Scaling Autonomous IT: The Real Enterprise Impact with Digitate ignio

My name is Rahi Naan. I'm the Vice President and general manager for Digitate. And I'm going to talk about today how to scale autonomous IT and the Genai platform in a real life situation.

Uh, I'll just speak into five stories to give you a sense of how it's getting deployed and what are the production outcomes, the value which the customers have realized. Uh, before I do that, I'll just give you a sense of the, uh, business we are in. We started 10 years ago, uh, ground up, uh, based out of Santa Clara here.

Uh, if you see almost half of my 250 customer base has given responses to their uses of this platform. Uh, almost 120 of them. 4 out of five one G two.

Uh, it's, as I said, ground up. There are 110 patents on this platform across, uh, different technologies and a very high ROI, which we'll talk about in some of the examples. Uh, as you can see, our journey and our focus is relentless on autonomous enterprise.

Why that focus? Because we believe in the enterprise today, 70 to 80% of the work is repetitive or with the TA knowledge of a lot of people that can be decomposed into a new world technology like agents. And that can solve a lot of problems and free up people to do high end work.

And lot of cus uh, customers, what we experienced today in our, uh, customer base, they're either on the assisted or augmented. That's where they're playing today about North Star remains to make it autonomous. And we'll talk about some examples there.

Why is a market size puzzle? Because why in this business, if you see some of the Gartner data, uh, this market, what we play with, and we have listed some of the product lines, what we offer to the market today, almost $31 billion market. So this state could potentially be a multi-billion dollar business if we play our game well.

And if we start working with the customer and they start realizing the value, it's a much faster scale, uh, across the market motions. We are global. Our customers are in every continent, every industry, uh, you can take.

Uh, the biggest for us is of course, uh, retail, pharma industries, uh, banking, financial institutions, manufacturing, uh, uh, and we have lot of stories and customers who talk about it, how they've used the product and what the value they have got out in each the markets. So, uh, coming to the story, which I talked about, uh, five of those stories, uh, if you look at lot of business problems, what you see today inherently happens because of technology. Something fail, uh, as simple as a certificate was not renewed.

And then a problem starts, and I'm sure you guys remember, you know, two years ago there was on one of the high frequency, uh, you know, retail day, one of the large retailer lost a lot of money because something was not working on their website. It's very common. The technology fails and a business problem happens.

So if you look at, you know, for example, uh, uh, the story is from tapestry where somebody orders a nice coach bag for their spouse or for anniversary, it didn't reach. And I'll talk about how you can solve the problem. Another example is if somebody wants to get the prosthetic limb and they want to go for a surgery, but they're not able to get, because the supply chain process for this prosthetic limb is not able to provide a definitive date when the limbs will be available.

Another example is there could be a 49 49 er games here and some retailers do not have enough, uh, products on their self and people are not buying and they're losing the market share and not, they're not willing. So I'll just talk about, uh, some of those examples here on this one tapestry. One interesting one, if you look at tapestry, they have 37 reference across the globe.

They're hiring s they value the customers experience. Uh, typically the order comes through the 37 reference. It goes to order manous system from IBM Sterling.

It goes to some financial system for adjudication and, and, uh, payments. And then the shipping happens in this whole journey. There's lot of issues which happens from the bad job cycle, the master data issue, the promotion issue is not applied.

The size of the bag, the color of the bag, a lot of those data issues and inconsistent can happen across the journey of a order coming to a front to order management system, to financial system, to the logistic system. And we have understood that journey mapped new into that. And today, INU is processing completely, almost a hundred thousand orders across their systems and they're saving multimillion dollars for them because of that proactive nature of a new agents working on those issues to solve that problem.

And that comes with combination of what we do, the horizontal o and the vertical stack, understanding through the inference what we get from the data, which comes from their monitoring tool in their environment. And we understand how those proactive nature of the problem happens, fix it much faster if it has already happened or try to prevent it from happening at the first place. And that's the journey which we have taken for them.

Uh, you know, we are going into as simple as when something has happened, we have deployed for them. Where as a consumer, if my, I'm expecting a product to be late, I call them and there are a lot of issues the customer service agents gets, ignor will read the sentiment of that email and it can bump up the priority of that email to the customer service agent and make a immediate phone call. And that's the level of service you're able to provide, uh, through the use of, uh, our agents in the system.

The other example is a large pharma company. Their problem was they've acquired several businesses over the years. They have lot of solutions and the supply chain process, their demand planning process for protic limb goes through different systems.

They have 70 different interfaces. So the data moves. Typically if the interfaces doesn't work right, the data doesn't move.

You cannot do the demand planning or for a surgery to happen. You need a surgeon, you need a hospital, you need a patient, but you need that limb. And if that limb is not confirmed to the hospital, the surgery cannot happen.

