Why Manufacturing's Most Valuable Data Isn't in Any System — with Anand Gnanamoorthy of Ingersoll Rand

**Daniel Faggella** (0:13)
Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Anand Gnanamoorthy, Director of Corporate Strategy and AI at Ingersoll Rand. Anand walks us through why a significant share of manufacturing knowledge still lives inside experienced workers, and why capturing it has become urgent as long-to-net employees retire and operations shift toward AI-enabled ways of working. He separates the three data layers that manufacturers already have. Structured operational data, decades of unstructured archives, and the tribal knowledge held by frontline staff. And explains why those unstructured archives sitting in employee drives and folders are the biggest untapped source of value most companies overlook. Today's episode is sponsored by Poka. Please note the opinions and views shared by Anand in this episode are his own, and do not reflect those of Ingersoll Rand or its leadership. Position your brand alongside the Fortune 500 leaders defining the Enterprise AI roadmap. For the opportunity to showcase your solution to the executives currently funding and scaling global initiatives, partner with Emerge to reach the decision makers holding the strategic mandate. Secure your partnership at go.emerge.com/partner. That's go.emerj.com/p-a-r-t-n-e-r.
Now the conversation with Anand.
Anand, thank you for joining me for an interesting discussion today.

**Anand Gnanamoorthy** (1:44)
Thank you, happy to be here with you.

**Daniel Faggella** (1:46)
Absolutely, I'm sure you'll agree with me that a significant share of manufacturing knowledge does not live in systems anymore. It lives in people, in the heads of experienced workers who know which machine runs hot, then which process needs a workaround, which shortcut is safe, which one is not. And as those workers retire and operations push towards AI-enabled ways of working, the question of how to capture and transfer knowledge is becoming one that needs some urgency in the industry. And that's where I want us to start today. So from your work in manufacturing strategy and AI adoption, where do you see the biggest challenge when companies try to prepare frontline teams for more digital ways of working?

**Anand Gnanamoorthy** (2:28)
Sure. I think it's a multi-dimensional problem. You cannot classify, hey, this is one single problem and you can answer it pretty simple way.
You have to look at several different, often conflicting set of ideas.
For example, one dimension, obviously, as you mentioned, the knowledge resides in the people. If you look at our current systems, the current systems have data in them, the knowledge resides in the people, and it's typically the managers who would be making the decision. So that's how our current systems are defined. When you're moving from our current digital systems to AI systems, where in AI is not that great with data, but it's very good with insights and also who has to make the decision. So if you look at it as a data insights and a decision problem, you can see it in a different angle. And if you look at all the three dimensions, so for example, if you take data, currently data resides in so many different places. And so companies have this extensive data stack to collect data, collect data into a single space, derive insights from that, share it within their network to make the decision. So it has such a high levels of automation involved in that. There are so many different layers in that. Now, if you're going from that system to another system with AI, which can look large volumes of data, can deliver, can develop insights immediately, and then you can make decisions based on top of that, the workflow needs to be adopted for that. What I mean by that is, currently, decisions and insights are developed by the humans. Now, when people are moving towards AI, they are kind of confused between, hey, should I let the AI make the decision? Which of those decisions should I keep? Which of those decisions should AI make? That is the biggest challenge right now that I see in the market. And if you're not able to define that properly, that's where the pilots fail. So a lot of people are doing pilots. They would do, one example is, you know, the simplest example of AI being used right now is email automation wherein you have an email coming in, the AI system would automatically develop a response to it. So what it is doing is it is looking at data, based on the data, it is developing the insight to, hey, this is what the user is asking for as a response to that email. That's pretty simple, but should AI send the email out, or should an human have it review the data, review the email draft and send it out? So that's where the biggest challenge right now is, which are decisions that AI can take, which are decisions that human should take. And many a time we have seen several examples wherein it was done poorly and people have gotten into issues. For example, companies use AI for their chatbots. And we have had several examples wherein user would be asking it questions.