Braintrust: What are the biggest hurdles to using AI in IIoT?
Training data scale, black boxes and sensor calibration are among the factors that can derail AI in IIoT environments, members of Protocol's Braintrust say.
This week, we're heading to the factory floor to dig into the future of the industrial internet of things. We asked the experts to think about what can go wrong when you bring AI into IIoT environments and what executives should be doing to clear the path and ultimately increase the chances of success when they do.
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Over the last few years, we have developed and deployed IoT-based solutions in Industries. These solutions range from video analytics on the edge to sensor-based preventative maintenance schedules, but there are common themes that emerge as challenges in all implementations.
- Hardware Installs – Harsh conditions in an industrial setting can cause problems which otherwise may not surface. A valuable sensor placed at the wrong location can be the difference between a great and mediocre predictive model. An example would be our experience with installing smart cameras at a surface mine in Arizona. Dusty lenses, extreme heat, vibrations in the mounting, sunlight flares at certain times of the day were some of the challenges we had to overcome to get to a robust AI solution.
- Continuous Calibration – Ensuring accurate values and identifying a need for sensor calibration becomes important so that AI models which consume these values can produce reliable results. Sensor drifts or changes in operational conditions need to be detected and accounted for in the models. With a lot of focus given to production value that AI brings, the time and complexity in maintaining underlying IoT devices can often go ignored.
- Change Management – The non-tangible hurdle when introducing AI in any setting is its interaction with humans. This is amplified in an industrial setting where changes to processes and production lines are uncommon. It requires a collaborative development approach with SMEs, operators and end users to leverage their decades of operational knowledge to validate predictive models and build trust.