AI Orchestration for Smart Cities and the Enterprise with Robin Braun and Luke Norris

**Sam Charrington** (0:01)
I'd like to thank our friends at HPE for their support of the podcast and their sponsorship of today's episode. HPE helps organizations bring AI into production faster with its AI Factory portfolio of engineered solutions delivered in partnership with NVIDIA. These turnkey private cloud systems give AI and IT teams the tools they need to innovate while simplifying operations and keeping data secure and under control. Through its Unleash AI program, HPE also connects customers with trusted AI partners and validated solutions to speed up adoption and results. To learn more, visit hpe.com/ai.

**Luke Norris** (0:43)
It is just my pet peeve, but I think the chatbot was almost the worst thing to happen to AI, like period. What we're actually finding in mass adoption, whether it's the Town of Aisle or it's Fortune 500s, is back office automation. The normal enterprise looks so similar at the finance area, looks so similar at the HR area, looks so similar at procurement, and generative AI can just knock all of that down, literally almost overnight. You get those ROIs and then you go on to the sexy and new use cases.

**Sam Charrington** (1:28)
All right, everyone, welcome to another episode of The TWIML AI Podcast. I am your host, Sam Charrington. Today, I'm joined by Robin Braun, VP of AI Business Development for Hybrid Cloud at HPE, and Luke Norris, co-founder and CEO at Kamiwaza. Robin's been leading HPE efforts to build out its Unleash AI ecosystem with the goal of enabling partners to take advantage of the company's infrastructure to deliver enterprise AI solutions. Luke's company is one of those partners. Kamiwaza offers an AI orchestration platform that connects enterprise data and systems with LLMs and agents. Before we get going, be sure to take a moment to hit that subscribe button wherever you're listening to today's show. Robin and Luke, welcome to the podcast.

**Robin Braun** (2:14)
Thanks for having us.

**Sam Charrington** (2:16)
I'm really looking forward to digging into our conversation. You've both been working on some really interesting projects together, including a new agentic Smart City deployment in Vail, Colorado that we'll be discussing a little bit later on. Before we dive into the details though, it feels like we're at a little bit of a moment where every company is trying to figure out how to connect AI more deeply into their operations. And the two of you have been right in the middle of this. I'm curious how you're both thinking about this wave of enterprise AI adoption right now. What's the mood in the conversations you're having? Luke, we'll let you jump in first.

**Luke Norris** (2:57)
Well, thanks. And once again, excited to be here.
So I think the beginning of the year, it was an AI mandate, AI washer, AI do anything. I think it was coming down from boards, it was coming down from external pressures. And now I think it's turned very sharply to AI ROI. You have to have this sort of return on investment. The investment dollars are there, and everyone's excited to put them in. But you have to be able to show some actual tangible reason for it. You have to take that baby step, and then you can take larger and larger and larger steps faster and faster. I think we're right at that inflection where people are getting that first baby step done. They're starting to actually see maybe that first ROI, and they're willing to take the next large step.

**Sam Charrington** (3:39)
And Robin, how does that resonate with what you're seeing?

**Robin Braun** (3:41)
I completely agree. I think that there were mandates. However, people also were looking at what could they do that actually had value, not just to do AI for AI's sake, but to be able to create something, you know, to be able to find kind of what is that right first use case that can help them propel forward, not only in that ROI portion, but also in process people learning for the organization and how AI can potentially impact them. So I think to Luke's point, we're at an inflection point where some of that learning now has been done over the past year, and people are looking at how to hit the acceleration button, which I guess for AI is not actually meant to be a joke, but I guess is. It really is looking at how they can get started. And there's all of these fearful numbers out there of like 90% fail and all of this. But I actually don't think that's a problem. It's people need to be able to experiment, people need to be able to try out different things to find that right first use case. And the technology is changing so quickly that things that we may have failed out at two years ago or a year ago now, we can solve in completely different ways. And that's one of the things I think with AI, that the speed is surpassing what kind of the analysis is.