Enterprise knowledge management and AI integration at a multinational enterprise
.png)
AI Enterprise Knowledge Management
This case study delves into the strategic integration of artificial intelligence (AI) technology within the framework of enterprise knowledge management at a multinational corporation. By leveraging AI-powered solutions, this enterprise sought to enhance knowledge discovery, retrieval, and utilisation across its global operations.
Background
A leading multinational enterprise, had accumulated a vast repository of documents stored across various platforms, including SharePoint and several legacy systems. These documents contained critical knowledge, from technical specs and product roadmaps to internal policies and client agreements.
XponixAI introduced an AI-driven solution tailored to the company's needs:
Solution
1. Deep Learning Document Integration: The AI was trained extensively on the company's expansive document library, understanding context, company terminologies, and intricate document structures.
2. AI Assistant Integration: Post training, a sophisticated AI assistant was deployed and accessible to all employees. Any query related to the company's operations, product details, or any internal procedures could be posed to this AI, which would then pull relevant information in real time.
3. Predictive Analysis and Insights: The AI, having understood the vast data landscape of GlobalTech, started offering predictive insights, such as identifying overlapping tasks, potential product synergies, or even compliance risks from older documents.
1. Collaborative workshops were conducted between XponixAI's team and GlobalTech's IT department to understand the nuances and priorities of the company's data.
2. The AI underwent a rigorous training phase, post which a pilot launch was executed in select departments.
3. Based on feedback, further refinements were made, ensuring the AI understood company jargon, could handle multi-level queries, and was adept at sifting through vast data without oversight.
Implementation
Navigating and retrieving specific information from the scattered data silos was cumbersome and time-consuming. On-boarding new employees became increasingly difficult as the learning curve steepened due to the sheer volume of data.
There was a growing need to make data-driven decisions swiftly, but the necessary insights were often buried in these disparate sources.
Challenge
1. The time taken to retrieve critical company information was reduced by 70%.
2. New employee onboarding became 40% more efficient, as the AI could guide them through company processes, documentation, and frequently asked queries.
3. Decision-making processes saw a marked improvement in speed and efficacy, with managers and executives being able to pull out relevant historical data, comparative analyses, and forecasted predictions effortlessly.
Conclusion:
The company's decision to harness the power of AI to make sense of its vast document repositories transformed its operations. With instantaneous access to data and predictive insights, they were better positioned to make informed decisions, improve productivity, and streamline their onboarding processes, ensuring they remained at the forefront of their industry.
Result
