AI for Knowledge Management
Knowledge management (KM) collectively refers to the methods of creating, sharing, using, and managing the information and knowledge existing within a company. The traditional KM methodologies have developed over the years, and their value cannot be overemphasized.
But the aggressive shift to remote and hybrid working models is quickly rendering these practices insufficient.
Luckily, AI has emerged as the right technology to help compensate for these deficiencies. Welcome to the new era where AI is completely changing how knowledge is managed in companies.
For perspective, about 70% of customers now prefer self-service portal options as opposed to email and phone. In another study, 83% of online shoppers expect to receive assistance in less than 5 minutes. With such trends, you’ll need a powerful knowledge management system that can serve the right information at the right time.
In this article, we discuss AI in knowledge management and how you can put it into effective use in your company.
What do we mean by AI for knowledge management?
In simple terms, AI for knowledge management refers to the use of Artificial Intelligence technologies to enhance the storage, retrieval and utilization of knowledge. The key AI technologies that are used in knowledge management include machine learning, natural language processing, and knowledge representation.
Some of the areas where you can quickly leverage AI in knowledge management include:
- Classifying and tagging information
- Extraction of insights from unstructured data
- Facilitating knowledge sharing and collaboration
- Providing personalized recommendations
- Automating routine knowledge-based tasks
AI is revolutionizing Knowledge Management Systems
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:
- Document organization
- Storage
- Indexing and search
How AI is disrupting document organization
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.
How AI is disrupting knowledge storage
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.
How AI is disrupting indexing and search in knowledge management systems
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.
Key benefits of using AI for knowledge management
The overarching benefit of using Artificial Intelligence in knowledge management is the ability to harness vast amounts of information and turn it into a tool that serves many needs such as customer satisfaction, innovation and decision making.
Here are the key benefits of AI for knowledge management:
1. AI improves task efficiency
Generative AI capabilities boost efficiency by automating tasks, saving up to 30% of employees’ time. With streamlined processes, errors decrease, and productivity increases.
With more time in their hands, knowledge management professionals can focus on strategic activities like analysis and decision-making.
2. AI contributes to scalability
Scalability refers to the ability of an organization’s systems to handle increased demand, larger workloads, and evolving needs.It means staying competitive, serving more customers, and maintaining efficiency as you expand.
When it comes to knowledge management (KM), leveraging AI ensures seamless expansion. Practices like Machine Learning Operations (MLOps) make it possible for organizations to create replicable development pipelines, enabling enterprise-wide AI deployment. This scalability allows AI solutions to handle larger datasets, complex tasks, and increased user demands, driving efficiency and impact.
3. AI brings speed into information processing
There is no doubt that slow processing leads to outdated information, which hinders timely responses to market trends or customer needs. It may also impede collaboration and innovation, limiting your organization’s ability to stay competitive. Neural network processing (NLP) and machine learning algorithms are two examples of how AI for knowledge management works to improve speed.
NLP algorithms enable quick analysis of unstructured data, extracting valuable insights from text-based documents. Machine learning algorithms automate repetitive tasks like data categorization and prioritize information based on relevance.
4. Artificial intelligence enhances customer satisfaction
The key to customers retention is satisfaction, with 86% ready to pay more for an enhanced experience. AI for knowledge management raises customer satisfaction through.
- Intelligent chatbots that address basic inquiries 24/7, offering self service solutions, immediate answers, and reducing wait times.
- AI recommends relevant knowledge based on customer history, creating a tailored experience that feels understood.
- AI proactively identifies potential issues, offering solutions before they become problems, exceeding expectations.
Overall, AI’s efficiency and personalization capabilities boost satisfaction, driving positive customer relationships and business growth.
5. AI enables and supports multilingual capabilities
For a sizable number of companies, most if not all the knowledge base is normally in one language. In such a setting, it would be very difficult to use this knowledge to serve people (especially customers) who speak different languages.
Imagine a scenario where all your company’s knowledge base is in English but you have customers who speak a totally different language and are seeking real-time answers from the customer support teams. The knowledge base may not help much since it’s only in English, yet all the information is right there. AI in knowledge management changes all this, enabling realistic and real-time translation.
Serving clients and staff in their mother tongues promotes diversity, broadens the company’s market reach, and increases global competitiveness. For example, picture your English-speaking support staff helping Spanish-speaking clients in Mexico or French-speaking staff in France with ease!
AI makes multilingualism possible by utilizing natural language processing algorithms to interpret and translate text. For example, chatbots powered by AI can converse with clients in the language of their choice, offering smooth support and increasing client engagement in foreign markets.
AI and user experience in knowledge management systems
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:
Reduction in the labor costs associated with KM
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.
Reduction in time to knowledge
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.
Use cases for AI-powered knowledge management
The information highway within organizations is often congested, hindering productivity and decision-making.
Thankfully, AI is emerging as a powerful traffic controller, streamlining knowledge flow and unlocking its true potential.
Here are some of the major ways that demonstrate how AI is being used to solve some of the biggest challenges that companies face today when it comes to knowledge management.
1. Co-browsing
In the context of AI for knowledge management, co-browsing is a capability that makes it possible for multiple people to browse the same webpage, app, document, or indeed any digital platform simultaneously regardless of where any individual user is located.
This capability is useful in situations like when one staff member is showing something to another colleague or several of them. It can also become useful for customer support agents. Think of when an agent prefers a customer they are attending to to join them in a browsing session so they can follow the resolution proceedings in real-time.
Essentially,co-browsing allows the sharing of one single page in real time and therefore makes conversation and solution solving very easy. Co-browsing really finds best use in areas such as customer service, sales, and training, where it only really deals with web content unlike screen sharing.
