficulty in managing unstructured data

From the massive data businesses generate, as much as 90% is unstructured data, including emails, videos, audio files, and images.

Traditional KM systems are designed to efficiently handle only structured data, such as customer names or invoice numbers. They struggle to store, categorize, and retrieve unstructured data.

This limitation of traditional KM methods has left valuable knowledge untapped for years, leading to miscommunication, delays, poor decision-making, and lost revenue.

4. Lack of personalization

Each team in your organization would require different kinds of information. Your HR team would need access to policy documents, while an IT employee would need technical manuals and system logs.

This is where traditional KM systems fail to account for the user’s role, context, or specific needs and bombard users with generic information. HBR research shows that 47% of employees receive information irrelevant to their job responsibilities.

The result? Your HR teams will struggle to find relevant policies among IT documents, or IT employees will have to search HR-related documents for technical manuals.

This disconnect forces your employees to make an extra effort to find the correct information, resorting to inefficient methods like repeatedly asking colleagues for information, impacting overall productivity.

5. Information overload

Today, employees are struggling to keep up with the massive amount of data organizations generate daily in the form of emails, reports, meeting summaries, notifications, and more.

A Gartner survey reveals that 27% of employees and 38% of managers feel burdened by the volume of information shared within the organization.

With so much information to process, it’s easy for employees to lose track of what’s relevant, miss critical insights, and experience excessive burnout, all of which impact their effectiveness at work.

This challenge extends to senior management as well, impairing their decision-making capabilities. Research from Harvard Business Review shows that 40% of leaders and 30% of managers feel overwhelmed by excessive information. Those facing high information load are 7.4 times more likely to regret their decisions.

So far, we’ve explored the limitations of traditional KM systems. Let’s see how AI tackles these challenges and reshapes how organizations access, manage, and use their knowledge for operational efficiency.

Here are eight key ways AI is transforming KM for the better:

1. Accelerating search and information retrieval

A recent Deloitte study shows that 71% of employees who could easily access information rated its value above average compared to those who struggled to retrieve it. This statistic highlights that the easier it is for your employees to find information, the more valuable it becomes.

AI improves your employees’ search experience by using Natural Language Processing (NLP) and Machine learning (ML).

NLP enables the interpretation of queries phrased in casual and colloquial language, and ML learns from these patterns to improve search results in the future.

Suppose an employee searches, “Why is my printer not working?” AI will understand this query is related to printer connectivity issues and surface relevant information required to resolve it. This eliminates the time spent on keyword matching.

2. Automating content creation

AI has the ability to analyze large pools of data, so you can now utilize this power in your KM to create reports, extract insights, summarize meeting notes, or prepare FAQ documents.

Suppose you’re a sales head and want insight into your monthly sales performance. AI will extract relevant data, such as total revenue, top-performing products, and region-specific performance, from the company’s CRM and sales enablement platform to summarize critical information in an easy-to-read format.

You can even use AI to produce this information in different formats, like texts, graphs, pie charts, and comparisons, without manual effort.

3. Real-time knowledge base updates

The value of KM relies on its accuracy and up-to-date content. Even a minor change in documents or troubleshooting guide must be updated immediately. Otherwise, it can cause delays and miscommunication, discouraging employees from using KB as a reliable source.

This is where AI solves the problem by automating the process. It continuously learns and monitors internal data streams like customer feedback and employee communication, as well as external sources like industry news.

When the AI detects any relevant update, like new product information or changes in compliance regulations, it automatically updates the KB. This ensures that your employees always work with the most accurate information.

4. Effortless content tagging and classification

Artificial intelligence transforms the tedious and error-prone process of labeling and organizing your company data into an easily searchable format.

AI algorithms scan your entire database, including texts, audio, videos, and documents, to consistently assign relevant tags for each piece of information, surpassing human capabilities in speed and scale.

What sets AI apart in this context is its ability to go beyond simply categorizing information. It analyzes your data in-depth and intelligently matches it with multiple relevant tags.

For example, for a single document tagged as employee benefits, AI can assign related tags like HR policies, remote work benefits, and healthcare plans. This approach allows employees to find information faster, regardless of the search term.

5. Personalizing knowledge delivery for employees

A survey by Accenture gathered insights from over 1000+ managers to understand how they collect, use, and analyze information. A striking 52% of respondents report that more than half of the information they receive is irrelevant. This means they must make an extra effort to find relevant information from the clutter.

AI makes this process easy by analyzing users’ past searches, preferences, roles, and recent searches to build a personalized profile for each user.

Let’s say your HR team is working on updating policies for employee benefits in your organization. So, based on past queries on healthcare plans and employee surveys and considering their role in the HR department, AI would immediately recommend relevant content such as:

• Domain-specific answers are independent of typical search engine results and help solve as many employee queries as possible.

• Previously drafted employee benefit policies for reference

• Recent changes in the labor laws affecting benefits

• Industry trends in the employee benefits package

• Employee feedback surveys highlighting concerns and current interests.

This personalized access to knowledge ensures that your HR team considers all the crucial elements, without the manual toil of extracting information, to structure the new benefit policy.

6. Facilitating collaborative environment

Implementing AI in your knowledge management system will help you provide seamless access to knowledge and expertise for your teams and customers worldwide.

With real-time and context-aware translations, employees worldwide can access the same reports, policies, and technical documents in their preferred language without losing their true meaning. This eliminates the need to maintain multiple versions of KB in different languages.

Besides translation, AI helps employees connect with the right experts at the right time. It scans your company’s internal data, such as projects, documents, reports, whitepapers, and employee profiles, to identify experts based on formal credentials like certifications and demonstrated experience, such as project contributions and authored papers.

