Knowledge Management System Development Services

We know that the key function of a knowledge management system is to structure knowledge assets in a way that makes it possible for users to seamlessly search and quickly retrieve any information they need, when they need it.

For the role of AI, the obvious question you are probably asking is: How exactly is AI disrupting this critical function of knowledge management systems?

Simply put, AI super charges and replaces traditional Knowledge Management systems as we have always known them. In other words, AI is completely changing knowledge management.

This change is happening in these key areas of knowledge management:

1. Document organization
2. Storage
3. Indexing and search

AI automates and streamlines the entire document organization process. Picture AI as an intelligent digital librarian that can swiftly analyze and categorize documents.

For example, AI organizes documents by clustering them into groups based on topics or themes. This ensures that related content is organized together, and this simplifies navigation within the system.

AI is enabling KMS to store knowledge in innovative ways, which are more efficient and scalable compared to traditional systems.

One way that AI is achieving this is through the use of compressed sensing. This technique allows KMS to store knowledge using less storage space than traditional methods. Here, AI is used to identify the most important information in knowledge content and then storing only that information.

Another way is through the use of federated learning. This technique allows the training of  machine learning models on data that is distributed across multiple devices. This means that KMS can store knowledge on devices that are closer to the users. The end result is better performance and reduced latency.

AI is also being used to develop new ways to store knowledge that are more resistant to cyberattacks. For example, AI is being used to develop hierarchical encryption schemes. These schemes allow knowledge to be stored in multiple layers of encryption. This knowledge management system makes it more difficult for attackers to gain access to knowledge, even if they are able to compromise one layer of encryption.

AI is being used in knowledge management systems to optimize data search and retrieval.

Specifically, generative AI employs what we call Natural Language Processing (NLP) to comprehend complex context. With this level of understanding, the responses that come out of the knowledge management system are more accurate compared to those that come out of traditional KM.

User experience is the foundation of a robust knowledge management system. After all, the knowledge is meant to serve users who depend on it to get work done or get services for the case of customers. 

Generative AI is at the center of this. When user experience in knowledge management systems is turbo charged by AI, these key benefits follow:

In traditional knowledge management, support agents were the ‘engines’ of customer service. Depending on the size of your customer base and the support request volume thereof, the agent costs could ramp up quickly to become a significant expense. But AI has changed this completely, and the costs are now on a downward trend.

Gartner is already predicting that technologies such as conversational AI will scale down contact agent labor costs to significant levels. The projection is that these costs could be reduced by as much as $80 come 2026 and beyond, thanks to AI.

This is super great news for organizations that are struggling with customer support related labor needs.

Time to knowledge is the amount of time a user takes between the moment they start looking for specific information and the moment they retrieve it.

The time- to-knowledge in traditional KMS is so long and often wasteful. But with AI, this time is reduced drastically.

For example, AI-driven predictive analytics can proactively provide users with the most pertinent insights in real-time, saving them a huge effort. 

The fundamental point here is that AI-driven capabilities collectively accelerate the knowledge discovery process.