Organizations deal with an immense amount of information that is often unstructured, exists in multiple formats, and is rarely available to all respective employees. One of the ways to solve the mentioned problem, store data centrally, and make it accessible is to use Knowledge Management Systems (KMS). In this article, we explore KMS, uncovering its functionalities, benefits, and challenges of its implementation and discovering how AI-based knowledge management systems drive decision-making and boost performance.

What is a KMS and how does it work?

A knowledge management system is like a digital library used by companies to collect, sort, share, use, and update knowledge. It makes structured and unstructured knowledge easy to find, use, and share.

The information in these systems comes in three types. The first is explicit knowledge like manuals, procedures, and data reports which are easy to pass on. Next goes implicit knowledge which is more about best practices, how-tos, and training. And the last one is tacit knowledge which is harder to share because it’s based on people’s experiences, insights, intuition, and creativity.

KMS helps to turn tacit knowledge into explicit and make it documentable and accessible. Take Apple, for example. They have lots of knowledge spread out in different departments. This includes product designs, marketing strategies, customer feedback, and patents. A knowledge management system lets them store all this information systematically. No matter where a team member is, they can access the system and use the information.

In a real-life case, say, an engineer in the manufacturing department finds a way to make iPhone production more efficient. They could put this valuable insight into the knowledge management system. Then, any engineer in the company can check this new method out at any time.

In short, a knowledge management system captures and organizes information and eases its sharing. It supports better decision making, encourages innovation, improves customer service, and helps bind a company together. By using such a system, a company not only avoids losing important knowledge when staff leave but also fosters a culture of continuous learning and knowledge sharing.

Role of AI in businesses and why use it in a KMS

According to the last year’s McKinsey Global Survey on AI, the adoption of AI has more than doubled since 2017. This year’s survey shows that the participating organizations regularly employ generative AI in at least one business process. About a quarter of upper management executives have integrated these tools into their work routine. Furthermore, 40% of respondents believe their organizations should upsize their AI investment.

Artificial intelligence also helps businesses manage their knowledge better, making data collection and analysis, as well as decision-making easier and more efficient compared to traditional knowledge management systems.

Benefits of AI-based knowledge management systems

Reducing manual work

AI systems automatically organize, update and categorize data, saving your team countless hours otherwise spent on these tedious tasks and allowing them to focus on tasks that require a human touch.

Improving accessibility

By gathering data in a singular, comprehensive database, AI improves accessibility, allowing your business to readily harness this information for critical processes. Streamlining data management within the organization, an AI-based knowledge management system enables users to easily manage, import, and access data from anywhere through web applications, chatbots, and other systems.

Making better decisions

AI-based knowledge management systems can create decision trees that visually represent information, helping businesses make smarter choices. For example, AI can identify patterns and make predictions about what might happen next in the market. Companies can adjust their plans based on these insights, contributing to their success and competitiveness.

Improving customer service

AI chatbots, which can chat with customers any time of day, provide quick answers and improve customer satisfaction. These advancements also help understand customer behavioral patterns, facilitating effective problem-solving strategies. Combining AI with augmented reality generates a potent blend of customer service options, delivering instructions to customers in real-time and providing a close-to-hand service experience.

Breaking language barriers

The ability to understand and translate several languages allows AI to convert vast knowledge bases into easily digestible and accessible information for users speaking different languages. This AI-powered translation yields realistic phrases, preserving the essence of the information.

Continuous training

AI models can be trained for different purposes and types of business, meaning the companies can further improve these models for better results.

Enhancing teamwork

AI enhances teamwork by efficiently managing, distributing, and retrieving relevant information across the team. Providing timely access to needed knowledge allows team members to learn faster and work cohesively. Tools like Slack use AI to go through conversations and pinpoint useful information. This streamlines communication and makes sure all information is put to good use.

In short, using AI in a KMS, businesses can turn their data into a helpful tool, leading to increased earnings and success in the market.

Examples of AI-based knowledge management systems

Document360

This AI-based knowledge management system makes knowledge-search simpler, assisting staff in content generation, offering detailed analytics, boosting internal team collaboration, and elevating learning experiences. Document360 allows users to easily craft tutorials, reference documents, system manuals, update logs, and substitute static and PDF manuals with dynamic, digital guides. It can create a secure and private database, accessible only after login, for employees or clients.

Lucy

This platform is built on machine learning, optimizing its function the more you use it, while enhancing internal team connectivity and refining user search outcomes. Lucy has the capacity to process every document, audio, and video file from common storage systems such as Box, Dropbox, Sharepoint, and others. If any new information is introduced or amended, Lucy learns from it, too. It can even link to external data sources. Whenever your team needs an answer that resides within your internal data resources, Lucy simplifies the process. It transformes enterprise knowledge into a readily accessible cache of essential insights.

Ayanza

Ayanza is a robust AI-based knowledge management system. It offers streamlined communications and boosts project collaboration. Users can create large knowledge hubs for better teamwork and a self-managing team environment. Ayanza also lets users build a website to share useful content and address customer queries, using an AI writer to develop a comprehensive knowledge base.

Is it better to use a ready-made KMS or create a custom solution?

