Could you explain how your product is applied in the enterprise business process?

Well, we are building a platform for enterprises that enables them to run on much smaller datasets, making it easier to integrate AI solutions into their business practices. We extract a high-level representation of different companies’ concepts of data sets and use that as a starting point. Right now, we’re debating and formulating touch points, so the process is dynamic and needs to be standardized.

What use cases does it aim to tackle?

I can list among our use cases: manufacturing projects (improving the quality of the process of a chemical plant; applying deep learning to the fine-tuning of a chemical process); leveraging IoT; supply chain; AI as service partnerships with financial institutions.

We are growing the platform with applications for each domain, translating the advantages that we created at lower-level growth of big applications. We are targeting the ability of AI to scale everywhere to reach Fortune 2000 organizations.

What do enterprises not understand about AI technology?

AI technology is still in the educational and implementation stage of an actual solution. For a lot of businesses, it’s difficult to distinguish a difference between rule-based systems and real AI systems. On one end of the spectrum: if they don’t use AI properly, it creates friction, so there are no expectations. Another group might think it’s magic: They say, “Oh, yay! Let’s just do this through AI.” It doesn’t work that way either.

The market has just started to understand how this works. The education process continues, not just in the technical aspect, but more in terms of what it all means: interacting with different types of systems, harnessing people’s strengths, learning what to avoid, and learning what the downsides to training are.

How do you convince enterprises to adopt AI technology?

Well, sometimes they experience fear of missing out. We show them a chart from the McKinsey report called ‘Artificial intelligence: The Next Digital Frontier’, the achievements of different industries, and who has adopted AI and what they gained. They see a curve trend, and those who adopted AI are leading in the chart. We are trying to be specific, provide sample data, proof of concept, and actually show the real numbers.

What are the risks of AI technology?

In my opinion, it’s unmonitored fragility and there’s a need to introduce critical processes because AI is stupid and narrow.

Also, I think bias in AI is a risk and there’s a lack of transparency. I’m afraid we’ll screw up this very powerful technology in the wrong way. It feels like launching rockets into space with no direction. The consequences of unwise AI technology implementation could be big system failures: Big companies could be hurt.

But as much as it is bad, it can also be very good. When you automate certain processes that are critical for your business, you expect them to run. It’s very risky to end up in the situation where you create weak points and you have to monitor them. So it’s all hitched around putting the proper processes in place, watching what AI is doing, and scaling.