AI chatbots are not going to make jobsites safer or more efficient, at least not in real time.
Many AI chatbots use generative AI to respond to user prompts. The user enters a question, which is sent to the cloud. Generative AI then uses large language models, which require significant computing power, to analyze the question and send an answer back to the user.
“That’s traditionally how AI has been in the digital world for a long time, but that’s not really possible when you’re talking about the real world,” explained Hemant Banavar, Motive’s Chief Product Officer.
That’s where Edge AI comes into play. Edge AI allows AI models to run directly on a device, reducing reliance on a cloud connection for real-time decision-making. By removing the cloud from the equation, the delay of communicating between the cloud and user is eliminated.
“Think about a construction site where you need to immediately detect if somebody is wearing PPE or not wearing a PPE. You cannot tolerate a loop back from the device to the server and then back to the device because you want to alert them right now,” Banavar said. “Edge AI is about being able to run the whole AI model right there on that device.”
Motive provides solutions for fleet management, driver safety, equipment monitoring and operations software for numerous industries, including trucking, oil and gas, as well as construction.
While these industries require immediate feedback that’s unachievable by cloud-based solutions, they also require devices that do not consume large amounts of power. Motive has developed solutions that use Edge AI while consuming minimal energy.
“You’re drawing power from the vehicle,” Banavar said. “You have to run these models at low power and still be able to have them run with high accuracy.”
AI Vision
Motive’s new AI Vision, introduced at the company’s recent Vision 26 summit in Nashville, is an example of how Edge AI is effective in the field.
AI Vision eliminates manual processes in the field, such as identifying service issues or site hazards, by observing the environment and automatically identifying and logging critical data in real time.
AI Vision builds upon Motive’s existing computer vision models that detect unsafe driver behaviour.
“Our cameras can go anywhere and see anything. Now, can we help our customers solve problems beyond driver safety with this technology and our ability to build models?” Banavar said. “What other problems can we solve for our customers that can be solved with computer vision?”
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By transforming video into structured, actionable data, AI Vision addresses operational challenges across the physical economy, from public-sector infrastructure monitoring to construction-site safety.
For example, Motive created an AI Vision model for a waste collection company to detect overfilled bins. The model helps organizations verify work and bill for violations, while also helping customers select the right bin size without requiring manual driver input.
“It’s a behaviour that we can detect from a camera, and it is actually meaningful revenue for them,” Banavar said.
AI Vision’s capabilities also extend to solving issues like detecting contamination in waste streams, verifying job completion or helping vegetation management companies determine overgrowth.
“We see AI Vision as essentially a capability that we can take to our customers and then apply to whatever domains that they are in,” Banavar said.
On construction sites, Banavar sees AI Vision helping contractors make sure their staff are safe and their methods meet regulations.
For example, a camera mounted on or near an excavator could detect when someone enters its swing radius and alert the operator or management in real time.
“A camera that’s actually looking at the job site is constantly running in real time, and it can detect and alert you if anything there it sees that is not up to code,” Banavar said. “I think there’s a lot of interesting use cases evolving in construction itself, where we believe that this can actually add a lot of value.”
