AI Integration for Knowledge Management Systems – A People and Technology Collaboration

AI Integration Chandrika Rao  |   6 mins read May 14, 2022 | 20 eye icon

Industry's fourth revolution has arrived. To many people, the prospects of Artificial Intelligence and how we will gain from it, are mind-boggling and beyond comprehension. It is predicted that, similar to how the second industrial revolution resulted in the invention of electrification, the fourth industrial revolution will result in 'Cognification.' We are entering a data-driven world, and it is critical to examine the link between Artificial Intelligence (AI) and Knowledge Management (KM) to harness AI more effectively.

Knowledge Management (KM) and Artificial Intelligence (AI) are fundamentally about "knowledge." AI provides tools and procedures for computers or machines to learn. 

AI enables machines to learn, interpret, and apply information to perform tasks and access knowledge that can be transmitted to humans to improve decision-making. 

Knowledge Management (KM) allows knowledge to be comprehended, while AI allows us to expand, utilise, develop, and unleash knowledge in ways we never envisaged. Many KM practitioners and theorists overlook Artificial Intelligence (AI) as a critical building component for the development and advancement of KM.

Knowledge Management (KM) has evolved to the point that many organizations either consider that they have such practices in place, or understand they are pertinent to the knowledge work regularly conducted in various industries. AI was founded before KM and has been rooted in the computing discipline for many decades, used broadly in many domains.

Knowledge workers are individuals who think, reason, create, analyze, and apply insights using non-routine cognitive processes. Knowledge Management is always evolving, adding to its arsenal of tools, technical advancements, and goals. 

Because KM combines skills and expertise, data, search, communication, and technology, it is critical to stay abreast in order to provide employees and customers with access to information. AI is changing the way people engage with software, but the problem remains in making software more meaningful to users in terms of flow, interaction, experience, and roles.

AI is no longer confined to the laboratory but has become an inescapable aspect of modern civilization. We rely on AI systems to assist us in making simple decisions like which movie to watch next or where to go for dinner, as well as more complex, high-stakes decisions like who should get a credit facility. We drive alongside self-driving cars, interact with social media bots on a regular basis, and algorithmic trading now dominates the financial markets. The continuous goal of AI is to improve it to the point that it matches, and in certain situations (such as computation and memory), exceeds the capabilities of the human mind. 

The viability of pursuing and accomplishing such a goal is currently being debated around the world.

Within the framework of the role technology plays in KM, the prospective role of AI in various forms is of special relevance. Such systems are meant to perform actions that have not been explicitly programmed. As a result, the search for programs that simulate intelligence or learning has dominated AI research.  Knowledge-based expert systems (KBES), neural networks (NN), and case-based reasoning (CBR) are the three main categories taken into consideration.

A computer program that provides information and experience for decision-making in a specific area is known as an expert system, otherwise known as a knowledge-based expert system (KBES). A database maintains a set of information and rules that describe all the problem domain data in the computer program framework. This class of AI is distinguished by the ability to use knowledge, or a specific skill or expertise traditionally attributed to a human expert, during the execution of a job or decision. Non-experts around the organization can then access this expertise. An expert system is designed to simulate the decision-making abilities of human experts in a certain discipline, such as construction management or another area of expertise where there is a dearth of conception among engineers or experts, and it can also provide guidance and explanations.

An artificial neural network (NN) is a sort of artificial intelligence that attempts to imitate the biological structure of the human brain and nervous system through its architecture. Speech synthesis, diagnostic challenges, medicine, business and finance, robotic control, signal processing, computer vision, mitigation process control, and biomedical applications are examples of neural network applications.

Neural networks have several advantages over other computer applications, including KBES in that they can generalize, abstract, and potentially even display apparent intuition with incomplete data. They are made up of several nodes that are equivalent to the biological brain's axons and are connected by weighted information links. As needed, fuzzy logic could be incorporated into this structure. As a result, the output is a complex function of all of the various inputs and their interactions. The system's primary goal is to produce results, such as decisions that are as good as, or better than what an expert individual would make, given the identical set of input data.

Case-Based Reasoning (CBR) is not a new concept in engineering. In advanced decision-making systems, AI technique is utilized to support reasoning abilities and learning. The primary premise of CBR is to address new challenges by customizing approaches used to address and solve previous problems. Unlike a neural net, however, the system does not attempt to manipulate or seek relationships between specific inputs. It merely identifies and displays potentially beneficial stored cases that fit the problem descriptions that are later provided to it in the form of a new case. This procedure is iterative, involving the user's questioning and response. When completed, the content and outcomes of successful new cases may be added to existing databases for future use. Manufacturing process design, knowledge management, power system restoration training, ultrasonic inspection, and other applications are among those implemented by CBR.

KM is a synthesis of concepts from applied artificial intelligence, software engineering, business process re-engineering, organizational behavior, and information technology. It is about developing, safeguarding, combining, retrieving, and sharing knowledge; both internally and externally within the organization. One of the most intriguing technological advances in recent years has been the use of AI in conjunction with Knowledge Management.

As we prepare for the Knowledge Era, KM should be a core pillar of an organization's worldview. The growth of KM is undeniable, and if implemented appropriately, it has the potential to provide considerable value to the organization.

Overall, it has been stated that AI-based technology does not give a one-of-a-kind solution to the organizational needs of KM. It cannot yet replace human intelligence and has only a limited ability to address the issue of tacit knowledge. It can, however, contribute as a facilitator of human interaction that remains the primary source of information or knowledge creation, hence offering a framework for promoting and facilitating an organization's KM processes. As a result, it is acknowledged that AI can build ways to acquire, recover; and transmit data and information more efficiently and swiftly. It can also manipulate raw data to provide higher-level information, potentially contributing to new and more effective means of obtaining and deploying knowledge.

Chandrika Rao
Personal Tutor and Facilitator
Athena Global Education

Chandrika Rao holds a Bachelor’s degree in Commerce and a Master's in Business Administration (with a specialization in Marketing), as well as a Post Graduate Diploma in Brand Management and Activation. 

Chandrika has worked in the Banking and Financial Services sector for the last seven years, her professional expertise being Credit Risk, Financial Analysis, Banking Consulting & Training. She also has three years of teaching experience for MBA students. She is a freelance Content Writer and has over two years of experience in both Creative Writing and Writing for research publications and/or academia.

Chandrika is an ardent reader and animal lover; and volunteers for an NGO in her city, working for the welfare of community animals, including rescues, providing foster care, and helping with adoptions.

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