Claremont AI Articles · Executive perspective
Agentic AI: From Generating Insights to Executing Work – Are Organizations Ready for the Next AI Transformation?
Artificial Intelligence is entering a new phase of enterprise adoption. While Generative AI has transformed how organizations create content, analyze information, and interact with knowledge, Agentic AI is beginning to transform how work gets done.
The distinction is significant.
Traditional Generative AI primarily responds to prompts by producing content, recommendations, or insights. Agentic AI takes this a step further. It introduces systems capable of understanding objectives, planning tasks, making decisions within defined parameters, interacting with enterprise applications, and executing multi-step workflows with varying degrees of autonomy.
In other words, we are moving from AI that helps people do their work to AI that can increasingly perform portions of that work on their behalf.
Read more here: Agentic AI: From Generating Insights to Executing Work – Are Organizations Ready for the Next AI Transformation?
LLMs and the Economics of Writing Code: Where the Value Is—and Where It Isn’t
Large language models can accelerate software delivery, but treating them as inexpensive, autonomous programmers can create a false economy. Their strongest value often lies in testing, debugging, exploration, and focused assistance—not in handing over an entire engineering problem and assuming the first answer is the right one.
Large language models (LLMs) have changed the economics of interacting with software. A developer can describe a requirement in ordinary language and receive code, test cases, explanations, or possible fixes in seconds. That speed is real. But speed of generation is not the same as speed of delivery, and generated code is not the same as a verified software outcome.
For executives evaluating AI-assisted development, the central question is therefore not whether an LLM can write code. It can. The question is whether using it reduces the total cost of producing reliable, maintainable software once review, integration, testing, rework, security, and long-term ownership are included.
Read more here: LLMs and the Economics of Writing Code: Where the Value Is—and Where It Isn’t