Jun 25, 2024

How Generative AI is Revolutionizing Knowledge Management Use Case

By harnessing the power of Gen AI in knowledge management, organizations can unlock a new era of information access, knowledge sharing, and automated workflows, ultimately leading to

The Rising role of Generative AI in Knowledge Management Use Case, Enhancing Productivity Across Different Domains.

AI is disrupting many different areas of business.
Gartner predicts that by 2025, 80% of customer service and support organizations will be applying generative AI technology. 
66% of CEOs think AI can drive significant value in human resources.

81% of contact center executives are investing in AI for agent-enabling technologies to improve the agent experience and operational efficiency.
A Harvard Business Review study found that companies using AI for sales increased leads by more than 50%, reduced call time by 60-70%, and reduced costs by 40-60%

Data and knowledge bases are absolutely fundamental to AI and generative AI models. They function as the fuel that powers these models in-

  • Training and Learning: AI and generative AI models learn from massive amounts of data. This data can include text, code, images, videos, or any other digital format. The quality and quantity of data directly impact the model's performance.
  • Understanding the World: Data serves as the model's window to the world. Through data, the model learns patterns, relationships, and the nuances of language.
  • Generating Creative Text: Generative AI models excel at creative text formats. The knowledge base provides the model with the factual data, which allows the model to generate human-quality text that is not only creative but more accurate, relevant, and coherent.
  • Reasoning and Decision-Making: Some AI models are designed for reasoning and decision-making. Here, the knowledge base plays a crucial role in providing the model with background knowledge, relevant facts, and cause-and-effect relationships. This information helps the model make logical inferences and arrive at well-informed decisions.

Problems Organisations faces with vast repository of data

Data Silos and Fragmentation:

Data is scattered across various cloud and on-premise storage systems, creating isolated silos. This makes it difficult to get a holistic view of the information and hinders data analysis.
Finding the right data can be a challenge due to complex access controls and a lack of centralized search capabilities.

Data Governance Challenges:

Organizations struggle to establish clear ownership, access controls, and security protocols for their data. This can lead to data breaches, compliance issues, and difficulty finding the right data for AI projects.

Evolving Data Landscape:

Data is constantly changing and evolving. New data sources emerge, data formats shift, and regulations around data privacy become more complex. Organizations need to be adaptable to keep their data infrastructure up-to-date.

Data cleaning Bottleneck:

As you mentioned, data scientists spend a significant portion of their time (80% according to some estimates) cleaning, integrating, and preparing data before they can even begin analysis. This reduces their time for core tasks like model building and insights generation.

Key Benefits of Gen AI in enhancing the Knowledge Management Usecase

  • Effortless Content Creation: Imagine knowledge bases that automatically generate FAQs, training materials, SOP’s, creative content and reports based on existing data. Gen AI can handle this supporting various tasks & domains- HR, customer support, sales.
  • Personalized User Experiences: Gen AI can personalize knowledge delivery in HR by creating customized learning paths for employees. Similarly, in customer support, chatbots powered by Gen AI can offer personalized solutions and knowledge base suggestions, enhancing customer satisfaction.
  • Intelligent Search and Information Retrieval: Say goodbye to keyword struggles! Gen AI understands the context and intent behind user queries, making information retrieval faster and more accurate across all departments.
  • Multilingual Knowledge Management: Generative AI can translate knowledge base content into different languages, making information accessible to a wider audience and fostering better communication within a global workforce.
  • Optimize and Automate workflows: Ensuring team members are equipped with required sources/assitance on time- empowering self service, which reduced reliance on subject matter experts. Gen AI can generate reports on knowledge base usage, user queries, and trends. This data can identify knowledge gaps and improve the overall effectiveness of the KM system.
  • Content Summarization and Knowledge Base Curation: Generative AI can automatically summarize lengthy documents and extract key information, making it easier for users to grasp complex topics. Additionally, it can help curate and organize knowledge bases by identifying relevant information and categorizing it appropriately.

Boosting Productivity in Different Domains:

By leveraging the power of AI in knowledge management, organizations can unlock a new level of efficiency, productivity, and overall success.


Customer Support: Enterprises can incorporate a customer service chatbot on their website that would use generative AI to be more conversational and context specific, eliminates the need for generic pre-written responses. Retrieval augmented generation (RAG) allows chatbots to efficiently search through internal documents, policies, and FAQs, customer data providing accurate, up-to-date and personalize interactions. Additionally, troubleshoot issues, and even escalate complex problems to human agents. This frees up human representatives to handle more intricate customer interactions, which will increase efficiency & enhance workflow. Summarizations can empowers customer service representatives to quickly grasp the information and answer customer questions effectively.


HR: HR departments can put AI to work through tasks like content generation, RAG & resume screening. Gen AI for content generation like Job Description Creation that attract top talent, Personalized Offer Letters, onboarding materials like welcome emails, sources/materials required during their training based on the role and department. RAG can enhance Employee Self-Service by answering common HR questions about benefits, policies, or company procedures. Automating resume screening with Gen AI can significantly reduce time-to-hire by shortlisting qualified candidates based on pre-defined criteria.


