WEBVTT 00:00:00.000 --> 00:00:01.835 What we've seen is the companies 00:00:01.835 --> 00:00:04.129 that are most productive are the ones 00:00:04.129 --> 00:00:05.922 with half as many pilots 00:00:05.922 --> 00:00:08.425 and they end up with twice as big ROI. 00:00:08.425 --> 00:00:09.718 - Lacy, welcome. 00:00:09.718 --> 00:00:13.680 Lacy, despite increased investments in maintenance support, 00:00:13.680 --> 00:00:16.433 mission-ready rates across many 00:00:16.433 --> 00:00:19.394 Western air forces are still below 70%. 00:00:19.394 --> 00:00:21.563 What do you put that disconnect down to? 00:00:21.563 --> 00:00:22.897 - That's a great point. I mean, 00:00:22.897 --> 00:00:24.190 over and above what we're seeing 00:00:24.190 --> 00:00:27.110 is the inputs do not equal outputs. 00:00:27.110 --> 00:00:29.571 And you could say the system is very fragmented, 00:00:29.571 --> 00:00:33.491 it's very reactive, it's not predictive in any sort of way. 00:00:33.491 --> 00:00:35.869 And as a result, where you're putting the money 00:00:35.869 --> 00:00:37.871 is not driving to the output that you want. 00:00:37.871 --> 00:00:40.457 And so what you see is more and more money getting 00:00:40.457 --> 00:00:43.585 funneled into the wrong place with the wrong resources 00:00:43.585 --> 00:00:46.796 and the wrong identification of what the root cause is. 00:00:46.796 --> 00:00:50.884 - What is the solution to the investment mission-ready gap, 00:00:50.884 --> 00:00:52.927 and where does AI play a role in that? 00:00:52.927 --> 00:00:53.762 - Great question. 00:00:53.762 --> 00:00:55.805 So we see companies coming at this 00:00:55.805 --> 00:00:57.265 with lots of different angles 00:00:57.265 --> 00:01:00.310 in terms of the way the AI plugs in. 00:01:00.310 --> 00:01:02.187 In places where they're experiencing 00:01:02.187 --> 00:01:04.856 manpower shortages or labor shortages, 00:01:04.856 --> 00:01:07.817 investments in something like a maintainer copilot, 00:01:07.817 --> 00:01:09.944 which helps bring new maintainers 00:01:09.944 --> 00:01:13.156 up the learning curve faster, improve first-pass yield, 00:01:13.156 --> 00:01:17.702 and be kind of that technical at-your-fingertips source 00:01:17.702 --> 00:01:21.247 has been super useful in terms of improving turnaround time. 00:01:21.247 --> 00:01:24.375 The second place we often see a lot of benefit 00:01:24.375 --> 00:01:27.128 comes out of when you're investing in control towers. 00:01:27.128 --> 00:01:30.340 So a control tower helps you see a fragmented system 00:01:30.340 --> 00:01:32.342 all in one view and know exactly 00:01:32.342 --> 00:01:35.053 where your bottlenecks are and where to invest. 00:01:35.053 --> 00:01:37.222 And then the last place is predictive maintenance. 00:01:37.222 --> 00:01:39.224 Now this has been around for a while 00:01:39.224 --> 00:01:40.809 with kind of mixed results. 00:01:40.809 --> 00:01:42.268 What we're starting to see now, though, 00:01:42.268 --> 00:01:44.687 is a better way to bring it into your maintenance program 00:01:44.687 --> 00:01:46.106 so that you're more proactive, 00:01:46.106 --> 00:01:48.233 you're taking demand out of the system, 00:01:48.233 --> 00:01:50.860 and it allows you to have less need 00:01:50.860 --> 00:01:53.822 to invest all those resources to get the same results. 00:01:53.822 --> 00:01:55.865 - Where should firms be prioritizing 00:01:55.865 --> 00:01:57.784 their AI investment, though? 00:01:57.784 --> 00:02:01.121 - So when we talk with firms, we like to start 00:02:01.121 --> 00:02:03.623 with where their top strategic problem is 00:02:03.623 --> 00:02:05.500 versus where their AI problem is. 00:02:05.500 --> 00:02:08.128 What we find is that a lot of the investment 00:02:08.128 --> 00:02:11.422 goes to science projects and fun things. 00:02:11.422 --> 00:02:14.008 What we strongly recommend is understand 00:02:14.008 --> 00:02:16.845 what the root cause is of what's driving your performance 00:02:16.845 --> 00:02:19.389 and tailor the AI solution to that. 00:02:19.389 --> 00:02:20.932 - Lacy, thank you so much for your time. 00:02:20.932 --> 00:02:22.350 - Thank you.