AI “its got legs”
This last week I spoke with two folks on AI intersection within their work and how their work intersects with one another. One was a CPO for a startup that is bringing AI into their life sciences products in a forefront way. The other was a compliance manager for a pharma company. The second happens to be my wife. For me, their work intersects. They both certainly got me thinking harder about AI agents. The first of the two was breaking down knowledge silos in organizations and I definitely saw a play for AI agents and the use of tools by those agents in their approach. The second needed some exuberant discussion on the potential of how AI can contribute to their organizational goals and needs; and without mansplaining, I was glad to help. :-) The conversations only reinforced in me where we are headed in AI adoption.
There were discussions on trend analysis, small language models, retrieval-augmented generation (RAG), and how AI agents and tools work to bring different knowledge repositories into a single interface with human consumable outputs, just to name a few topics. Surprisingly or not, a lot of these topics were not with the CPO but with the wife; putting the theory into practical examples. It was motivational to me.
I have been looking for good, practical, and real-world examples of AI agenic tools to work on as knowledge-expanding exercises; whether they work or, in the end, surface the immaturity of the technology. I don’t get especially motivated by the common and tired examples of financial analysis, LinkedIn scraping for recruiting, or travel booking scenarios that every agent example seems to rely on. These discussions were a step forward.
These topics reinforced with me that for AI and its agents — “its got legs”.
