Dr. Peter Koerte Shares How Siemens is Driving Industrial AI in India

So India has tremendous potential, as you know, with so many young, energetic people. I was very shocked to see that so many of these solutions were actually being built by Indian engineers.

Actually, it's not that hard.

Thank you, thank you for having me. So before getting into the questions, I would like to tell everyone that Peter and I are in Mumbai for a very special event called Siemens Transform Innovation Day 2024. Would you like to tell everyone what exactly is this event about? Yeah, this event is about how we can help the Indian economy to achieve its goals with regards to the growth of 7.5% on GDP and how to build a manufacturing base, increasing the share from 15% to 25%. And as you know, at Siemens we provide all the technology to make sustainable infrastructure, to build state-of-the-art digital native factories. So very, very excited about discussing with Indian customers what it takes to get to that next level.

Yes, we first off focus on two different things. Number one is on larger companies where we obviously work very much hand in hand in a direct sales motion. But then also with regards to the SMEs that you mentioned, India has 60 million small to medium-sized enterprises. We need to find a good way of how to interact with them. That's why we launched two years ago at the Innovation Day in India the Siemens Accelerator, which is a curated portfolio that connects very easily between hardware and software, that brings in an ecosystem of many different partners on the marketplace that everybody can go to and find the elements that they like to see. Now you're right that every business in India has somewhat a different need. They produce different things, they have a different setup, they have different tools, methods, knowledge, and all of that sort. However, what we find is there are use cases that are there over and over and over again. Let me give you three. One is the machines, it's about predictive maintenance and making sure that the machines are having their uptime. And irrespective if you are a producer of contact lenses or if you produce some food and beverage, it's always the same problem that we try to solve. So we provide the basic tools to them in order to solve uptime. The second one is about energy and energy efficiency, it's about sustainability and reducing your CO2 footprint. So we see that use case over and over again. And the last one right now is how can we lower the hurdle in terms of technology adoption, which is really low-code environments where people very flexibly can put things together, because sometimes the skill level is not there. We have a local platform which we can provide to anyone and they can switch it on and then start coding and develop their applications.

Yes, it is both actually. It is an ecosystem, however that's on the supply side. So the demand side comes really on the marketplace. Yes, so you go there. Usually you are a manufacturer of a certain industry, let's say again the example of food and beverage. So you may, of course doing your search engine, you say, 'You know what, I have an issue with regards to energy efficiency in dairy and whatever dairy production processes is required.' And then you go, you Google it, or you search it, whatever search engine you use, you end up on the marketplace at Siemens. You find it already tailored to your needs for food and beverage, where you find the seven to eight major common use cases and challenges where then Siemens and its partners provide an easy-to-use application to make that work.

Yeah, sure. So first off, what's the difference between AI, consumer AI as you picked it, and industrial AI? The number one difference is that if AI in assisting writing you an email gets the tone wrong, there's a little bit of harm done, but you can figure it out and correct it easily. If the AI gets it wrong when I, for example, produce any kind of dairy or any kind of cars or whatever, the harm can be outright detrimental. So therefore the level of precision that is required in the industrial world is much higher. So for us, industrial AI has three components: it needs to be safe, it needs to be reliable, and it needs to be trustworthy. And that's what industrial customers usually ask you to do, and that's what we provide to them. So in our case, industrial AI, to give you an example, is where usually there's a lot of repetitive tasks that you can teach the machine to do on behalf of us humans. There's one particular operation that is called pick and place. So if you, for example in packaging, have to take many different elements, assemble them, put them into a box, wrap the box, and then ship the box, and you do this the whole day, I tell you, if you do that it's very hard on your mental health. The machine can do this much better. And so this is very broad, what we call a pre-trained AI, which can pretty much already now the cobot can take this, already sorted, and put the bin picking and put it down there. And that is fantastic because that frees up yet again our creativity as a human to solve other problems.