Sales: Gen AI can analyze vast amout of organisations & customers data to generate personalized marketing copy like emails, social media posts, ad copies, generate creative visually appealing pitch decks. Gen AI can analyze customer interactions and predict the likelihood of conversion. This allows sales teams to prioritize leads.

RAG can verify factual information within the creative content generated by AI, backed by real data from the organization. Gen AI can summarize complex product data sheets, white papers, or competitor analysis reports. This provides sales teams with concise and easily digestible information to support their sales conversations.


Data Science: Gen AI can potentially assist with code generation for data cleaning, feature engineering and even generate reports and summarizations highlighting key trends, results, patterns and even generating natural language explanations of insights, transforming data into a usable format. This allows data scientists to quickly grasp the overall structure of the data and identify potential areas for further investigation.

Gen AI can analyze vast amounts of research papers and technical documentation to create a comprehensive knowledge base for data science concepts, algorithms, and best practices. This helps data scientists stay up-to-date on the latest advancements in the field.


Coding:
Generative AI can analyze a company's codebase, internal documentation, and project goals. Based on this information, it can generate summaries or targeted knowledge collections relevant to a developer's specific task. Summarization of complex business Knowledge data can empowers developers to quickly grasp the context & focus on core coding activities and creative problem-solving.

Developers can use Generative AI powered Large Language Models (LLMs) to translate natural language instructions into actual code, aditionally ensure functionality like- Unit Testing Automation, Automatic Code Documentation, Code Explanation, Bug Detection and Debugging

To wrap up, Building a Strong Foundation: AI Knowledge Base

  • A well-structured AI knowledge base serves as the backbone for Gen AI to function effectively. This knowledge base should include comprehensive and up-to-date information relevant to different departments.
  • By integrating domain-specific data and expertise with Gen AI, organizations can create a powerful knowledge management system that caters to the unique needs of each department.

Overall, the integration of Gen AI with knowledge management empowers organizations to:

Capture and share knowledge effectively, information flows more freely within the organization, breaking down silos and fostering collaboration. Easier access to relevant data and insights informs better data-driven decision-making across all levels of the organization. Improved access to information empowers employees and provides a superior customer experience, tha’ll boost satisfaction of both Employee and the Customer.

Decision pointsOpen-Source LLMClose-Source LLM
AccessibilityThe code behind the LLM is freely available for anyone to inspect, modify, and use. This fosters collaboration and innovation.The underlying code is proprietary and not accessible to the public. Users rely on the terms and conditions set by the developer.
CustomizationLLMs can be customized and adapted for specific tasks or applications. Developers can fine-tune the models and experiment with new techniques.Customization options are typically limited. Users might have some options to adjust parameters, but are restricted to the functionalities provided by the developer.
Community & DevelopmentBenefit from a thriving community of developers and researchers who contribute to improvements, bug fixes, and feature enhancements.Development is controlled by the owning company, with limited external contributions.
SupportSupport may come from the community, but users may need to rely on in-house expertise for troubleshooting and maintenance.Typically comes with dedicated support from the developer, offering professional assistance and guidance.
CostGenerally free to use, with minimal costs for running the model on your own infrastructure, & may require investment in technical expertise for customization and maintenance.May involve licensing fees, pay-per-use models or require cloud-based access with associated costs.
Transparency & BiasGreater transparency as the training data and methods are open to scrutiny, potentially reducing bias.Limited transparency makes it harder to identify and address potential biases within the model.
IPCode and potentially training data are publicly accessible, can be used as a foundation for building new models.Code and training data are considered trade secrets, no external contributions
SecurityTraining data might be accessible, raising privacy concerns if it contains sensitive information & Security relies on the communityThe codebase is not publicly accessible, control over the training data and stricter privacy measures & Security depends on the vendor's commitment
ScalabilityUsers might need to invest in their own infrastructure to train and run very large models & require leveraging community experts resourcesCompanies often have access to significant resources for training and scaling their models and can be offered as cloud-based services
Deployment & Integration ComplexityOffers greater flexibility for customization and integration into specific workflows but often requires more technical knowledgeTypically designed for ease of deployment and integration with minimal technical setup. Customization options might be limited to functionalities offered by the vendor.
10 ponits you need to evaluate for your Enterprise Usecases

As organizations adopt generative AI in knowledge management, it is important to start with small pilot projects to test effectiveness and address any integration or performance issues before full-scale deployment. At Fluid AI we offer RAG-based solutions that integrate knowledge repositories, allowing users to incorporate their private and real-time data for processing and leveraging diverse data sources effectively & securely. Book a free Demo Call with us today to explore how we can assist you to kickstart your AI journey

By harnessing the power of Gen AI in knowledge management, organizations can unlock a new era of information access, knowledge sharing, and automated workflows, ultimately leading to a more productive, efficient, and data-driven work environment.

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