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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-
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.
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
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.
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.
Decision points | Open-Source LLM | Close-Source LLM |
---|---|---|
Accessibility | The 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. |
Customization | LLMs 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 & Development | Benefit 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. |
Support | Support 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. |
Cost | Generally 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 & Bias | Greater 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. |
IP | Code 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 |
Security | Training data might be accessible, raising privacy concerns if it contains sensitive information & Security relies on the community | The codebase is not publicly accessible, control over the training data and stricter privacy measures & Security depends on the vendor's commitment |
Scalability | Users might need to invest in their own infrastructure to train and run very large models & require leveraging community experts resources | Companies often have access to significant resources for training and scaling their models and can be offered as cloud-based services |
Deployment & Integration Complexity | Offers greater flexibility for customization and integration into specific workflows but often requires more technical knowledge | Typically designed for ease of deployment and integration with minimal technical setup. Customization options might be limited to functionalities offered by the vendor. |
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