Why Data‑Driven Efforts Stall in Fragmented Environments - with Jason Loomis of Freshworks

**Daniel Faggella** (0:12)
Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Jason Loomis, Chief Information Security Officer at Freshworks. Our conversation today addresses the central tension enterprise leaders face when adopting AI at speed, and that is how to move fast enough to remain competitive without compromising security or data governance. He outlines a sequenced approach, starting with regulatory compliance, building toward data trust, and extending into AI specific security frameworks as the most practical path for organizations at any scale. A quick note for our audience that the views and opinions expressed by Jason Loomis on today's program are his own, and do not reflect those of Freshworks or its leadership. Getting in front of enterprise AI buyers is not about impressions, it's about trust. At Emerge, we help AI vendors engage decision makers through research driven content and conversations that matter in the buying process. To learn the exact strategies we use to help leading AI brands and startups connect with their ideal enterprise AI buyers, visit emerge.com/ad1. That's emerj.com/ad1.
Now the conversation with Jason.
Jason, welcome to The AI in Business Podcast.

**Jason Loomis** (1:53)
Thanks for having me. I'm excited to be here.

**Daniel Faggella** (1:55)
Absolutely, I'm excited to pick your brain today. I know that you are a no unnecessary hype, no sugarcoating expert. And I guess that should be the starting point of our conversation today, is to get to the bottom of challenges that we see. And I think we see organizations currently scale their use of data and AI in all departments. And we've kind of assumed in the beginning that in most cases, everything will just magically sync up and it will work perfectly. But we know that's not the case. So what challenges stand out to you in how teams access, interpret, and rely on the information?

**Jason Loomis** (2:31)
Within the context of the most overused acronym today is AI, artificial intelligence. I think it's the speed and maybe it's maybe just old.
Maybe this is like where your parents would say, back in my day, we never get this. But I've been around for cloud transformation. I was around for open source transformation, all these different transformations in technology. It just feels and I feel this is valid, that it's moving lightning fast compared to previous digital transformations that I've experienced. So I think the hardest challenge is the speed at which it's moving, which either could be benefits and risks. I tend to be more on the risk side. I think there's more risks than there are benefits to it, but I think that's the biggest challenge right now.

**Daniel Faggella** (3:15)
When we say challenge with speed, is that the speed at which we see things change or is it the speed at which we're adapting to that change?

**Jason Loomis** (3:23)
It's both.
For people that are using AI for productivity, which is the number one thing they want to do, either you're enhancing productivity or, to be honest, a lot of it's job displacement. So productivity of development, for example. It's the speed at which that's incrementing is keeping up with just the ability to keep up with what others are doing in that space as a company. Let's say your company has 100 developers and you're not using AI.
You have to move fast because the other companies are able to use AI to start doing daily releases, maybe hourly releases, or maybe with your developers before AI, you're only doing one a week release or feature requests every month or two months. So that's the first part. Of course, with that speed then, as you're implementing new tools and you want to play catch up because it's survival for your company, if you're not using AI to develop code, then comes with all the risks with that, of moving so fast and not being safe or trust or secure. It's such a... I don't think I've ever been in this situation with such a balance of speed and safety.

**Daniel Faggella** (4:23)
And I guess that's also if we think trust, when you say trust and you say you're more on the risk side or you're more exposed to the risk side, security also becomes an issue. So at what point does the lack of data or the lack of trust in data become a security issue and not just an operational problem?

**Jason Loomis** (4:41)
From day one, immediately. But when you look at risk, if you're a good CISO, you don't just look at cybersecurity risk, it's business risk. So for a lot of companies, the business risk far outweighs cybersecurity. And maybe you're going to have to make some trade-offs just for survival as a company that you may not be as secure as you would like, or secure in an ideal world. So like with cybersecurity, no matter what its scale, if you're the largest company, if you're Amazon, or you're this large company, and you have a security program, you still, even those programs can't do everything. You have to balance the trade-offs. You have to say, we can't do everything, let's prioritize what we can do.