If your organization demands a speedy deployment with minimal time and capital investment, an off-the-shelf KMS solution is the wise choice. These ready-made systems come equipped with standard functions and a user-friendly interface that’s easy to navigate even for the less tech-savvy. Plus, these solutions are typically cloud-based, enabling seamless accessibility from virtually anywhere. Support and upgrades are often included in your subscription. This choice fits best for businesses where the need for customization isn’t paramount, and the key focus is on speeding up operations and enhancing efficiency with minimal disruptions.

Alternatively, businesses with unique processes and specifications may consider building a custom KMS for internal use. This option requires more time and resources but allows for the tailoring of features to fit the specific business needs and workforce adeptness. Taking this route means that your system can grow and adapt with your business. The custom-built KMS also minimizes reliance on the KMS vendors, thereby reducing vulnerability to external risks. Opt for this route if you have an organization with intricate processes or if your business handles highly sensitive information where security is of utmost importance.

Lastly, it’s possible to create a custom KMS and sell it to other companies. This could prove lucrative if your business has the expertise and resources to not only design a robust, adaptive KMS but also to commercialize it effectively. However, this venture requires considerable time, resources, and potential risks associated with market acceptance and competition. Take this path if you have both the entrepreneurial spirit and the necessary resources to venture into the world of tech product innovation and sales.

To conclude, the choice between using an existing KMS, creating one for internal use, or developing one to sell ultimately depends on your organization’s specific needs, capabilities, and strategic ambitions. Factors such as necessary time commitment, available resources, in-house expertise, and projected ROI should guide this crucial decision. Make a well-informed choice; it can redefine your organizational knowledge management and drive your business towards success.

Where to use KMS solutions

Knowledge management systems (KMS) are useful tools for both businesses and organizations in many sectors. They help capture, spread, and use knowledge effectively. Here’s a look at some specific examples of where knowledge management systems can be used:

1. Healthcare: AI-based knowledge management systems help to store and retrieve important medical knowledge. These can include treatment guidelines, drug information, trial documentation, or medical records. In this setting, a KMS not only boosts efficiency but could also save lives by making critical information readily available.
2. Education and research: Universities and other learning institutions use KMS to compile and distribute learning resources to students. Moreover, an organized knowledge base can be used for research purposes. By sharing academic literature, research findings, lecture notes, and other relevant content, a KMS encourages better learning and research.
3. Government: Government agencies interact with vast amounts of data and information on a daily basis. A KMS can manage this data effectively and make it easier to share information between departments and with the public transparently. Public policies, legal documents, regulations, and citizens’ records can all be incorporated into the KMS.
4. Business corporations: In the corporate ecosystem, knowledge management systems help in storing and retrieving internal knowledge such as process documentation, market research, competitor analysis, and employee expertise. This could significantly improve strategic planning, decision-making, employee training, and customer service.
5. Non-profit organizations: An AI-based knowledge management system assists non-profits in managing and sharing information effectively among employees, volunteers, and members. Policies, fundraising data, donor information, and project details can be managed through these systems, promoting efficiency and improving outcomes.
6. Customer service: KMS promotes efficiency in customer services by having a fully-loaded database. This can include problem-solving methods, ways to fix problems, product knowledge, and how-to guides. This helps customer service teams fix issues quicker and in a more informed way.

In general, knowledge management systems can work in all types of places. They help create an organized and easy-to-use knowledge base that helps an organization grow and perform better.

Challenges in developing and implementing AI-based knowledge management systems

As artificial intelligence continues to mature, organizations increasingly seek to integrate AI-based knowledge management systems. Despite the capabilities of these systems, developing and implementing them poses several challenges.

One of the key challenges you can encounter in developing AI-based knowledge management systems is the handling of unstructured data. Unlike structured data, unstructured data such as videos, images, emails, or social media posts lacks a predefined model. To effectively process and leverage unstructured data, AI algorithms must be meticulously built and trained, a task that demands significant time, technical expertise, and computational power.

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Data privacy also presents a substantial challenge. While AI systems typically improve with access to more data, strict regulations in many jurisdictions limit data collection and usage. Companies developing AI systems must therefore navigate these regulations carefully, ensuring compliance while also maximizing their system’s capabilities.

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Integrating AI-based knowledge management systems with existing IT infrastructures can also be a problem. The system has to be adaptable enough to work with the company’s current infrastructure and must also be flexible to accommodate future growth. In other words, the AI system needs to be both backward and forward-compatible.

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Lastly, the issue of bias in AI systems is a significant hurdle. Bias can creep in during AI development through skewed training data or errors in algorithms, leading to biased results that can disadvantage certain user groups.

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Overcoming these hurdles paves the way for more efficient, effective, and fair AI-based knowledge systems.

Summing up

Using AI in knowledge management systems can greatly help businesses by simplifying data search and management, enhancing teamwork, assisting in decision-making process, and increasing overall efficiency. However, it’s not always easy to start using an AI-based system. Some issues can include managing complex data, respecting data privacy laws, ensuring it works with existing software, and avoiding bias. Successfully navigating these challenges is key to using such a system effectively. As AI keeps getting better, it will continue to play an increasingly important role in how businesses manage data and knowledge, leading to more growth opportunities in the future.

If you’re interested in building an AI-based knowledge management system, we can help you create it from scratch or using your existing materials. We can also integrate your ready-made knowledge system with an AI model. Contact us by clicking on the button below and we’ll get back to you in 24 hours.