AI Ladder short review: main features and scope

AI Ladder short review: main features and scope

The foundation on which the AI Ladder rests is a modern information architecture (IA): it’s the utopian lift of democratizing AI across an enterprise. We say repeatedly in this blog that “there is no AI without IA.” At the same time, if you try to create a modern information architecture, collect your data, and organize your data before you can start any analysis, you’re not likely to get anywhere, at least not this decade. That’s a problem we address specifically. Although it’s a ladder, there are ways to take shortcuts, start with some successful projects, and get on the road to AI without going rung by rung. Indeed, those first successes will help you get the buy-in and support you need for everything else.

Collect data

Find the data that your organization has access to, regardless of where it is or how it is stored. This includes data from external sources and data that’s currently “falling on the floor.”

Organize the data

Data is just a “seething mass of bits” if it isn’t organized. Data needs to be trustworthy if you’re going to have trustworthy results. It needs to be cataloged so others can use it; it needs to be governed, and access needs to be controlled, for regulatory compliance; and it needs to be cleaned so you know it is accurate.

Analyze the data using machine learning

This is the fun part; it’s where you build and deploy AI models developed from your data.

Infuse AI throughout the organization

AI can transform your organization - but it won’t if it’s limited to a few projects in a few departments. The most exciting part of the AI Ladder isn’t the first few successes, it’s finding out how to make your entire organization more effective.

 

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