attAInable

Agentic AI was supposed to be the natural evolution of generative AI. A tool that could “make decisions” with holistic and relevant contextual knowledge, plan and articulate independently, learn and adapt over time, pursue goals autonomously, and take action by converting its planning outputs into actionable execution.

“ERP transformation through AI augmentation” is a mouthful, but every word is a building block that explains what happens when an enterprise resource planning system is transformed with a network of AI modules rather than refactoring, rearchitecting, or rebuilding it.

Business corner cases can deplete your company’s resources at an astonishing rate; burying the information you need to make smart decisions, sapping the productivity and satisfaction of your employees, and whittling away at profits.

Data democratization is broadly described as capturing more data and making it more widely available to more people. It’s a straightforward definition; however, it’s odd that we aren’t questioning it more now that the sheer volume, variety, and velocity of data the world creates is almost incomprehensible.

Most of the AI talk swirling around on the news, in social media, and in online forums is anecdotal, speculative, or raw click-bait. Noise, not signal.

In the rapidly evolving landscape of artificial intelligence, the fusion of generative models with advanced retrieval techniques has paved the way for Retrieval Augmented Generation (RAG). This innovative approach enhances the accuracy and contextual relevance of responses produced by large language models (LLMs).