IoT Sensor Data Fueling Interest in AI

AI could be applied in similar ways to truck fleet management, IBM’s Biller said. IoT sensors on different parts of a truck could allow AI to assess the truck’s ability to make it from one point to another, and if it’s determined that it won’t, a replacement truck can be readied at a point in between. “If you have no unplanned downtime you can run a much tighter schedule,” he said. “With logistics you really have to focus on the asset and how AI can make that asset more reliable in your operations.”

While such use cases offer great promise to transportation and logistics companies for how AI and IoT together can help improve their operations and processes, change will happen gradually. Kirkpatrick said that as with IoT deployments, many companies in the early stages are deploying AI in isolated fashion. “It’s not like getting new IBM computers and trying them out in accounting and than getting them for everyone else if they work well,” he said. “With AI, you really need a different algorithm for every use case, and you have to make sure for each use case you can generate ROI.” Kirkpatrick said many AI technology vendors he spoke with said it still takes a long time to get enterprise and industrial customers to move past the proof-of-concept stage.

That may change as more companies deploy more extensive IoT networks that generate more data needing real-time attention. AI systems will be able to analyze more data much faster than any human or group of humans can, but companies deploying AI still must accept the notion that they essentially will be implementing technology that may be nowhere near as effective and valuable for them at the outset as it will become over time.

“It’s a revolution where we don’t see all the benefits yet,” IBM’s Biller said. “but they are coming.”