Shelf Blog
Get weekly updates on best practices, trends, and news surrounding knowledge management, AI and customer service innovation.
This post was created by Shelf with Insider Studios. We’ve all heard the explanations for why AI projects fail: the models aren’t advanced enough, they don’t remember past interactions, they hallucinate answers — the list goes on. However, those explanations overlook AI’s...
Key Takeaways An MIT report reveals 95% of AI pilots fail. Contact centers are rushing AI deployments without the governance layer needed for success. We are seeing poor data preparation and lack of feedback loops as the leading causes of AI project failure. Organizations that implement...
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,...
Key Takeaways Poor data quality is silently killing customer support AI initiatives, regardless of how much you spend on AI models or vendors Bad data poisons AI training, routing, deflection, and agent assist, making ROI impossible to achieve The solution is proper data inventory,...
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...
Intro: The Rise of AI and Automation The rapid advancement of artificial intelligence (AI) technologies, particularly in the realm of generative AI, is ushering in a transformative era across various industries. As enterprises embrace these cutting-edge technologies and automate an increasing...
Bridging the Gap: Unlocking Business Value from Unstructured Data In today’s data-driven landscape, organizations grapple with a significant challenge: harnessing the immense potential locked within their unstructured data. While raw AI capabilities have advanced rapidly, translating these...
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...
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating an external retrieval system. This allows the AI to ground responses in authoritative, real-world data, which mitigates hallucinations and extends an LLM’s knowledge base beyond its pre-training data. ...
AI models don’t think—they predict. When they generate false or misleading outputs, it’s because they’re filling in gaps based on patterns in their training data. This phenomenon, known as AI hallucination, leads to responses that sound correct but have no basis in reality. For AI leaders...
Making sure your data is ready for AI agents is critical for the success of your projects. As an AI leader or tech strategist, you understand the importance of data accuracy and integrity in AI models. Well-prepared data leads to more reliable outcomes, higher customer satisfaction, and better...