Artificial Intelligence (AI) is already a driving force when it comes to using it as a tool to improve services and the wider economy in the era of digitization. Besides products and services, performance growth deeply relies on the collaboration between various departments and teams, sustaining all their knowledge, and sharing that know-how to foster productivity.
Combining AI and knowledge sharing drastically improves interactions among everyone in the organization and creates new knowledge and skills that contribute to organizational performance.
The complementary relationship between AI and knowledge sharing is the answer that solves the lack of integration of existing knowledge, such as knowledge gained from past projects, business processes, or lessons learned from specific historical events. Exploring existing knowledge generates new knowledge from business processes and employee interactions. With the implementation of AI technologies and access to a knowledge database, the efficiency of employees significantly improves.
What is knowledge sharing?
Knowledge sharing is essentially the exchange of know-how between employees and is an important facet of the organizational knowledge process. In a way, it’s a resource-based view of the organization that can be used as a strategic tool for competitive advantage. These strategic assets are valuable, unique, and difficult for competitors to imitate or substitute.
Both tacit and explicit knowledge act as the foundation for organizational knowledge, and this combination produces new knowledge that can be leveraged for innovation and strategy purposes.
Tacit knowledge refers to knowledge that is acquired over time by an individual. This knowledge unconsciously becomes part of that individual, and sharing it produces new knowledge that helps refine business processes and strategies. Explicit knowledge, on the other hand, is codified knowledge available in the form of documents, processes, and reports that are stored and shared.
All knowledge or expertise within an organization is shared, transferred, or managed through a system to optimize organizational capabilities that are knowledge driven. Interactions in different forms, like socialization, lead to new knowledge. Thus, it can be seen as a strategic environment to promote the sharing of tacit knowledge. In the case of explicit knowledge, the social construct and environment of externalization facilitate the interaction of employees with the systems and the sharing of tacit knowledge.
The collaboration between AI and knowledge-sharing has the potential to directly enhance the progress of knowledge-sharing methods and practices. This, in turn, can foster innovative concepts and streamline strategic business processes, ultimately leading to improved performance.
The importance of AI for organizational know-how processes
How can organizations overcome the data silos and connect employees with the practices and knowledge that they require? AI redresses this challenge by identifying and strengthening weak ties and, consequently, enabling community-based learning.
At its fundamental core, AI and knowledge sharing (KS) are two sides of the same coin. AI is built on algorithms, NLP, machine learning methods, and human intelligence to support human activities and decision-making. It allows machines to acquire, process, and use knowledge. While knowledge sharing facilitates the understanding of knowledge, AI provides the capabilities to expand, use, and create knowledge in ways we have not yet imagined.
AI helps break down organizational silos in two ways:
- By connecting people separated by boundaries but working on the same issues
- By building more connected systems of coordination
AI promotes creative thinking, creates shared resources among various team members, and facilitates feedback and peer review. Intelligent features like these are increasingly being embedded into enterprise or personal communication systems, enhancing them to function as much more than just communication channels.
Employees will need to learn how to interact with intelligent systems for many of the AI-driven tasks. AI literacy is a crucial component for both managers and their teams who need to interact with these systems. They have to fully appreciate their artificial counterparts, their algorithmic competencies, and their data-centered and analytical skills for workers to be able to interpret AI-based decisions.
It is an essential role for decision-makers to have a curious mindset to actively ask questions, engage with algorithmic results, and provide critical feedback. Any feedback provided, essentially, creates a dynamic training ground for the AI system.
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Contribution of AI and knowledge-sharing integration to organizational performance
Strategic decisions taken by organizations are a direct result of various performance perspectives, such as financial, product market, and shareholder returns. By enhancing employee efficiency and know-how, AI-KS systems positively impact these performance perspectives.
The acknowledgment of knowledge as a resource-based entity has brought about a shift in the definition of organizational assets. Building knowledge networks has become an add-on to the business process, and as a result, there is now a need to invest in systems promoting organizational knowledge activities or intellectual capital.
Organizations rely on employee knowledge and expertise to devise strategies that offer a competitive advantage. Therefore, AI-KS systems reinforce the employee-organization partnership through the common goal of improving performance with shared ownership of knowledge resources. Employees trust the process of tacit knowledge exchange more when AI technologies are involved in knowledge engagements. It is possible to extract further knowledge with a research-based approach.
Using AI, it is possible to generate dynamic social graphs that capture interactions between people and teams and have a detailed perspective on knowledge sources and bottlenecks in the organization. Additionally, using these social graphs, organizations may be able to reward those who have significantly contributed their knowledge and expertise more accurately and equitably.
Potential barriers in AI-KS systems
Despite the progress of AI-enabled technologies that have helped improve business operations and performance, there are still recurring challenges in many business processes. It hinges on the fact that integrating existing and new knowledge into the learning process of AI becomes difficult for organizations, thus creating a lack of an enabling environment.
- Implementation challenges
Organizations often struggle with the implementation of intelligent systems, the distribution process, retention, and reuse of knowledge. As a result, the benefits of AI become limited when it comes to organizational performance.
- Lack of investments
It is important to remember that simply implementing AI alone is not sufficient to improve performance. The association of knowledge activities, such as knowledge gained from past projects using AI, is what contributes to performance and efficiency. However, such knowledge activities are not acknowledged as key factors by organizations, making them limit their investments in its implementation.
- Lack of employee participation
Another potential barrier to the implementation of KS in the organization is the attitudes of the employees toward the new system and their lack of will to participate. There is a need to further educate them on the AI-KS intersectional perspective because such insights are critical to encouraging employee interactions with AI-enabled processes.
AI-KS systems, when integrated effectively in an organization, can significantly aid in problem-solving and decision-making. The activities that are driven by KS social exchange enhance the learning curves in employees, thus improving organizational performance.
However, it is important to understand that a single type of AI system is not enough to support the increasingly complex nature of knowledge workers’ activities; AI systems should be tailored to support the unique roles and processes of knowledge workers.
Additionally, the organization’s policies should reflect the ethical and legal implications of using AI for knowledge sharing and comply with the regulations and best practices.
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