Why is AI/ML important? (Part 2)

It’s no secret that data is an increasingly important business asset, with the amount of data generated and stored globally growing at an exponential rate. Of course, collecting data is pointless if you don’t do anything with it, but these enormous floods of data are simply unmanageable without automated systems to help.

Artificial intelligence, machine learning and deep learning give organizations a way to extract value out of the troves of data they collect, delivering business insights, automating tasks and advancing system capabilities. AI/ML has the potential to transform all aspects of a business by helping them achieve measurable outcomes including:

  • Increasing customer satisfaction

  • Offering differentiated digital services

  • Optimizing existing business services

  • Automating business operations

  • Increasing revenue

  • Reducing costs

AI/ML examples and use cases

That all sounds great, of course, but is on the abstract, hand-wavy side of things. So let’s take a look at some practical use cases and examples where AI/ML is being used to transform industries today.


AI/ML is being used in healthcare applications to increase clinical efficiency, boost diagnosis speed and accuracy, and improve patient outcomes.

HCA Healthcare received the Red Hat Innovation Award for its use of machine learning to develop a real-time predictive analytics product—SPOT (Sepsis Prediction and Optimization of Therapy)—to more accurately and rapidly detect sepsis, a potentially life-threatening condition.


In the telecommunications industry, machine learning is increasingly being used to gain insight into customer behavior, enhance customer experiences, and to optimize 5G network performance, among other things.

In fact, according to our State of Enterprise Open Source report published in early 2021, 66% of telco organizations expect to be using enterprise open source for AI/ML within the next two years, compared to only 37% today.


In the insurance industry, AI/ML is being used for a variety of applications, including to automate claims processing, and to deliver use-based insurance services.

A majority of insurers believe that the modernization of their core systems is a key to differentiating their services in a broad marketplace, and machine learning is part of those modernization efforts.

Financial Services

Financial services are similarly using AI/ML to modernize and improve their offerings, including to personalize customer services, improve risk analysis, and to better detect fraud and money laundering.

As the quantity of data financial institutions have to deal with continues to grow, the capabilities of machine learning are expected to make fraud detection models more robust, and to help optimize bank service processing.


The automotive industry has seen an enormous amount of change and upheaval in the past few years with the advent of electric and autonomous vehicles, predictive maintenance models, and a wide array of other disruptive trends across the industry.

And of course AI/ML is a big part of this transformation. For example, it is a key part of BMW Group’s automated vehicle initiatives.


Energy providers around the world are also in the middle of an industry transformation, with new ways of generating, storing, delivering and using energy changing the competitive landscape. Additionally, global climate concerns, market drivers and technological advancements have also changed the landscape considerably.

The energy sector is already using AI/ML to develop intelligent power plants, optimize consumption and costs, develop predictive maintenance models, optimize field operations and safety and improve energy trading.

Getting started with AI/ML in your organization

While AI/ML is clearly a powerfully transformative technology that can provide an enormous amount of value in any industry, getting started can seem more than a little overwhelming.

The good news is that you can start small. It’s possible to adopt AI/ML into your organization without a huge upfront investment, so you can get your feet wet and start to figure out how and where AI/ML can benefit your organization in smaller, easier to manage pieces.

If you’d like to know more, we’ve written a 13-point roadmap about how to start your AI/ML journey.