Augury’s Artem Kroupenev on AI’s manufacturing potential

Artem Kroupenev, Augury’s Vice President of Strategy, discusses how AI can solve the world’s problems, the current limitations of AI models and what’s next

Artem Kroupenev is the Vice President of Strategy at Augury, where he oversees what’s next for Augury's Production Health AI solutions and ecosystem. He’s passionate about building advanced technology-driven products that drive big leaps in value for customers and help transform industries.

“The manufacturing industry is behind almost everything that’s useful in our lives, and now it’s on the cusp of unprecedented growth,” he says. “I’m passionate about the people who work in this industry as well as the limitless opportunities to help them work in significantly better and smarter ways.”

Augury is a pioneer of industrial-grade AI solutions focused on unlocking Production Health, which provides over 100 of the world's largest manufacturing and industrial companies with insights into the health of machines, processes and operations, to transform how people work and what they can create. 

“Our Machine Health solution combines AI and IoT with human expertise to offer precise predictions that are 99.9%+ accurate, so maintenance people can fix critical equipment long before it fails.”

Here, Kroupenev tells us more.


Can AI solve all of humanity’s problems?

“AI is a powerful technology that has the potential to solve a lot of the world’s problems - but it will not solve every problem. Playing into this narrative often leads to unmet expectations and disappointed users. For the manufacturing industry specifically, though, AI is already driving major benefits in many areas of business - like helping to reduce production downtime, improve process efficiency, maximise yield, achieve sustainability goals, upskill workers, and so much more. Today’s AI solutions help people perform tasks at unprecedented scale, and enable much better decision making in key areas of expertise. Where we’re headed with AI in the future however, is having a co-pilot or a partner of sorts that can access and understand deep and wide topics across the industry and help people in manufacturing achieve their objectives in a much more informed and autonomous manner.”


Tell us about the current limitations of AI models.

“Accuracy of AI is especially important in manufacturing, as errors can be disastrous, but insights that are too generic quickly create overhead and erode trust for the team. A great number of solutions out there will collect and send low quality data, which can critically limit the ability to provide timely and accurate diagnostics. 

“The other key limitation is ability to deploy at scale and bridge the trust gap in a simple and replicable manner. Many AI solutions projects turn out to be highly custom one-off projects that provide little value to the whole enterprise. The ability to quickly roll out an AI solution across 50+ factories, to get people trained and relying on AI as a natural part of their daily work process, is one of the biggest challenges Augury has been able to overcome in the last few years.”

Can AI save the world?

How can manufacturers read through the AI marketing fluff?

“For manufacturers to read through the AI marketing fluff that’s being pitched to them, it’s critical they ask the right questions. What is the quality of the data and the insights that are generated? If you’re being pitched that the solution is accurate and provides value, do you know how exactly that is being measured? How is the AI solution learning from human interaction and expertise, and how does it improve over time?

“Most importantly, has the AI solution been shown to work in your specific environment, and has it been successfully deployed at a comparable enterprise scale?”

How can they understand if an AI solution they’ve been pitched will really solve their problems? 

“Look for AI solutions that solve critical use cases and that can be easily measured on business outcomes. Can the solution drive down the cost or improve efficiency of at least one critical task by 10-100x+ (for example, Machine Health increases the scalability of predictive maintenance by a factor of at least 100).

“Or, can the economic value of making this task exponentially more efficient provide a significant ROI (for example the downtime, asset care and operational costs avoided with Machine Health provide a 3-10X ROI).”

What do the next 12 months hold for you and the company?

“We will continue to redefine our Machine Health solutions to increase our machine coverage, release new sensing capabilities, enhance our autonomous process insight and push the boundaries of our insurance - backed AI diagnostic guarantee to cover production losses. We're also working on new and exciting ways to integrate AI into our customers' production environment, stay tuned!”


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