AI tooling

At the heart of agentic AI lies the concept of imbuing artificial intelligence systems with agency — the ability to act and perceive the world around them, much like human agents. Unlike traditional AI models that operate within predefined parameters and rules, agentic AI systems possess the autonomy to adapt to changing circumstances, respond to stimuli, and make decisions in real time. This fundamental shift from reactive to proactive AI marks a significant leap forward in the field, unlocking a multitude of possibilities across diverse domains

One question kept coming up:

Why haven’t incumbent software providers been able to leverage their valuable data assets yet?

Both giants like Salesforce and growth-stage companies with 10+ years of collected data seem to be struggling to deliver truly differentiated AI experiences. Most of us would assume these companies are sitting on data gold mines that should give them a massive advantage. But is that actually true?

One perspective I found compelling: The historical data these companies have collected simply isn't the right kind to make AI models truly effective. The workflow data, outcomes data, or database info they've aggregated over the years might not be as useful as we'd think. What's most valuable in building agentic AI isn't just workflow data or outcomes data—it's a granular understanding of how humans actually perform tasks.