What is AI and ML, and Why Should You Know the Difference?

Futuristic graphical user interface concept.

Twenty years ago, investors and analysts asked businesses, “What’s your web strategy?” Fifteen years ago, it was, “What’s your mobile strategy?” Today, it’s “What’s your AI strategy?”

Artificial intelligence gives businesses powerful new ways to get deeper insights into customer experiences, needs, wants, habits, risks and more. Yet as with the web and mobile, recognizing AI’s “why” isn’t the same as understanding its “how” and “where.”

In fact, many organizations that are new to AI face a common challenge because the technology applies to so many areas. All of the possibilities can quickly become overwhelming as IT leaders take a strategic approach to their investments and initiatives. And then they face a gauntlet of technology choices and implementation options.

To make the most of AI, organizations must understand how it works, the role machine learning plays in recognizing important patterns in the data and the best practices to help them get started.

Machine Learning Puts Data in Context

The first step is understanding how AI works. In a nutshell, AI is software that can do something it wasn’t explicitly written to do. That capability is enabled by machine learning (ML), which uses linear regression, decision trees and other algorithms to identify patterns. ML teaches the AI which patterns to look for so it can respond when it sees them.

For example, suppose a hurricane is headed for the East Coast. A financial services firm could use AI to predict how certain stocks will react before and after the storm. ML will scrutinize historical data about how those stocks performed during hurricanes with similar paths and intensities.

This enables the AI to predict which stocks will benefit, such as building-supply companies, and which ones will suffer, such as amusement parks and hotel chains. These insights help the firm provide a great customer experience because its investors’ portfolios can be fine-tuned to weather the storm.

Getting Started with AI

In any application for any industry, the AI-development process begins with training data and a model designed to perform a task, such as detecting fraud, classifying risk or segmenting customer types. This training data produces a test program capable of that task. Next, new data is fed into the test program so it’s continuously learning and testing. The more data the AI program has to work with, the better it becomes at its task.

But what should that task be? This question is another place where businesses often stumble when using AI for the first time. The following checklist can help organizations to get over that hurdle:

  • Pick a single use case, such as optimizing the customer experience, rather than trying to implement AI immediately across a whole company.
  • Pinpoint the business problems and opportunities you want AI to address and the ways you plan to measure the AI’s impact (metrics).
  • Determine which departments need to provide input, approval and funding.
  • Identify the data types (structured, unstructured or both), sources and lifecycles. Determine what quality data looks like.
  • Choose tools for working with the data, such as libraries, APIs and programming languages.
  • Use the metrics to track results and understand the effect on business goals and challenges.

That’s still a lot to consider. Many organizations deal with the complexity of AI projects by working with a partner that has extensive experience on such initiatives. The right partner also can help to identify the products capable of meeting specific goals and develop governance policies and best practices that ensure data is always secure and compliant with relevant regulations. All of this lays the foundation for AI success.

To learn more about digital transformation technologies, visit CDW.ca/DigitalTransformation