Agentic AI fails due to unrealistic expectations about automation capabilities, poor use case selection, data quality problems across multiple sources, and governance gaps requiring custom solutions.

Companies seeing real returns from AI agents build measurement systems alongside the technology, treating deployment as architectural decisions rather than bolt-on solutions.

And for everyone involved, meaningful change in legal operations happens through evolution, not revolution.

Florida State University researchers found AI buzzwords are seeping into spontaneous speech through unconscious learning, potentially creating algorithmic influence over how we frame ideas and express thoughts.

Generic models are giving way to deep integrations where AI development happens inside the enterprise

I keep seeing teams conflate AI agents with agentic AI, and this distinction matters more than most realize. One's a contained service; the other's a network of intelligent actors making collective decisions.

Excerpt: JPMorgan predicts AI productivity gains in under seven years versus 61 for steam engines. But current entry-level job losses suggest the transition might be rougher than historical precedent indicates.

Scalekit raised $5.5M to build authentication for AI agents. With Gartner predicting 25% of breaches by 2028 will involve compromised agents, traditional identity management needs an upgrade for autonomous workflows.