What is AI Knowledge Management ?
We are currently in a world where technology is at the forefront of every innovation and development. As technology advances, artificial intelligence is becoming an essential tool in all industries, especially in the management of knowledge.
When it comes to AI knowledge management, it’s how artificial intelligence (AI) is used to more efficiently and effectively manage an organization’s knowledge.
What is Knowledge Management?
Knowledge management refers to the processes of identifying, categorizing, storing, archiving, and disseminating information within an organization. Knowledge management is critical for completing outcome-focused tasks. It can prove costly to any organization when employees spend valuable time looking for relevant information instead of focusing on areas that drive profitability.
It’s estimated that an employee spends an average of 1.8 hours every day (equaling 9 hours every week) searching for and gathering information. This means that for every five employees, only four show up to work every day. The fifth one is busy searching for answers so that the other four can complete their work.
Enter the knowledge management system (KMS). This system harnesses and disseminates knowledge via AI-based algorithms to improve understanding, collaboration, processes, and operations in an enterprise. A KMS essentially gathers the collective knowledge of an organization, stores it effectively, and makes it easy to find and utilize when needed. Knowledge management systems are supported by a knowledge base, which is a centralized place where information is stored and can be accessed readily.
Types of Knowledge
The definition of knowledge management would be incomplete without a review of the different kinds of knowledge:
Tacit knowledge is usually acquired through experience. It is expected to be intuitively understood. This makes tacit knowledge difficult to articulate and codify. Moreover, it is challenging to transfer tacit knowledge to other individuals within the organization. Some great examples of tacit knowledge include facial recognition, language, and leadership skills.
While some group tacit and implicit knowledge together, specialists and knowledge academics argue there are some nuances that differentiate the two. As mentioned, although tacit knowledge can sometimes be challenging to codify—implicit knowledge doesn’t necessarily have this problem. Implicit knowledge, or ‘know-how’, is simply knowledge that hasn’t been documented yet. It generally exists within processes.
Perhaps the most well-known type of knowledge across enterprises, explicit knowledge can be captured within different organizational documents such as step-by-step guides, manuals and reports. Explicit knowledge can be shared across teams easily and quickly. It includes knowledge assets such as eBooks, white papers, case studies, and other assets.
Explicit knowledge is critical to retaining intellectual capital within an enterprise and facilitating successful knowledge transfers to new employees.
The Need for Knowledge Management
What happens when an organization’s learning and collaboration increases as a result of a well-implemented application of a knowledge management strategy? The most prominent result is more successful business outcomes as a result of faster—and better—decision making. What’s more, organizational processes such as training and onboarding employees also improve, helping employee retention and satisfaction rise throughout the organization.
The management of an organization’s knowledge is critical in today’s business landscape, and without it, a business can be losing valuable time and opportunities. According to Global Industry Analysts’ research, the global knowledge market is expected to cross $2.1 trillion by the year 2030.
Efficient knowledge management helps create an environment that fosters learning, creating, and collaborating. It enables employees to leverage their expertise for the overall benefit of the enterprise and its goals.
By ensuring employees have access to all the necessary information to carry out their work quickly and efficiently, organizations can build a smarter workforce to fuel better day-to-day decisions.
AI-Powered Vs. Traditional Knowledge Management
Knowledge management is one of the most critical internal processes—and, at the same time, the most difficult. This is due to the fact that knowledge is an asset that needs to be managed well in order for it to provide value in the years to come. Knowledge management tools are used to store, arrange, and make available the knowledge gathered in companies in a variety of media such as text, images, and videos.
Traditionally, systems took saved items and used the file name to identify what the items were. So, if an employee was looking for a ‘holiday application form’ but the document was called ‘annual leave form’, then all was lost. Luckily, things have changed a lot from then and continue to adapt at breakneck pace.
On the other side, cognitive search technology can help AI-driven knowledge management systems find relevant data quickly and easily. The language employees use to search for information or files is recognized and processed by this technology more accurately and quickly. This makes it possible to comprehend difficult and complicated queries better and produce the necessary reliable results. Through clever auto-completion, search suggestions, and synonyms for typed words and phrases, AI technology also expedites searching and updating data.
Every organization in the world should be investing in knowledge management to collect employee knowledge, record business processes, articulate standard operating procedures, file records, catalog products, and collect all data about service delivery.
Knowledge management software is essential to a company’s knowledge management strategy. A strong knowledge management system and strategy should enable the organization to effectively capture, communicate, discover, and maintain its organizational knowledge. AI is poised to transform knowledge management, especially in terms of assisting organizations in focusing on knowledge retention, collaboration, and customer service.
