Why Does Business AI Adoption Stall?

Vineet Edupuganti says a lot of what bottlenecks AI adoption is twofold: people and process, and data in context.

Projects need people to stand them up. Without the right expertise or the right desire, or when the work being automated is not even working properly in the first place, he files the project under ill-formed use of AI. It can sound like a good idea on paper while the prerequisites to succeed are missing. From the technical side, the fuel for AI, and one of the highest-order drivers of how effective AI can be, is data in context. Automating financial fraud detection at a consumer lending company is very difficult with no insight into what fraudulent claims look like. People often do not have the right data foundation. They toss AI at a problem and hope it will work. The smartest model in the world does not work out without the right data.

A context graph that models the enterprise, with agents taking actions on top of it, unlocked a huge wave of outcomes once the data piece was right.