Motorola Solutions, Inc. New Piper Sandler 5th Annual Growth Frontiers Conference

Thank you for having me.

I would say things are looking reasonably good on the state and local level. I think Jack Molloy articulates it the best, saying that state and local budgets are continuing to be strong. Everything that we provide is a need to have more than a nice to have, so we get prioritization within that budget framework as well. On the federal side, we're seeing good traction. I think we are not seeing an issue there.

Yeah. And maybe, Jim, if you allow me.

Motorola didn't enter the AI space this year or last year or even the year before. We've been in this space for a very long time. My entire career, as an example, has been really focused on machine learning and AI applications in security and in public safety. I came into Motorola through the acquisition of Avigilon. And the way Avigilon really took market share from its competitors and grew actively is applying computer vision machine learning techniques right into our video management systems, our cameras, et cetera. And one of the lessons that we learnt in that process was that having AI capabilities as a sidecar or an over-the-top application that helps users outside their normal course of their workflows initially makes for a good proof of concept but doesn't have that sustainable legs to it where it becomes the thing that gets actively adopted.

The tack we took at Avigilon that really led to our success early on is embedding a lot of these capabilities within our core applications. It is that same strategy and tactic we have taken into the public safety space as well. As we think about our command center applications, be it 911, VESTA, computer-aided dispatch, CAD, or even console applications across the board, that's the dispatcher suite that you were referring to. The more we thought about it, having stuff like transcription or translation capabilities be something that's an adjunct to the core workflows really didn't serve our users well. We redesigned our core applications. VESTA NXT, which we launched last year, has AI capabilities natively built into it, native capabilities that actually facilitate transcription in real-time as the user is, the call taker is having a conversation with the caller.

Automatically recognizing that perhaps a call taker cannot understand the language that the caller is calling, and being able to switch to a translated form immediately without having to wait for an interpreter to join the 911 call line. Being able to fill out forms, summarize, send that summary to an APX NEXT device with a responder in the field. Being able to communicate all those capabilities seamlessly behind the scenes, I think that is really where we see the embedded capacity of AI. 100% of the 911 orders that came in in the past few quarters have gone with our AI capabilities. And literally, because it's embedded, customers choose a tier, the AI tier of our solution. That's the assist tier. That's the highest tier of our solution.

As a consequence, we have seen an ASP uplift across each one of our workflow applications because they are all choosing the assist tier. What the dispatcher suite and the responder suite allows us to do beyond the adoption of those individual applications is that when someone already has bought into VESTA, adds CAD to their portfolio, and wants to be able to add assist capabilities into their CAD solution, this just makes it an easier purchase decision because it's available as a suite.

Yeah. I would say it's less of a network challenge. It's a user experience challenge. It's an opportunity to how we can leverage data more effectively. Let me just maybe talk briefly about the data portion of the story. We launched SVX last year. One of the key things that SVX enables, along with the CommandCentral DEMS platform, Digital Evidence Management platform in the background, and also our records platform, is the ability for officers to author narratives in an AI-assisted manner.

One of the issues that we saw very early on as a challenge for officers is that as they use their body-worn camera to capture what they see and they hear, a good chunk of information gets missed out because they're using an earpiece attached to their radio and having conversations and listening to conversations via the earpiece, which the body-worn camera never captures. So the officer is not actually capturing everything that he or she hears or sees within that body-worn camera recorded audio. By integrating all those pieces of information, audio both that's recorded by the body-worn camera, but also by the LMR audio streams that are captured in the background, and by the way, 911 audio, which also becomes relevant.