AI’s Role in Knowledge Management
The number of businesses using AI has more than doubled over the last five years per McKinsey’s research. If an enterprise made the decision to use AI and implemented it wisely, then they are likely already experiencing some of the benefits it offers. AI has effectively impacted several business sectors- including knowledge management. In fact, knowledge management systems driven by AI have enabled organizations to strengthen their competitive advantage even more
By enabling enterprises to manage knowledge more effectively and efficiently, AI-driven knowledge management systems help organizations to gather, store, and analyze massive amounts of data to give decision-makers insightful information. They can also further improve an organization’s decision-making, productivity, and cost reduction by utilizing AI technologies like deep learning (DL), machine learning (ML), and natural language processing (NLP).
Machine learning algorithms enable knowledge management systems to learn from an organization’s existing data to identify patterns and trends. The insights derived from this enable management to make faster and smarter decisions.
Deep learning is a subset of machine learning. It enables knowledge management systems to process and analyze large amounts of unstructured data—for example, images and videos. Deep learning enables organizations to derive insights at a higher and faster level that would have otherwise been impossible with a manual work approach.
Natural Language Processing
Natural language processing enables knowledge management systems to understand human language—including text and speech. It then helps the system analyze and categorize that information. What this does for a knowledge management system is that it helps it to better understand the needs of users such as employees, providing better content and insights.
Common Uses of AI in Knowledge Management
Now that we’ve gone over the basics of knowledge management and its AI-enabled components, it’s time to learn what it can do. Here, we’ve put together best practice uses of AI knowledge management:
- Analyze and uncover knowledge through deep data analysis
- Interact with users virtually via chatbots to answer queries and guide users to relevant knowledge resources
- Enhance problem-solving collaboration with real-time communication and more accessible document sharing
- Create useful content by extracting hidden knowledge in mounds of data
- Automate tagging and categorizing content to make organizing and retrieving knowledge easy
- Solve complex problems like human experts would
- Deliver intelligent and relevant search results from user queries
- Discover insights, patterns, and trends through data analysis of large amounts of knowledge
- Share and transfer knowledge by creating coordinated, connected systems across organizational silos
- Provide virtual assistants to help by managing queries
The Benefits of Implementing AI in Knowledge Management
Although AI can be used in several different ways, as shown above, the benefits of it depend on how and where it’s applied. Here are some of the benefits an organization can expect to see with the use of AI knowledge management.
A Knowledge Network That Works in Real-Time
Imagine being able to draw from the information and expertise that any coworker possesses because everyone knows everyone and understands their proficiency in a given topic area. Everyone would know exactly who to ask when a problem arose to swiftly solve it. However, even in a start-up or small-to-medium business, this is not conceivable. With employees churn, people routinely come and go. This results in ongoing learning from fresh information and experiences.
Take that concept and apply it to a multinational corporation that employs more than 20,000 people. What happens to a knowledge network then? Sadly, the majority of organizational knowledge remains undiscovered and under-utilized. Information of all levels of importance is locked in silos left undiscovered and unknown. All of that unused information is also probably costing the organization money due to storage costs.
AI enables the whole knowledge network of a company to be seen and accessed in real-time. Employees can quickly and precisely identify the relevant subject matter experts. Every time they interact with the AI-powered knowledge management system, it learns and the knowledge network gets stronger.
Easy and Efficient Knowledge Delivery
For traditional knowledge management solutions to be effective, there needs to be many dedicated employee hours. So, in addition to their regular duties, subject matter experts (i.e. knowledge holders) are tasked with producing comprehensive material and maintaining the knowledge base in their respective fields. Employees looking for answers (i.e. knowledge seekers) are confined to manually searching through the company’s own information source. If they can’t find it there, they are left searching for the ideal expert to respond to their query.
AI allows for a more streamlined knowledge delivery system. When employees use intelligent search capabilities to find their inquiries in the platform, one of three things can happen:
- Their precise query has previously been posed and responded to. As a result, they are satisfied with their discovery, and their search is finished.
- To reduce repetition and speed up response times, AI can identify related questions and automatically share relevant information. After studying the responses, they discovered the solution to one of the related questions, ending their search.
- After multiple searches with the same criteria, a solution to their question cannot be found and they submit a fresh query. Meanwhile, AI has already identified the most qualified experts to respond to the inquiry, and a trustworthy response is provided.. The query is swiftly addressed, the information is recorded, and it is now accessible for use in the future. Their search is now done.
