From the course: AI Project Coordination for Lead Data Scientists
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Resource and team management
From the course: AI Project Coordination for Lead Data Scientists
Resource and team management
- Let's start with a story. You launch an AI agent, designed to automate customer onboarding. It pulls data from internal customer tools, drafts emails, books meetings, and routes tickets. The prototype wows everyone, I've seen that before, but within two weeks, it breaks. Emails go out to the wrong names, tickets route to the wrong team. You find out the problem isn't the model, it's how you looked at the team structure. This example makes one hard, fact clear. AI delivery is not a solo sport. If the roles in your project management aren't clear, the work gets duplicated, dropped, or delayed. So smart teams design their structure before the first sprint. That takes coordination between multiple teams, data scientists, data engineers, ML engineers, product leads, and a few more you probably won't know until you're in there. If you don't define the roles in the teams and understand the role they play, you'll be…
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