Artificial Intelligence (AI) & Machine Learning (ML) Introduction

August 1, 2022

AI/ML Overview

Is Artificial Intelligence (AI) and Machine Learning (ML) unfamiliar topics? We suggest you start with this video for an overview.

The Case for an AI/ML Strategy

In a given week, we often hear from customers that they “are not ready for AI.” Or “AI is later on the roadmap for us.” These statements are made for good reasons. The reality is that many companies are still struggling with basic data capabilities. Often still trying to accumulate clean data that can be served up in reports and data exploration tools. In short, they are operating on the left of the data maturity curve (see exhibit below). And legacy data tools are fragile, expensive, and inflexible. So, the thought goes, if I’m running my business blind, I don’t have time to invest in AI.

De-prioritizing an AI strategy is a mistake, though. Why? Because more than likely your competition hasn’t. One only needs to look at other market examples to understand AI’s disrupting effect (e.g., Netflix, Uber, Tesla, Davinci Surgery, etc.). So without a strategy to use Artificial Intelligence to improve business outcomes, you leave the door open for your competitors to disrupt the market. This is why at Macula, we believe you don’t simply need a new data strategy; you need a business strategy based in the realities of our data-first world that includes AI/ML.

“In short, software is eating the world.” – Marc Andressen
“Every company is not just a software company, but also a data company.” – Matt Turck

AI/ML Capabilities

So what types of questions can AI/ML answer? Basically, all AI/ML models usually answer one of the following generic questions:

Is this A or B or C or ….?

Is this unusual?

How much?

How many?

How is this organized?

What should I do next?


Now that you know what Machine Learning can do, head over to this post to see real-world examples:


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