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Remi Duquette

Remi Duquette

Vice President of Industrial AI, Maya HTT

Finding a good IIoT solutions provider can dramatically improve the way an industrial engineering company operates.

Artificial intelligence (AI) and the Internet of Things (IoT) have enormous potential to help industrial engineering firms work better and smarter. With the availability of advanced algorithms, computational power, parallel computing, and real-time data gathering via IoT and the industrial Internet of Things (IIoT), AI is quickly becoming an essential part of any successful business strategy.

Starting from the beginning

When investing in IIoT, it’s crucial for companies to first examine a business use case rather than jumping on the AI bandwagon and applying technologies reflexively. “We recommend that companies start with their business use case and understand their business first,” says Remi Duquette, Vice President of Industrial AI at Maya HTT, a company that provides computer-aided engineering software to industrial engineering companies and that is an expert in industrial AI and IIoT, engineering, and software development.

Maya HTT conducts readiness assessments to help its customers determine if they’re ready to implement AI or if they need to iron out any details first — such as data acquisition, quality, or quantity. “There are all sorts of new data strategies and AI-based strategies that you can apply once you have this IIoT under control and properly road-mapped for the future,” says Duquette.

Digital transformation roadmaps

“When it comes to developing a digital transformation roadmap, the first step is understanding the data needed in support of your business use cases, and how to connect the assets and components out in the field, whether in operations, your manufacturing plant, or your engineering process,” says Duquette. “The next step is analyzing that data, so people can start understanding the root cause of what’s happening. And you should always assume your data is dirty until proven clean, and not the other way around!”

After that comes implementation of new technologies such as various deep neural networks, reinforcement learning models, where you are building up your predictive capacity, and then adapting and steering accordingly. “Because AI models are fed by data, they can be made to self-update and always bring you the optimal solution,” says Duquette. “It’s a self-optimizing loop learning from data (or learning from the environment in the case of reinforcement learning) instead of being statically programmed by human beings.”

The industrial manufacturing and operations world are embracing digital transformation, but the transformation is actually more about people and process than technology. “Technology is an enabler but for the enabler to have value, there are always people in the loop,” says Duquette.

Infinite possibilities  

AI presents businesses with infinite possibilities. From agriculture to the marine industry, electric vehicles and aerospace, companies can leverage AI solutions to gain a competitive advantage. “For example, there are typically many stages to manufacturing batteries for electric cars,” explains Duquette. “In one case, we built up to 130 different neural networks and in the end optimized two of them, and it increased the product quality and reduced the failure rate and toxic material production waste by over 78%.

IIoT technologies provide manufacturers with a suite of proactive solutions to help isolate issues before they cause complications and costly repairs. The time and resources saved means that engineers can spend less time solving the problems of yesterday’s processes and more time focusing on the innovations of tomorrow.

Contact Maya HTT for a readiness assessment at

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