Vollständiges Transkript von Are You Tracking the Wrong Metrics? How AI Can De-Risk Delivery

milestones on time, on budget, with, you know, as little friction and as much, you know, accuracy and predictability as possible, right? 00:04:24.233 --> 00:04:29.514 So all the magic that you need to track are the metrics that are gonna help you get to that state, right? 00:04:29.663 --> 00:04:30.624 It doesn't be anything. 00:04:30.923 --> 00:04:36.533 It can be, you know, how fast tests are moving, where things are blocked, vacation days, how people are using AI. 00:04:36.894 --> 00:04:41.634 You can track anything as long as it keeps you in that box of what you're actually trying to achieve. 00:04:42.233 --> 00:04:46.584 But if you're not tracking the things that are gonna help you reach that you're tracking the wrong things. 00:04:47.004 --> 00:04:51.834 I like that perspective because I think we tend to gravitate towards the right things to measure. 00:04:52.074 --> 00:04:54.624 I even said it in the question like, what is the right thing to measure? 00:04:54.834 --> 00:04:55.824 You're saying actually. 00:04:56.363 --> 00:04:59.514 Whatever the thing is that helps move you forward is the right thing to measure. 00:04:59.574 --> 00:05:02.303 Even if it's way different than what that other team is doing. 00:05:02.303 --> 00:05:04.913 Even if it's way different than what you did at that other organization. 00:05:05.184 --> 00:05:07.254 Like it should be aligned to the mission. 00:05:07.613 --> 00:05:11.423 You need to track whatever is going to help you get to where you wanna go. 00:05:11.934 --> 00:05:13.473 Everything else is, you know, is noise. 00:05:13.954 --> 00:05:14.374 That's fair. 00:05:14.584 --> 00:05:14.944 That's fair. 00:05:14.973 --> 00:05:21.334 So I mean, for folks who are like, oh wow, Lior, that's, you're speaking my language, we are definitely not tracking the right things. 00:05:21.634 --> 00:05:22.293 We need to start. 00:05:22.293 --> 00:05:24.004 How do you kind of map out. 00:05:24.379 --> 00:05:27.439 What the right things are to push you forward? 00:05:27.978 --> 00:05:30.559 Is there a sort of best practice or a process? 00:05:30.949 --> 00:05:32.329 You need to understand your business, right? 00:05:32.329 --> 00:05:33.499 You need to understand your project. 00:05:33.499 --> 00:05:34.999 You understand what's moving the needle? 00:05:35.269 --> 00:05:36.678 What are the movers and shakers? 00:05:37.189 --> 00:05:38.089 What's gonna make an impact? 00:05:38.629 --> 00:05:39.379 Is it people? 00:05:39.379 --> 00:05:40.249 Is it resources? 00:05:40.249 --> 00:05:41.718 Is it complexity? 00:05:42.259 --> 00:05:44.689 How do you sort of unpack everything? 00:05:44.689 --> 00:05:46.279 Say, okay, these are the components. 00:05:46.548 --> 00:05:48.978 This is what needs to happen now, how to attract that. 00:05:48.978 --> 00:05:51.829 It's actually happening and not sort of falling between the cracks. 00:05:53.134 --> 00:05:53.689 Is public. 00:05:53.689 --> 00:05:55.339 It like you're gonna have like lots of tools. 00:05:55.848 --> 00:05:57.139 Like data is gonna be all over the place. 00:05:57.139 --> 00:05:58.069 It's not gonna be updated. 00:05:58.069 --> 00:06:03.060 And you're gonna say, oh well people are not updating their system so it's garbage and garbage out and I can't do it. 00:06:03.060 --> 00:06:05.459 You know, and I'm spending all this time instead showing any returns. 00:06:05.908 --> 00:06:12.718 There's like a million ways and a million reasons why it's not gonna work, but we need it to work, so, so you know, it's on you. 00:06:13.259 --> 00:06:13.439 Okay. 00:06:13.439 --> 00:06:14.279 That makes sense to me. 00:06:14.654 --> 00:06:38.113 I'm wondering, would you be able to just maybe walk us through an example, either of a metric that was maybe the wrong metric and that process of how they changed towards measuring the right thing, or even if there was just some unconventional metric that somebody that you worked with was tracking that actually pushed forward towards the goals, but actually is a pretty non-traditional thing to measure? 00:06:38.714 --> 00:06:41.264 Let me talk about like one of our customers, they're called Versa. 00:06:41.269 --> 00:06:45.723 P. There are a PTF delivering product operations. 00:06:46.055 --> 00:06:46.714 It's called bu. 00:06:47.163 --> 00:06:47.944 He is a brilliant guy. 00:06:48.365 --> 00:06:55.533 We love him, we love working with him, and he wanted to improve, like how they forecast what they're gonna deliver every quarter, right? 00:06:55.533 --> 00:06:59.853 So plan was actual improving the planning accuracy, improving forecasting. 00:07:00.514 --> 00:07:03.004 They were running very fast and things are changing all the time, right? 00:07:03.004 --> 00:07:06.245 So priorities change, capacities change, people move between teams. 00:07:06.603 --> 00:07:08.074 So it becomes really complicated. 00:07:08.555 --> 00:07:22.439 And what he came up with was a formula that helps take the previous performance of every contributor and every team and then add that on to whatever, like the current capacity for that team or that group is gonna be. 00:07:22.920 --> 00:07:28.860 And instead of, you know, having to sort of crunch all that data in Excel, you know, which takes a lot of time. 00:07:28.860 --> 00:07:35.098 You have to bring in you know, data about vacation days and, you know, tasks and priorities and lots of stuff and doing it in Excel. 00:07:35.668 --> 00:07:38.488 So he was willing, you know, to take our platform and automate it. 00:07:38.978 --> 00:07:40.509 And it took him like, like a day. 00:07:40.959 --> 00:07:43.629 But now, whenever he wants, he has like that in front of him. 00:07:43.639 --> 00:07:45.288 Okay, what am I gonna deliver with this month? 00:07:45.588 --> 00:07:50.149 You know, so like the CEO or the CPO comes in and say, Hey, you know, I need to shift priority. 00:07:50.149 --> 00:07:57.738 Something else comes, you know, is most t he has that what's actually going to change

