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There's a lot of hype and buzz around machine learning and IoT.
Back in 2017, I explored the burgeoning relationship between Machine Learning (ML) and the Internet of Things (IoT). Since then, both fields have matured significantly, moving beyond hype to deliver tangible value across multiple industries. Today, ML and IoT are not just buzzwords; they are critical components driving innovation, efficiency, and safety in sectors ranging from manufacturing to automotive to retail.
This is an update to my original 2017 post, incorporating modern examples and a reflection on the growing role of cameras as sensors.
There was a lot of hype around machine learning when I originally wrote this post, and with the advent of ChatGPT, the hype has increased exponentially. Just about every major company is asking themselves “what is our AI strategy?”. Today “AI” has become an umbrella term for a wide range of applications of machine learning, including LLMs like ChatGPT, but in this post I’ll be primarily focused on non-LLM applications of machine learning.
At a high level, machine learning takes large amounts of data and generates useful insights that help the organization. That could mean improving processes, cutting costs, creating a better experience for the customer, or opening new business models.
The thing is, most organizations can get many of these benefits from traditional data analytics, without the need for more complicated machine learning applications.
Traditional data analytics excels at explaining historical trends, helping organizations track goals and support decision-making. But when dealing with high-volume, unstructured, real-time data—like sensor streams from IoT systems—machine learning becomes essential. ML enables systems to adapt dynamically, uncover hidden patterns, and make predictions in ways that static models cannot.
The data models that are typical of traditional data analytics are often static and of limited use in addressing fast-changing and unstructured data. When it comes to IoT, it’s often necessary to identify correlations between dozens of sensor inputs and external factors that are rapidly producing millions of data points.
While traditional data analysis would need a model built on past data and expert opinion to establish a relationship between the variables, machine learning starts with the outcome variables (e.g. saving energy) and then automatically looks for predictor variables and their interactions.
In general, machine learning is most valuable when you know the outcome you want but don’t yet know which variables matter most. ML works by identifying those key predictors automatically, learning from vast amounts of data. So, you give the machine learning algorithm the goal and then it “learns” from the data which factors are important in achieving that goal.
A great example (though nearly a decade old as of this update) is Google’s application of machine learning to its data centers in 2016. Data centers need to remain cool, so they require vast amounts of energy for their cooling systems to function. This represents a significant cost to Google, so the goal was to increase efficiency with machine learning.
With 120 variables affecting the cooling system (i.e. fans, pumps speeds, windows, etc.), building a model with classic approaches would be a huge undertaking. Instead, Google applied machine learning and cut its overall energy consumption by an astounding 15%. That represents hundreds of millions of dollars in savings for Google in the years since.
When I originally wrote this post in 2017, there were many emerging use cases for ML and IoT but it was unclear which would become widespread. Today, it’s clear that ML applied to cameras (aka computer vision, or vision AI) is among the most powerful and fastest growing applications of ML plus sensors.
In retrospect, it feels fairly obvious why cameras have emerged as one of the most powerful sensors:
Thanks to their versatility and ubiquity, cameras allow businesses to unlock dozens of high-value use cases—from monitoring compliance to tracking customer journeys—all from a single hardware source.
Let’s take a look at a few different industries with some concrete examples of machine learning applied to sensor data
The automotive industry has become a major adopter of ML-powered IoT solutions, applying them across both vehicle operations and manufacturing environments. From real-time performance analysis in service bays to automated quality checks on assembly lines, machine learning helps automakers improve reliability, reduce downtime, and enhance service experiences. With vehicles and factories increasingly instrumented with sensors—and many already equipped with connected camera systems—automotive companies are using ML to turn raw data into real operational value.
See how Leverege PitCrew is equipping managers and technicians with automated insights into bay utilization, service times, and technician performance to achieve higher efficiency, lower wait times, and increased revenue in automotive service centers.
Manufacturing has become one of the clearest beneficiaries of ML-powered IoT. Sensors embedded in production lines continuously collect data on temperature, vibration, pressure, speed—and increasingly, high-resolution visual data. This data, combined with machine learning, enables transformative gains in:
See how Leverege WorkWatch is improving quality, compliance and productivity with actionable insights across production lines and manufacturing plants—keeping manufacturing workforces safe and operations efficient.
In brick-and-mortar retail, ML and IoT are driving operational efficiency and improving the customer experience. Cameras—already widely deployed for security—are now being used to power real-time insights that optimize stores and reduce costs:
See how Leverege ExpressLane is reducing queue abandonment and increasing sales with actionable insights derived from real-time data that track every step of the customer journey—from entry to checkout.
Machine learning and IoT are no longer emerging technologies—they’re proven tools driving real-world impact across industries. By combining intelligent algorithms with sensor-rich environments, organizations in automotive, manufacturing, and retail are improving efficiency, reducing costs, and delivering better experiences for customers and employees alike. As the technology continues to mature and become more accessible, the question is no longer if ML and IoT will transform operations, but how quickly businesses can adapt to stay ahead.