Shelf Blog: AI Education
Get weekly updates on best practices, trends, and news surrounding knowledge management, AI and customer service innovation.
Data is classified into two main types: structured and unstructured. Structured data refers to organized information that follows a predefined format and resides in fixed fields within a record or file. Structured data is easily searchable, organized, and can be stored in databases. Unstructured...
Key Takeaways Generative AI processes information fundamentally differently than humans. AI predicts patterns rather than comprehending meaning. This distinction requires completely rethinking enterprise data governance, moving from systems designed for human interpretation to frameworks...
Key Takeaways The real AI race isn’t about having the most advanced models, it’s about having the cleanest, contextually rich, and governed data. While most organizations fixate on AI tools, strategic leaders are building competitive advantages through superior data governance,...
RAG as a service provides Retrieval-Augmented Generation (RAG) technology as a managed solution, combining information retrieval and generative AI models to deliver accurate and relevant outputs. This service offers significant benefits such as improved response accuracy and timely access to...
Vector Solutions is a platform for enhancing training and development in organizations, education, and professional settings. It integrates tools for incident management, reporting, and improving learning outcomes. With Vector Solution, you can boost efficiency and safety while effectively...
The Rush to Deploy Generative AI Nowadays, organizations across industries are scrambling to deploy generative AI. While some have already implemented generative AI projects into production at a small scale, many more are still in the proof-of-concept phase, testing out different use cases. A...
As enterprises race to integrate AI agents into operations, many are discovering a hard truth: it’s not the models holding them back—it’s the mess. Specifically, the mess of unstructured data. While much of the excitement in enterprise AI focuses on models, tools, and interfaces, one fundamental...
Why Good Data is the Secret Ingredient for AI Success The Real Cost of Bad Data in AI AI performance metrics often look straightforward: systems should respond within 3 seconds, successfully complete 85% of tasks, and keep error rates below 5%. But these numbers lose all meaning if the AI is...
Prevent Agent Failure Before It Happens. In today’s data-driven world, AI agents are crucial for maintaining a competitive edge. However, many organizations are unknowingly undermining their AI’s potential due to poor data quality. This article addresses the critical issues of data...
It’s no surprise that artificial intelligence is disrupting the job market. Some roles will be automated entirely, while others will evolve as AI takes over repetitive tasks. The question isn’t just what jobs will AI replace—it’s how fast will it happen, and what does it mean for you? AI is...
AI for predictive analytics represents a cutting-edge approach to forecasting future trends and making data-driven decisions with precision. By integrating AI, you can uncover deep insights, identify emerging opportunities, and mitigate potential risks in real-time. In this article, you’ll...
Over 50% of organizations have paused their Copilot initiatives. Why? Because of data quality and data governance concerns. Generative AI is powerful, but when you feed it bad data, it generates bad responses. “Garbage in, garbage out,” as we say. Inaccurate or irrelevant responses hurt user...