The ROI of AI – Techstrong AI Podcast EP36

Hello and welcome to the Techstrong AI Podcast. I'm Amanda Ani and I'm very excited to be here today with Annika Gupta. She is the Chief Product Officer at Rubrik, and she's also a lecturer at the Stanford Graduate School of Business.

How are you doing today? I'm doing great. Thanks for having me.

Happy to have you on the show. So can you first share a little bit about your company and what services are provided? Yeah, absolutely.

So Rubrik is a data security company. We focus on helping organizations bounce back after they've been hit with the cyber attack and ensure that they can recover their data and applications and get up and running with minimal business downtime. Okay, wonderful.

Well, that leads us into our topic for today, which is how technology executives can navigate the ROI of ai, which has come on the market very rapidly over the last couple of years. We've seen it really advancing that causes some issues when it comes to cybersecurity as well. There's more to think about.

So, um, so let's start with that. First of all, what are some of the additional concerns business leaders have now that there is AI to think about? I think there are a few business challenges around ai.

One is really figuring out how to leverage AI to actually deliver top line or bottom line results for the business. AI is a technology, it doesn't just automatically deliver results. So how do you put the pieces together of this technology and really make a difference in productivity for your organization?

So that's just, you know, on how do you get value from the technology itself. I think the second thing that's a big challenge is how to make sure that you're not opening your business up to more risk by leveraging ai. And that risk could be security risk because, um, you're potentially, um, exposing data in a different way that may cause problems for your organization.

Um, privacy risk. There's a tons of tons of different risks that organizations have to think of as they're putting together these technologies and as they're actually implementing use cases for their organization. So what is step one, as business leaders are looking at some of these changes, what is the very first thing they should consider?

I think the first thing to consider is where where can AI be used to best, uh, best benefit the organization? Um, so looking at things that are routine tasks in the organization, looking at places where you have a lot of people that are, are doing work that could potentially be automated in some way or augmented. Um, a good example for a lot of organizations where they've started is their call centers and really helping their call center employees really figure out how to shorten the time it takes to resolve, uh, calls that are coming in or, um, or figuring out ways to triage calls better, um, based on, um, BA based on issues.

So that's a place where people have seen immediate ROI and that the time to resolve issues has gone way down and they've been able to augment their call center reps using technology to make them better and more productive. Um, so I think that's always a good place to start is figuring out what do you actually wanna do? What is the benefit that you wanna get out of it?

And then after that you have to figure out how am I going to implement this technology in a secure way? What are the kinds of data, um, streams, data assets that I need to actually make this a a useful use case for my organization? And how do I do things like make sure that, you know, I'm scrubbing these data assets for sensitive data so that I'm not necessarily exposing confidential or sensitive data, um, internally to employees that shouldn't have it.

Um, how do I make sure that this data is all kept up to date? There's, there's a lot of challenges, um, that poses in terms of actually architecting these solutions in a secure manner. Do you have any tips in regard to that?

Uh, yeah, I I think that the good place to start is there's a lot of ways that AI can be used. There's a lot of challenges around security and quality on the input side of thinking about what data you're putting in. And on the output side, I think the input side is way easier to control because you're not gonna necessarily be able to control exactly how LLMs answer your questions or exactly what the, the quality of that output is going to be.

But what you can control is what goes in. So I think a great place to start is to really select down the data that you want to use for the use case. So for instance, if you are trying to help your customer support or call center teams do things better, what are the knowledge bases that you wanna pull in?

How do you make sure that you are just taking the most up-to-date data and not pulling in a lot of old data that may be irrelevant because that could impact the quality. And then what are the ways in which you can actually take those data assets and scrub out confidently sensitive data or confidential data so that that doesn't make it into the input side of the equation. Um, once you have really clean data on the input side, then I think there's a lot more confidence on the output that first of all, you're gonna get high quality answers and secondly, you're not gonna end up exposing data to people that shouldn't have access to that sensitive data.

