Data Makes the World Go 'Round

Chapter 14The AI Ecosystem

In this chapter, we will examine the following:

• What’s involved in the AI ecosystem
• Skills and roles
• Best practices for collaboration
• The growing importance of openness

So now the rubber meets the road. Let’s dig into the AI ecosystem and skills that are involved with AI and the roles that you’ll need to think about as you deploy AI. As part of this, we’ll also talk about the move (that has been happening for several years) to open-source and open platforms. This will set us up as we start to talk about building AI applications as well as newer kinds of AI such as generative and agentic applications and operationalizing all of it. This will get a bit technical, but the idea is for you to understand enough about the technical layers to make strategic, budgetary, and governance decisions, obviously not to do the coding yourself.

The AI Ecosystem: What’s Involved

We talked earlier about the data ecosystem and why it is central to modern analytics. We saw that it had producers and consumers and platforms and governance. There is also an AI ecosystem. At a basic level, it contains familiar components—the data infrastructure, the algorithms, the computing resources, the software frameworks, the applications, and the people who design, deploy, and use them.

But something different is happening with this ecosystem. The pace of AI research and productization is unprecedented. New models and methods move from the lab to market in months, not years. Start-ups ...