Mind the AI Gap: AI Adoption Is an Operating-Model Problem

AI capability is doubling every six to seven months. Most organizations can't absorb it anywhere near that fast, and the distance between the two is where advantage now compounds or stalls.

In a recent TechStrong TV conversation with Alan Shimel, Brian Paniccia, Chief Product Officer at nVeris, makes the case that value stream management is the missing layer for closing that gap. AI can only improve work an organization can actually see, and most don't have a clear, shared picture of how their work flows. So AI gets bolted onto isolated tasks while the system around them stays the same. The capability arrives; the absorption doesn't.

See the work before you automate it

Closing the gap starts with making the work visible. nVeris replaces the month-long mapping workshop with a living model of how an organization runs, built collaboratively and in real time from its own signal. Teams see where work actually slows, rank the constraints by impact, and decide where AI earns its place before committing resources.

The human layer is the deciding factor

Brian's argument turns on people. Keeping teams both in the loop and on the loop, validating the model and steering the system around it, is what decides which side of the curve an organization lands on. AI does the heavy lifting; people stay in command.

He and Alan also get into the economics of running agentic AI at scale. Orchestrating across smaller, local models rather than routing every request to a single large one keeps token costs from spiraling as adoption grows.

The gap won't close on its own. The organizations that pull ahead will be the ones that can see their own operations clearly enough to put AI exactly where it pays.

Watch the full conversation: https://techstrong.tv/videos/interviews/mind-the-ai-gap-value-streams-and-the-adoption-curve