Is there some education that might be needed within companies, uh, some employee training that could help them and uh, what do you suggest? Yeah, I definitely think that there's training that could be helpful. I think on the general employee side, a lot of organizations have put together AI policies that essentially are saying like, Hey, here are the acceptable uses of ai.

Here are the kinds of questions and, and things you can ask. Here's the data and assets that you should not be inputting into an AI product. I think that kind of structure can really help protect organizations, um, especially, um, uh, with, in regards to unintended data exposure.

Um, I also think that uh, or like on the more technical side of the organization, uh, whether it's security, it r and d really, um, looking at the technologies that are out there that can augment, um, that can help you put together these data assets in a secure way. Um, and thinking about the processes in detail and really figuring out what you like, how you're gonna architect your solutions. And also evaluating like a lot of people are putting, using like the AI add-ons for teams or for Zoom or for their other application productivity applications.

And just like having a process to carefully review what you are gonna be using that for and how the vendors have architected these solutions just to make sure that they comply with the policies that you have and what your expectations are. So implementing any new technology and, and definitely AI is one of them can get expensive. So how, so how can business leaders, um, address the cost 'cause they're looking for that return on investment.

Um, how do they be best address the cost and how can they, um, best track their return on investment To Really a really good question. Uh, because what we're seeing is that a lot of providers out there of different kinds of productivity solutions are adding on their AI modules and charging significantly more for those capabilities. I think that's a really tough, that puts companies in a really tough position of trying to justify paying a lot more for an app that they're already paying for to increase the productivity of their organization.

So I think what business leaders really need to do is like go back to first principles and really look at what is the outcome business outcome that they're trying to generate? Are they're trying to generate bottom line results? Are they trying to figure out, are they trying to, um, improve top line results?

Like what is, what are they really trying to do and in what part of their organization? And then really looking at, do I need to buy an external solution that might be pretty expensive, or is there something that I can put together internally with the tools available to me that might be less expensive? Um, and even when you come to the costs of like making queries into, uh, an LLM, you can also choose your LLM models and choose less expensive models if that's going to give you the results that you need.

Not everyone needs the most like up-to-date, most expensive large language model. You might be able to take an open source model off the shelf and be able to use that yourself, um, as opposed to paying for an external service. So there's a lot of different options there.

I think the challenge right now is that because there's so many options and, and it's not super clear which options are best for what use cases, a lot of leaders are having to spend a lot of time experimenting and figuring out how to put the pieces together to generate the outcome that they're looking for and do it in a cost effective way. But those tools I like, I'm optimistic, I think these things are gonna change and get a lot better as organizations figure this out. And as they're more blueprints as technology providers figure out how to streamline these workflows for, um, for organizations.

So AI and, and, and there's other technologies besides ai. We're in a technological revolution here, so we're seeing a lot of technologies advance quite rapidly, AI being one of them. What do you see as, um, the future, say a year from now as it relates to the enterprise?

I think what we're seeing right now in the enterprise is that many organizations are still what I would call in the tinkering phase of figuring out and experimenting with where can AI make a big impact in their work and how to put the technologies together and how to put the right guardrails and security functionality. In my hope, and my I'm optimistic about this is that a year from now enterprises have moved from the tinkering phase to truly finding production use cases where they've deployed this technology at scale. Um, and I think it is like I, it it will take, it takes time, but I'm really optimistic that, um, the, the advances that are being made, the ways in which AI is being integrated into existing technology and making that technology better so you don't have to start from scratch, all of those things I think are going to really lead to significant real adoption of AI in in the coming year.

Well, if there was one key takeaway you could leave our audience with today, what would that be? Uh, I think really focus on, use that on coming up with use cases that can be high impact for your organization and ensure that as you're architecting these solutions, you're really taking data security first mindset of figuring out how do you architect from the data source data input level for security first so that you can feel confident in the security of your solutions down the line. Alright, well thank you for coming on our show and sharing your insights with us today.

Thanks, Amanda. I really appreciate it. Yes, and thanks to our audience.

Stay tuned. There's more.