​Why AI Needs An Orchestration Layer

​Rizwan Jan is VP and chief information officer of The CNA Corporation.

In my industry, I often see companies implement AI in a fragmented way. Departments like finance and HR each use their own native AI system. Each one works, but the data stays siloed. Nothing is bridging the two systems except people: Someone has to extract data out of the HR system and manually hand it off to another in finance, who then has to upload it to their system.​

AI without integration just creates more fragmentation. When companies spin up models within their own departments or business units, they end up with pockets of employees using their own engines with their own dashboards. Ask what they’re doing with the new capacity, and the answer might be “getting coffee” or “having longer lunches”—none of which is driving business value at all. ​

AI models that never connect across boundaries within the organization only get a subset of the data they need to make informed decisions. Instead, companies should be asking how AI is actually driving business value. The answer is an orchestration layer that stitches AI systems together. ​

Have The Conversations First

Creating an orchestration layer requires restructuring how work gets done, not deploying yet another platform. The first step is building trust within the company. This means explaining why, how and where AI is going to be introduced and what problems it is trying to solve, as well as where human judgment and accountability are going to be retained.​

It’s crucial to develop trust between those who deploy the technology and those who use it. Trust drives adoption, and a key part of that comes from having conversations before rollout. Too often, IT will push a system onto employees with little or no explanation. We first need to ask whether people actually need a new tool. How will it make their job easier? Is it going to provide more time or higher-value research? ​