Why Do AI Projects Fail to Scale From Pilot to Production? AI pilots are usually designed around a narrow use case. A small team may work with a controlled dataset, a limited number of users, and a clearly defined objective. When something goes wrong, the team can often resolve the issue manually. These conditions make experimentation relatively straightforward. Production environments are considerably less controlled. An AI application may need to access information from mul

From Manual to Automated: The Supply Chain Transformation Playbook Supply chains rarely become inefficient overnight. The problems usually build gradually. A team starts with spreadsheets to track inventory. Another team manages orders through email. Warehouse updates live in a separate system. Procurement relies on manual approvals. Transportation data comes from multiple providers. Reports are assembled at the end of the week because no one has a reliable view of what is ha

How to Get AI Into Production When Your Data Is Not Ready Here is the situation most technical teams find themselves in. The business has approved an AI initiative. The use case is legitimate. The model has been selected. And then someone runs an honest assessment of the data that is supposed to feed it, and the picture is not great. Inconsistent formats across source systems. Missing values in critical fields. Historical records that were entered manually and contain errors

What Healthcare Leaders Need to Know Before Deploying an AI Agent Agentic AI is no longer a concept that health systems are evaluating. It is a technology they are deploying. Prior authorisation queues that once required coordinators making phone calls are now being handled by autonomous agents. Revenue cycle workflows that absorbed entire departments are being compressed by systems that monitor, flag, and escalate without human input at each step. Supply chains are being ma