And that's what we have reduced from weeks, two hours because we are able to monitor those 70 different interfaces to ensure that the system works fine, the data flows through, and the demand planning process could be completed. The third example is very interesting. Very large company, global brand.

Uh, they again go to direct store delivery model. They sell lot of, uh, day-to-day consumer products, which we consume. And typically the order flows through in the very high frequency and high volume, almost $20 billion worth of products they sell and fulfill to the stores in the United States itself.

And the journey goes through from the order which the route agent will take in the store. They put it in some systems that goes through the several SAP systems internally, then goes to the their manufacturing plants, which are very localized in nature and goes to the shipping on the tracks. In that whole journey process, there are lot of issue.

One simple example could be creation of manifest. If that stops, the track cannot move. So we have understood the whole journey and deployed Ignio in the whole value stream of how that fulfillment happens, right from the order taking to the movement of the trucks to the store.

And that's how we are removing the issue in last, I would say six months. We have avoided 12,000 orders, which is worth multimillion dollars for them, which could not have reached to the shelf. And you can imagine in a retail environment, that's a very highly competitive environment.

So that's the value we have created for these examples. And these three are just a simple use cases. We have done much more than that in these kind of environments.

If there's any question, I'll move to another example. We're running a little low on time, so I think we're gonna avoid the questions. Sure, yeah.

So, uh, this is more about a very enterprise scale transformation for a large distribution company. Uh, they're dealing in the pharma, uh, goods distribution. Uh, if you look at, we have deployed across their cloud, their infrastructure, their applications.

Uh, the complete, uh, even management layer from the event perspective is managed by new today, uh, almost, uh, more than 20,000 cis, almost 220 applications, very critical applications for them. IO is working at almost 80% noise reduction. That what it means at the command center.

The team is not looking for needle in the haystack. That is done by the io even management agent. We do lot of automation.

Almost 110,000 hours of the work, which the humans used to do has been being done by the new agents today. Uh, very critical business function. What they do in the distribution center, they almost process a billion dollar worth of products on through their distribution system every day.

It's very critical and heart of their operations. So on their shipping cycle, they have almost 500 issues every day because something would break from the technology perspective. So we have deployed igloo on that process to start with so that those 500 issues, which was taking maybe two hours to resolve, it's getting resolved in less than five hours.

And those are very critical FDA regulated process where you need validation at each step of the process. And that's the machine is doing today. Uh, of course, uh, there are a lot of automated ticket creation, which happens in that environment, almost two 20,000 every year, which seamlessly new agents are doing today.

And you know, the idea, the intent is that more and more agents can cut through the work which the humans are doing to avoid the error, remove the delays, and if at all something has happened, make the issue feel like a blip. That's what we are trying to do in this enterprise. Uh, another example, very large retailer, uh, again, uh, they're uh, front store retailer and a pharma retail company.

9,000 stores. Well, I'll just take one example of that, cemental mine Revenue assurance. Typically, if you walk into a store and if you scan a product, and if you did get, uh, a promotion applied or a price applied behind the scene, what happens?

Those store clerks either can give you a one $99 because they know what the price of the product is. You can pick up or you can drop the product and walk outta the store. Both are not a good situation.

But post that, the store clerks goes at the back office, create a ticket and says, this product does not have an item or a price or a promotion. That takes almost 15 minutes for them to create. They go back, that ticket goes back to the corporate systems, passes through the merchandising team, the SAP team, the business finance team, the pricing team, the bad jobs overnight, and then the next approval comes and the bad cycle the next day possibly that price and promotion is applied to the PO system in that store.

That's the journey which goes through, it takes almost 25 to 30 minutes just in the individual time, which it takes in that process that all work has been now onboarded to Ignio. What we realized that the loss of the revenue because of this problem in that environment was $17 million. And the effort, which they were putting as a team for 4,500 tickets every week was $5 million every year.

So one use case, we have elevated the revenue by $17 million and reduced their cost by $5 million. So that, there are a lot of other examples which I'll talk about from the MTTR perspective, which you see they have 30 x improvement. 81 days now with ignorance less than 10 minutes.

A lot of customer experience, which has been improved, I think. Uh, just last two slides in the interest of time, this is the scale and complexity we are managing today. Uh, of course agents and agent platforms are very relevant in today's context.

We have been doing this for last several years, almost. I will give you a data of last year, almost 300 million automated actions our platform has taken. 2 billion events at 87% NI reduction.

What it means for our customers that this much of volume of work is not being done by humans. It's being done by ign new agents today, of course, uh, SAP is a big, and IOP processing is a big element in any enterprise. When you see how the, uh, SAP works and reprocessing and using that ability to solve that problem gives a lot of business benefit to the customers.

And finally, as I said, autonomous is the operating model, which we believe and we are relentless focused about that journey to ensure that we eliminate so that the business disruption doesn't happen because of it, or we can ensure that it finds before a business finds that problem. Or if the problem has happened, we can self it super fast that the situation looks like a blip.