Powered by AI, co-browsing becomes more intuitive in knowledge management. AI algorithms analyze user interactions and preferences, providing personalized recommendations and suggestions during co-browsing sessions. Machine learning models predict user behavior and optimize the co-browsing experience based on historical data.
2. Smart decision trees
Remember those rigid decision trees you’d encounter in school? Toss them out. Smart decision trees, powered by AI, are here to revolutionize knowledge management. They’re not static maps, but dynamic GPS systems guiding employees through complex situations with personalized recommendations.
Imagine a loan officer handling a loan application. Instead of a basic “good credit/bad credit” tree, AI considers factors like income, job stability, and market trends. The result? A tailored decision – offer the standard rate, suggest a different product, or negotiate a special deal.
3. Unpacking behavioral patterns
An AI-powered knowledge management system can be customized to enable it to study and understand user patterns.
For example, based on customer patterns, the KM system can predict what certain customers are likely to ask as soon as they join the support queue. With the right prediction, the system will line up the appropriate answers, making it easy for the agent to serve the customer.
Additionally, the system can over time group together all the commonly asked questions. These can then be used to power a self-service app or portal where customers will easily find satisfying answers to their questions.
All this is made possible through analysis of different sources of data. The sources may include customer feedback, employee surveys, social media, and web analytics. The goal is to derive patterns on how people relate to products, services, and processes.
4. Recommendation systems that can suggest relevant knowledge to employees
Ever wondered how a platform like YouTube suggests videos tailored to interests? This is the magic of recommendation systems.
These systems analyze user behavior and preferences and offer personalized and automated content curation, enhancing productivity and decision-making.
In the corporate realm, AI-driven recommendations extend beyond videos. A recommendation system in a typical company will guide employees to relevant knowledge and resources, fostering collaboration, and driving innovation.
5. Analysis
Companies leverage AI for various analyses, including text, sentiment, and predictive analytics, enhancing knowledge management. Text analysis extracts insights from documents, aiding knowledge discovery.
Sentiment analysis makes it possible to measure customer perceptions in order to derive improvement. The derivation of strategic plans that anticipate future trends can be done through predictive analytics.
6. Employee collaboration
AI-driven knowledge management enhances collaboration through technologies like smart collaborative platforms which enable real-time co-working on tasks and communication.
A good example of an AI-powered collaboration tool is Avoma. The meeting assistant facilitates participants to automate note taking during meetings, transcribe discussions for easy reference, and analyze content for insights.
7. Expert systems
Expert systems are computer programs that simulate human experts’ decision-making processes using knowledge bases. These systems use preset rules to analyze data, draw conclusions, and carry out particular activities.
They offer expert guidance, make decisions automatically, help with problem-solving, and spot mistakes or dangers. Expert systems perform exceptionally well in jobs that need domain-specific knowledge and expertise,such as detecting medical ailments or resolving technological problems.
8. Virtual Assistants
AI can be used to create virtual assistants from a company’s massive knowledge base by applying natural language processing and generation techniques.
These techniques allow the AI-powered virtual assistant to understand a user’s queries, search for relevant information in the knowledge base, and generate natural and coherent responses.
AI-powered virtual assistants enable companies to provide faster, more accurate, and more personalized customer service.
AI-powered knowledge management in action: A look at Accenture
For its workforce of over 500,000 employees, Accenture succeeded in automating internal knowledge sharing and access to expert knowledge.
What was the problem?
Traditional knowledge management systems were siloed, static, and keyword-based. This made it difficult to find relevant information quickly. It hindered employee productivity and collaboration.
Solution: Accenture deployed its own AI-powered “Knowledge Assistant” solution. This platform leverages natural language processing to understand user queries and surface relevant documents, experts, and solutions. Additionally, machine learning algorithms analyze user interactions and historical data to continuously improve search results and personalize recommendations.
Result: 50% reduction in time spent searching for knowledge: Employees can now find the information they need faster, leading to increased productivity and efficiency. Timely access to internal expertise also helps teams solve client problems promptly and effectively.
How to settle on the right knowledge management system- checklist
|
Criteria |
Description |
|
Compatibility |
Ensure the system can integrate seamlessly with the company’s existing software and platforms |
|
Security |
Verify the system’s security measures to protect sensitive information and prevent breaches |
|
Scalability |
Assess whether the system can accommodate future growth and increasing data volumes |
|
Usability |
Evaluate the user interface and accessibility for employees at all levels of technical skill |
|
Search Functionality |
Check the effectiveness of the search feature in retrieving relevant information efficiently |
AI is not just transforming knowledge management; it’s redefining it. From co-browsing to expert systems, these intelligent tools unlock hidden understanding, empower workforces, and fuel innovation.
As AI evolves, its impact in knowledge management will only grow, pushing organizations towards a future where knowledge is truly accessible, collaborative, and a driving force for success – as it should be.
How can Tricension help with knowledge management development
The foundation of our Knowledge Management System Development Services is the Domain AI Engine. Our proprietary engine is capable of using your organization’s data to create a powerful knowledge base.
The engine works by ingesting data from different file formats such as .pdf and .doc. This ingested data is then transformed into a custom generative pretrained transformer that is purely designed for your unique business environment. This knowledge management system is then connected to different systems within the organization to provide responses that serve the operational needs of the business.
Based on this, we offer knowledge management system development services that are tailored for diverse needs across different industries.
We view our knowledge management development service as a partnership opportunity to transform your essential knowledge into a powerhouse of growth and increased revenue.
