Shelf Blog: AI Challenges

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...

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...

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. ...

Google’s Bard chatbot made news with a major error. It wrongly stated that the James Webb Space Telescope captured the first photos of exoplanets. This incident showed how AI hallucinations can spread false information through even the most advanced systems. These AI mistakes aren’t...

AI agents promise automation, efficiency, and smarter decision-making. But too often, they fail to deliver. The reason isn’t the model itself—it’s the data behind it. AI depends on organized, accurate, and complete data. If that foundation is weak, the results will be unreliable. Poor data quality...

In the banking sector, every percentage point in efficiency can translate to billions in revenue. According to McKinsey, GenAI could potentially add $340 billion in revenue to the sector’s annual global revenues. This represents a 4.7% increase in total industry revenues – a surge comparable...