Artificial Intelligence (AI) holds the promise of transforming industries, speeding up processes, and delivering strategic insights at unprecedented levels.
From 15 weeks to 10 minutes: Novo Nordisk now drafts clinical study reports with AI. Posts like this are common these days! What we don’t hear enough about is the rigorous planning and testing that goes into creating a controlled, repeatable solution. There was a lot of groundwork by the
The main challenge most organizations must solve is how to incorporate proprietary enterprise content — your intellectual property — into AI solutions. General-purpose AI models don’t know your business content and your IT and security teams are working hard to keep it that way.
Almost daily, I have conversations with content leaders struggling to solve complex challenges in their organizations. They’re trying to help employees find critical information scattered across multiple systems, scale content creation without ballooning costs, measure the business impact of their content investments, and ensure consistency across thousands of content assets.
AI is completely disrupting how organizations need to think about their content, whether it is customer-facing or internal. The data reveals just how profound—and significant–this disruption has become
Ain’t that the truth? When projects are easy, inexpensive, and quick they are likely to get done. Even tasks that might not be so important have a chance.
Recently, I have been doing a bunch of research on Retrieval Augmented Generation (RAG). RAG uses a combination of generative-based and retrieval-based artificial intelligence to produce results. Essentially, it combines what it finds with what it creates and gives you an answer to a query.
The main gist of the article is large AI companies including OpenAI, Google, and Meta are running out of training content for their AI engines. Before we talk about what they did next, let’s think about the implications of this.