AI-Enhanced Knowledge Management System

In today’s era of digitalization, businesses generate and store vast amounts of data. This data is critical for informed decision-making and improving business operations. Large organizations face significant challenges in managing and extracting valuable insights and staying attuned to the overall organizational sentiment. By establishing a knowledge base that encompasses ideas, lessons learned, and concerns of the organizational participants, a company can foster growth, improvement, and resilience. Miscommunications often lie at the root of community issues. With that in mind, we propose a knowledge management system that provides the opportunity for employees to share their experiences and opinions, assisting businesses in organizing, storing, and analyzing data efficiently.

Over the past few years, large language models, also known as foundation models, have revolutionized the business landscape. Trained on massive amounts of data, these models can understand natural language, leading to applications in diverse industries such as education, finance, and marketing. By integrating state-of-the-art AI technologies into knowledge management systems, organizations save time and money while gaining deeper insights into their employees and customers, ultimately making business knowledge more accessible and appealing.  

Lessons Learned  

Capturing lessons learned is a crucial and continuous effort that should be integrated throughout the entire lifecycle of a project. The term “Lessons learned” refers to the knowledge acquired from personal and collective experiences throughout the execution of a project. By actively sharing these lessons, organizations can avoid repeating mistakes and adopt best practices, leading to more efficient and effective work practices among team members. The process promotes the exchange of innovative approaches and valuable insights, fostering a culture of continuous improvement within the organization. Moreover, leveraging lessons learned can greatly enhance the success of future projects and subsequent stages of ongoing projects, contributing to overall organizational growth and adaptability. 

System Details 

Our AI-augmented knowledge management system, featuring Natural Language Processing (NLP) techniques, offers a convenient and interactive platform for employees to share their lessons learned. We employ KeyBERT, a variation of Google’s Bidirectional Encoder Representations from Transformers1 (BERT), and PatternRank’s2 KeyPhraseVectorizers to extract and display meaningful keywords from user inputs. This approach enables managers and other stakeholders to efficiently access frequently mentioned topics, saving hours of manual data processing.

Furthermore, employee feedback can be clustered into distinct groups based on their key phrases, resulting in an easily accessible and concise knowledge base. To ensure users gain insights into similar experiences while contributing their own, we utilize BERT embeddings to find and display related inputs. Users can review comparable lessons learned and suggestions, determining whether their experience is unique and worth sharing, or already addressed and resolved. This process helps prevent the accumulation of redundant information within the system, ensuring that the knowledge base remains updated and relevant.  


In conclusion, our AI-enhanced knowledge management system offers advanced features such as NLP techniques, clustering algorithms, and BERT embeddings to provide an efficient, interactive, and user-friendly platform for employees to share their lessons learned. By leveraging these technologies, our system promotes continuous improvement, prevents redundant information, and enhances accessibility to business intelligence, ultimately leading to improved organizational performance. 

  1. Devlin, Jacob, et al. "Bert: Pre-training of deep bidirectional transformers for language understanding." arXiv preprint arXiv:1810.04805 (2018). 
  2. Schopf, Tim, Simon Klimek, and Florian Matthes. "Patternrank: leveraging pretrained language models and part of speech for unsupervised keyphrase extraction." arXiv preprint arXiv:2210.05245 (2022). 


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