AI’s ability to provide bite-size knowledge to your employees makes the knowledge delivery process easy, whether they are searching for or sharing knowledge.
Improved Productivity and Agility
Text analytics automatically creates relevant tags for each piece of knowledge base information, which helps AI-based search engines bring up relevant results. As the knowledge base expands, this enables every employee to find the right information at the right moment. This, in turn, increases employee productivity.
AI can also proactively notify experts in case there is a knowledge gap. With this strategy, knowledge gaps are swiftly addressed, ensuring that all company information is current with evolving business needs. Because of AI-powered search, the organization’s agility and business continuity are routinely improved.
Responses are Tailored to User Preferences
As employees interact with AI-powered knowledge management systems, it builds connections and understands how they use the system—and for what. It essentially curates knowledge on their feeds and tailors it to their individual preferences. For instance, salespeople who are regularly following product updates and new features will probably have a feed that places a big emphasis on those things.
With knowledge tailored to preferences, each employee will likely be more engaged with the information, retain it longer, and find value in it. This curated knowledge also helps prevent information overload by providing the most useful results, not gargantuan amounts of unhelpful data.
Employees are not static. AI makes a concerted effort to precisely identify employees and their areas of competence in real-time. When people know who their organization’s experts are, collaboration between teams and individuals improves. The system immediately notifies the appropriate individual, so queries are answered more quickly. Additionally, employees are valued for the information and worth they contribute to the company.
Automating Routine Tasks
No human can sort through the mountains of project records and paperwork that many enterprises have. Here is where AI assists in automating mundane, low-value processes like data collection and organization, as well as the dissemination of essential content. Auto-classification of documents by assigning metadata is another example of how AI knowledge management systems simplify back-end knowledge management automation.
Challenges Enterprises Face with AI Knowledge Management
Every technological advancement comes with its own challenges. There are specific issues with the use of AI in knowledge management as well—primarily those that pertain to ethics and governance.
When creating and implementing AI-powered knowledge management systems, organizations need to ensure that ethical issues, including bias, privacy, and transparency, are taken into account by every stakeholder responsible for the system’s design. The enterprise must also make sure that its AI systems abide by all relevant laws and rules, including ones specific to the business including the Health Insurance Portability and Accountability Act (HIPAA), and more broad ones like the General Data Protection Regulation (GDPR).
An AI-powered knowledge management system needs a strong governance framework to overcome these challenges. The governance framework should have a number of essential elements, such as standards and policies, risk, oversight, compliance, and responsibility.
AI-powered knowledge management systems should also prioritize ethics and values. Organizations must make sure that their AI systems are in line with their moral standards and ethical values with the interests of all stakeholders, including clients, staff members, and society at large, in mind.
Policies and Standards
To have a strong governance structure for the knowledge management system, organizations need robust policies and standards in place. The creation, implementation, and application of any AI-powered system should be governed by these well-defined policies and standards. This entails making certain that all AI systems are open, understandable, and subjected to regular audits and assessments.
Another crucial aspect of AI governance for knowledge management systems is risk management. Organizations should recognize and control any risks connected to their AI systems. This includes both technical and ethical risks like bias and discrimination, as well as ethical risks like security flaws and system failures.
Enterprises must make sure their AI systems abide by all relevant laws and rules, including GDPR and HIPAA, and that they are subject to ongoing compliance checks and evaluations.
Accountability and Responsibility
Accountability and oversight are also crucial elements of AI governance in knowledge management systems. Clear lines of responsibility and oversight should be established by organizations for their AI systems, and they should make sure that these systems are regularly monitored, assessed, and updated as needed.
The Future of AI Knowledge Management
The adoption of AI in knowledge management software has been accelerated by the quick emergence of a veritable host of cloud providers that supply numerous industry-standard AI algorithms as a service.
By utilizing AI capabilities, knowledge management systems are already revolutionizing how organizations can create and manage their organizational knowledge. Many knowledge management platforms will soon include cutting-edge AI-based features to help organizations improve collaboration, magnify information sharing, and increase worker productivity.
While AI products and services do so many great things, they can’t do everything. These solutions, including AI knowledge management, aren’t a foolproof guarantee for fully accurate operational activities. For AI to work and be effective, there will likely always need to have a human in the loop (HITL). Organizations should not allow AI to create and publish articles on their own or handle customer interactions without some level of human supervision.