For example, if your marketing team requires insights into consumer behavior for a new marketing campaign, AI may recommend a data scientist who has conducted relevant research on this topic.

7. Identifying and bridging knowledge gaps

Before AI, many organizations hardly realized they had knowledge gaps, and those that did couldn’t do much to help themselves because identifying them is not easy.

AI solves this problem by continuously monitoring how employees interact with internal KB, analyzing search queries, usage patterns, and frequently asked questions. So, if your employees encounter failed search queries or repeatedly ask questions on a topic yielding no helpful resource, this signals a knowledge gap.

When AI identifies the missing information, it alerts the leadership. It suggests the next steps, such as creating new documents, updating old information, or notifying subject matter experts to fill in with relevant information.

McKinsey’s State of AI report reveals that 65% of respondents say their company uses Generative AI regularly. The report also shows that many organizations use AI in at least two business functions.

This increase in the use of AI is due to companies recognizing its power to maximize their operational efficiency.

Let’s see how your enterprise can benefit from AI-powered knowledge management systems:

1. Informed decision-making

AI provides leaders and employees with accurate information at their fingertips.

By analyzing your company’s internal data and monitoring external trends, AI offers distinct patterns and insights that provide a broader perspective into industry trends. This helps leaders in strategic planning and decision-making.

2. Employee and customer satisfaction

Using AI in your KM system guarantees an intuitive user experience for your employees and customers.

Automating repetitive tasks, quicker access to information, and seamless collaboration with experts enable your employees to concentrate only on important tasks and excel in their roles.

For your customers, AI improves self-service by providing quality information and faster query resolution.

3. Relevant information

Incorporating AI into your KM system directly impacts reducing your company’s expenses. Here's how:

• AI reduces the need to hire more workers by automating repetitive tasks such as data entry and responding to common queries.

• By constantly monitoring your KB for updates and flagging issues in maintenance, AI helps you avoid unprecedented costs to the company.

Data privacy and security, data accuracy, and ethical use are the top 3 challenges of organizations when implementing AI in knowledge management systems.

Let's take a closer look at each:

1. Data privacy and security

Integrating AI in your KM system allows it to scan through your organization’s database.

However, your company data contains sensitive information, including customer information, personal employee data, and intellectual property. This information must be handled carefully. Any mishap can lead to data breaches, cyberattacks, and legal consequences.

To prevent such mishaps, you must build robust privacy controls and encryption protocols and regularly audit your AI-powered KM systems against privacy laws such as GDPR and CCPA to ensure compliance.

2. Data quality and relevance

AI’s output is as good as the data you train it with.

If you input incomplete, outdated, or poorly structured data into the AI system, the output will be flawed. Your employees will make decisions based on misleading information, significantly affecting your bottom line.

To ensure your data is valuable, you must take measures to maintain high data quality. It involves breaking down data silos and regularly assessing the accuracy, relevance, and structure of your data.

3. Ethical considerations

Today, many leaders use AI to make data-driven decisions for core parts of their business.

But, AI systems limit users’ understanding of how they make certain decisions. This opacity has raised concerns about accountability and data privacy.

Another challenge is bias in AI systems. If trained on flawed data, AI will inherit biases like racism and discrimination and reflect them in their outputs. This could harm human decision-making.

Knowledge management has been here for decades, but the challenges posed by traditional management systems made use of organizational data limited.

For years, organizations have used only data that seems available to them, leaving a ton of information untapped.

All this changed when AI came into the picture. It started uncovering different aspects of organizational data, making it easier for employees to quickly search and retrieve information, automate mundane tasks, analyze large data sets to extract insights, fill knowledge gaps, personalize information delivery, and much more.

With the rise of AI, there have been some concerns that it might entirely replace humans in their jobs. So, instead of looking at AI as a threat, think of it as an ally to work smarter, not harder.

The reality is that AI is here to stay, and adopting it will give you an early-mover advantage, allowing you to stay ahead of your competition.

So, if you’re thinking about automating your knowledge management system with AI to stay on top of your game, consider Workativ.

Schedule a demo today with Workativ and see what it can do for you!

1. How can Generative AI help in knowledge management?

Gen AI is helping in knowledge management by:

• Automating content creation

• Updating knowledge base in real-time

• Facilitating quick information access

• Personalizing knowledge delivery

• Mapping experts across the organization

• Identifying and bridging knowledge gaps

2. How does AI acquire knowledge?

AI acquires knowledge by processing large datasets with the help of its NLP and ML algorithms. It learns from texts, audio, videos, and past experiences.

Let’s say you want to train an AI Chatbot. You can use your company’s knowledge base to feed structured and unstructured data. AI will quickly scan through your entire KB and learn to provide accurate results.

3. Can AI generate new knowledge?

Yes, AI can generate new knowledge by analyzing your existing knowledge base to uncover patterns and insights that were lacking before.

With AI’s ability to synthesize complex information, you can repurpose complex procedure documents into a reader-friendly format for customers or extract actionable insights from thousands of customer feedback.

4. How will AI change knowledge work?

AI will automate routine tasks like data entry and answering frequent customer queries.

It will enable quick data retrieval, deliver personalized knowledge, and streamline workflows. All this will free your employees to concentrate on revenue-generating tasks.

5. How does AI-powered knowledge management benefit enterprises?

AI-powered KM systems help enterprises to:

• Make informed decisions and achieve long-term organizational goals

• Enhance employee and customer satisfaction by providing an intuitive user experience.

• Reduce the overhead cost by automating repetitive tasks.

6. What are the challenges in traditional knowledge management systems?

Here are the top 5 challenges that businesses face with Traditional KM systems:

• 1. Information in silo

• 2. Manual categorization of data

• 3. Difficulty in managing unstructured data

• 4. Lack of personalization

• 5. Information overload