Knowledge is an asset that must be properly managed in order for an organization to grow and stay competitive. Knowledge management tools make it easy to create, catalog, and share information within an organization^.1 They can be applied to various types of information, including sales scripts, company history, and handbooks.²

Well-known examples of KM tools include Zendesk, HubSpot, Confluence, and Infinity.³

Regardless of which tool a company uses, it should:

• clarify information
• manage expectations by providing policies or instructions, and
• provide a way for employees to be held accountable for their responsibilities.³

KM tools are primarily geared towards employees of a company (although a few are designed to be used by customers). These tools can help keep employees safer by enhancing security software.²

Automated tools mean employees spend less time conducting repetitive tasks and have greater opportunities for creativity.²

While the term knowledge management was introduced in the 1980s,¹ the technology used for KM has since changed dramatically. Some of the relatively recent computational developments used in KM include artificial intelligence, data mining, big data, the internet of things (IoT), machine learning, and cloud computing.^4–8

Recently, researchers have investigated how KM plays a role in digital transformation: how KM tools can be integrated with data and processing tools to improve the fourth industrial revolution, or “industry 4.0.”^9,10 This industrial revolution will be defined by increased automation, coupled with smart machines and smart factories.¹⁰

When considering the relevance of AI and machine learning, it’s more important than ever that knowledge of AI and machine learning is well-organized and accessible to employees. 

Why doesn’t knowledge management stay at the top of leadership’s priority lists? It may be explained by the salience bias: our tendency to focus on what’s right in front of us and ignore everything else.

Our salience bias pushes us to focus on the negative aspects of knowledge sharing, instead of the beneficial big-picture implications.

Given that proper knowledge sharing can enhance an organization’s impact and help keep it competitive in a changing landscape, it’s pivotal to reduce salience bias as much as possible. In fact, the level of maturity of an organization's KM methodologies is an important signal of its potential for competitiveness.¹¹

Through our work with The World Bank, we identified ways to reduce salience bias and increase engagement: make KM more social, easier to use, and link it to self-development.¹²

With over 10,000 employees across 120 offices worldwide, The World Bank is a complex ecosystem with immense capacity to benefit from enhanced knowledge management systems. It partnered with The Decision Lab to create a knowledge incentive framework based on motivational and behavioral research.

The Decision Lab conducted over 300 interviews with World Bank staff and stakeholders to identify psychological barriers and drivers to knowledge sharing, which led the research team to create an incentive framework based on three key principles of change to boost knowledge sharing practices. In the end, The Decision Lab not only built and co-published a toolkit to help readers translate these insights into action, but its interventions led to a 65% increase in knowledge-sharing behaviors.

You can find a link to the case study here.

1. Ghani, S. R. (2009). Knowledge management: Tools and techniques. DESIDOC Journal of Library & Information Technology, 29(6), 33.

2. Knowledge Management Software & Tools—A Comprehensive Guide. (n.d.). Zendesk. Retrieved October 25, 2022, from https://www.zendesk.com/service/help-center/knowledge-management-tools/

3. 23 Knowledge Management Tools (Plus Benefits and Definition). (2022, April 2). Indeed Career Guide. https://www.indeed.com/career-advice/career-development/knowledge-management-tools

4. Dennis, C., Marsland, D., & Cockett, W. (2001). Data Mining for Shopping Centres – Customer Knowledge-Management Framework. Journal of Knowledge Management, 5. https://doi.org/10.1108/13673270110411797

5. Dubey, R., Gunasekaran, A., & Childe, S. J. (2018). Big data analytics capability in supply chain agility: The moderating effect of organizational flexibility. Management Decision, 57(8), 2092–2112. https://doi.org/10.1108/MD-01-2018-0119

6. Hopkins, J., & Hawking, P. (2018). Big Data Analytics and IoT in logistics: A case study. The International Journal of Logistics Management, 29(2), 575–591. https://doi.org/10.1108/IJLM-05-2017-0109

7. Khan, Z., & Vorley, T. (2017). Big data text analytics: An enabler of knowledge management. Journal of Knowledge Management, 21(1), 18–34. https://doi.org/10.1108/JKM-06-2015-0238

8. Zhang, T., Wang, W. Y. C., & Pauleen, D. J. (2017). Big data investments in knowledge and non-knowledge intensive firms: What the market tells us. Journal of Knowledge Management, 21(3), 623–639. https://doi.org/10.1108/JKM-12-2016-0522

9. Bordeleau, F.-E., Mosconi, E., & de Santa-Eulalia, L. A. (2020). Business intelligence and analytics value creation in Industry 4.0: A multiple case study in manufacturing medium enterprises. Production Planning & Control, 31(2–3), 173–185. https://doi.org/10.1080/09537287.2019.1631458

10. What is Industry 4.0 and how does it work? (n.d.). IBM. Retrieved October 25, 2022, from https://www.ibm.com/topics/industry-4-0

11. Oliva, F. L., & Kotabe, M. (2019). Barriers, practices, methods and knowledge management tools in startups. Journal of Knowledge Management, 23(9), 1838–1856. https://doi.org/10.1108/JKM-06-2018-0361

12. Sharing the Tools to End Global Poverty | Behavioral Science Case Studies. (2022, July 20). The Decision Lab. https://thedecisionlab.com/case-study/world-bank-knowledge-management