00:19:36 · David Flickinger, Mikk
But if you had connected it into the systems, into the CRMs with thousands and thousands of customer records, into the supplier portals that has up to date pricing, into your labor and payroll data. So you know if you're putting a seven man crew on this job, you know what that's going to cost with a high degree of predictability. It can give you a very good answer. And so in short, it can give you these answers if it's connected into the systems. And that is where we really spend the majority of our focus in an engagement is first and foremost connecting an agent into your systems so that it has the context so that it can start to iterate and learn. [00:20:26] S0: So as a business owner operator, from which moment will you be sure about this this work the AI was just doing when it comes to this particular offer? From which moment are you so sure that you are willing to send this to a client and making sure that it's it's all correct? Because people make mistakes. I guess AI is making mistakes. And I've been flying recently quite a lot, and, like, I I like to read, like, planes and things, and they have those pilots double checking, triple check... Checking things like this. How do you make sure that this... What you just sent to a customer is is correct and you can you can actually sleep well on it?
00:21:05 · David Flickinger
[00:21:05] S1: So we design a workflow that has human checkpoints at key instances along the way. And that system, the goal would be that you, over time, remove those human checkpoints as you validate them. So you start with a lot of human checkpoints is the answer. You don't wanna have AI going and emailing proposals to customers without you having checked it out, of course. And you wanna design the system such that you feel very confident at each step along the way that you are either approving the next step, the next phase, or you've approved it enough times, you're confident that you only need to approve it here. But even in those instances where you say, hey. Full approval. Actually, go email the customer. Do the proposal and email it. Let's say you're there. We don't have any clients that are just doing that yet, by the way. We do have clients who are reviewing a final proposal in their draft emails, clicking the PDF and saying, well, that looks good. Send. So, of course, they could. But what it will do is identify anomalies and red flags as well. So if there's a confidence interval below a certain threshold, it will say, this is ready for your review, but please let me point you to here, here, and here because this is worthy of additional investigation. This is an anomaly. I haven't seen this before. And so the goal is not to take the human out of the loop for these critical things. The goal is to make them more efficient and hopefully direct their attention to things humans are best at. I think ultimately that's one of the promises of AI more broadly stepping out and zooming out of what we done is directing the human's attention to things we are best at and letting AI do things it is best at, whether that's repetitive tasks, data analysis, data extraction. I got a 30 page contract yesterday. And even if I was an attorney and, of course, forward that to my attorney, I wouldn't even really know where to look to sit down and read a 30 page document. But this is an example anyone can do is you can put that document into ChatGPT and say, hey. Where are the most critical areas I should look? What are the red flags? What seems unusual in this contract? It can direct you.
00:23:34 · David Flickinger
So it's a a micro example of how when you have this architecture embedded in a business at enterprise scale, the AI should be directing the humans to the most critical items, thus allowing them to be more productive, more efficient, and automating work streams that should be automated, but make the human judgment element that much better in those businesses. And that I think is a competitive advantage. If you can make your human element of judgment better because you're directing your humans faster to those areas which need their attention, that's a win using AI. That I think is a great AI implementation. It's not always just let me automate everything. In fact, I think that that could prove to be a mistake. Over time, you see even this silly little thing of automated emails where you clearly get an email. We've all gotten them now. It's totally written by AI. There's not a human touch in it, and you're just like, throw this away. This was this was written by an AI that gave... Whoever sent this gave no individual human thought to what they were sending. So, again, these micro examples we start to see, but I think at a 30,000 foot view, thematically, what we wanna do is, again, start to streamline what can be automated and what should be delivered to a human so that you can run a business better. Because at the end of the day, a lot of these businesses are still people businesses. It's still human to human interaction. And, of course, people and and we'll build systems for these agent to agent interactions. But the human human interaction element, if you can use AI to make that better, I think it's really a tremendous force multiplier.
00:25:27 · Mikk
[00:25:27] S0: Now in the roofing business, what about the junior estimators as the senior ones are so more efficient now? What's gonna happen with the junior ones?
00:25:41 · David Flickinger
[00:25:41] S1: So they get up to speed faster. That's for sure. They can do more complex estimates earlier on than they otherwise would have. And so if you wanna use them in that way, that's great. I could tell you one area where I think we've seen that instance you just described and my prior answer align really well is maybe the junior estimators are able to spend more time with the customer, with the decision maker because they're not spending so many hours as an inexperienced junior estimator in front of their computer screen. They're spending more time with the customer. They're spending more time on a roof. They're spending more time doing the things that help them win jobs because the answer that really was the answer to a math problem has largely been automated. Their review was directed to these specific areas. Now there's a high degree of confidence that they're getting to the right answer. The chief estimator, while he may have had to spend a lot of time reviewing a junior estimator's proposal before he approved it to be sent, now maybe has to spend a minute or two because that person has the confidence as well that this proposal was generated with sound logic. It was using the context of the business, the correct rules, pricing, rates for materials and labor, etcetera, and that the junior estimator was focused in the right areas. And then the senior review of that is is again much more efficient. So I think the promise is twofold. One, that you are just more efficient with the workforce that you have. If you have junior employees, what can they do faster and above and beyond their current skill set with less training and less time in the role? But two, can those junior employees focus again their time and effort on things that win business, win jobs, help customer retention, help improve a customer experience versus spending more time in front of a screen.
00:27:44 · Mikk
[00:27:44] S0: Okay. And last one here, are you now able to make and send more proposals to potential clients as previously for the senior estimator, it took two weeks to create it. Now it's, like, twenty to thirty minutes and, obviously, some additional time to review and double check. So are you now able to just simply send more offers to to win more jobs?