Machine learning is a little bit like yoga: we all know what it is, we all know it’s good for us, but few of us understand how to actually do it. There’s a mystery surrounding machine learning that (even if our colleagues won’t admit) leaves many people in the built environment overwhelmed. Still, machine learning is more than just an industry buzzword at this point; it’s a necessity. It helps organizations make more accurate estimates, produce more valuable products, use capital and resources efficiently, identify and prioritize safety concerns, perform schedule and portfolio optimization and so much more. Now more than ever, companies are looking at ways machine learning can make them more efficient. International Data Corporation predicts spending on Artificial Intelligence systems like machine learning will reach $97.9 billion in 2023, more than two and one-half times what was spent in 2019.
It’s clear that machine learning can bring incredible cost- and time-saving opportunities to your business, but it’s difficult to know where to start. The concept can seem large and abstract, especially if you’ve never implemented machine learning solutions before. It’s easy to think to yourself, “this isn’t in my existing set of skills,” and hope someone else in your organization leads the charge. Understanding and implementing machine learning is quickly becoming an invaluable skill in our industry because organizations have to move quickly and change often. It is one of the quickest ways to put distance between you and your competitors.
At Enstoa, we work with many organizations dipping their toes into the machine learning pond for the first time. In our experience, if you have a hunch that machine learning could optimize areas of your business, it probably can.
One of our clients came to us with a few machine learning hunches that translated into impactful machine learning solutions. For example, their team spends hundreds of hours a year manually entering data received in vendor quotations. They had a feeling that PDFs could be read and populated in the cost system automatically. We helped with the ML. Their hunch was spot-on, and the solution development is underway.
If you’re still unsure about how to get started with machine learning, here are three good first steps:
Machine learning is sweeping the built environment for a good reason: it brings proven, data-driven results. The last and perhaps most important tip we’ll share with you is simple. If you haven’t already started thinking about how machine learning can help your organization, don’t waste any more time - start now.
Want to get a deeper understanding of Machine Learning quickly before you start implementing the industry’s leading technology at your organization? Sign up for our Machine Learning Essentials course. Learn more here.