and how it's actually impacting all the initiatives that they're trying to push. 00:07:58.189 --> 00:08:00.488 I thought it was brilliant taking data, you know. 00:08:00.894 --> 00:08:03.834 Putting it in the right context to tell the story that he needs to tell. 00:08:04.163 --> 00:08:05.634 I was just at an Agile conference. 00:08:06.249 --> 00:08:08.228 And I mentioned velocity earlier, right? 00:08:08.228 --> 00:08:10.660 And everyone's yeah, it's like a poor measure of impact. 00:08:10.660 --> 00:08:15.730 And sometimes it's just a poor measure because you know, in a lot of agile teams you're meant to have a dedicated team. 00:08:15.730 --> 00:08:16.959 You know, they're resource full-time. 00:08:16.959 --> 00:08:19.389 And people I talk to, they're like, that never happens. 00:08:19.689 --> 00:08:23.740 People are, you know, to your point, they're going on vacation, they're getting pulled away to other projects. 00:08:23.949 --> 00:08:29.439 So you look at this velocity metric and it's going you know, up and down and you're trying to plan ahead and you're like, what is going on? 00:08:29.439 --> 00:08:31.298 It's it's not a very good metric. 00:08:31.569 --> 00:08:33.460 And I would say most people probably wouldn't. 00:08:33.984 --> 00:08:38.904 They either wouldn't think to have an aggregate metric that is based on dynamic data or they wouldn't know how to build it. 00:08:39.355 --> 00:08:39.654 Right. 00:08:39.654 --> 00:08:41.004 But I think that makes a lot of sense. 00:08:41.244 --> 00:08:41.994 It's almost like. 00:08:42.413 --> 00:08:46.193 This notion of like real time resourcing where it's okay, well this person's gonna be away. 00:08:46.203 --> 00:08:52.173 We should expect this dip in this metric about our output, about our performance, about our impact. 00:08:52.173 --> 00:08:53.104 You know, whatever it is. 00:08:53.104 --> 00:08:59.094 It's actually grabbing data from other places, not just trying to have the one measure, the one metric to rule them all. 00:08:59.094 --> 00:09:01.403 I mean, it does function as that, but it's an aggregate metric. 00:09:01.494 --> 00:09:02.754 Actually, I think that's really cool. 00:09:03.144 --> 00:09:04.553 It's interesting you bring this up, right? 00:09:04.553 --> 00:09:07.433 But think about like how data propagates across the business. 00:09:07.673 --> 00:09:08.604 That person goes away. 00:09:09.144 --> 00:09:11.634 It takes time until people know their way, right? 00:09:11.734 --> 00:09:13.203 If manager's gonna know their way. 00:09:13.443 --> 00:09:27.754 But until that propagates to the right person who has like the understanding of how it's gonna impact the project timelines, and you know, what, you know, secondary or tertiary effect is gonna have, it's gonna take days or weeks until that's time you're not getting back, right? 00:09:27.754 --> 00:09:30.244 You add more resources, you're just gonna extend the timeline. 00:09:30.244 --> 00:09:31.573 It's not like it's gonna make it

shorter. 00:09:31.783 --> 00:09:32.423 You have that learning curve. 00:09:33.073 --> 00:09:39.464 So the faster you're able to get that information across your organization, getting it to the right people, it's massive impact. 00:09:39.464 --> 00:09:40.634 It's compounding interests. 00:09:40.934 --> 00:09:42.039 That lag is like so real. 00:09:43.379 --> 00:09:50.578 In our community, there's always a lot of conversation about well, I wish someone had told me that my lead dev was gonna be going on holiday for three weeks. 00:09:50.908 --> 00:10:05.580 And then even that lag, like I see it in my role all the time where it's like you get the like a DP, like vacation request and you go in there and there's a process and a flow and bottlenecking that happens before anyone even knows that person's vacation is approved. 00:10:05.610 --> 00:10:07.200 A little less what like date that is. 00:10:07.620 --> 00:10:09.059 I like that kind of streams it in. 00:10:09.330 --> 00:10:10.110 Vacation is easy. 00:10:10.110 --> 00:10:13.799 You know, think about, you know, you're working remote, you're on Zoom, and somebody's gone. 00:10:14.279 --> 00:10:15.570 Who's gone, you know, let go. 00:10:15.570 --> 00:10:17.309 Nobody told you to be let go. 00:10:17.490 --> 00:10:17.820 Yeah. 00:10:17.820 --> 00:10:18.000 Okay. 00:10:18.000 --> 00:10:18.389 I see. 00:10:18.509 --> 00:10:20.580 You know, two weeks in, he is not answering my calls. 00:10:20.580 --> 00:10:21.539 I'm like, what's up? 00:10:21.570 --> 00:10:22.529 Oh, he's not here anymore. 00:10:22.769 --> 00:10:23.159 He's not here. 00:10:23.159 --> 00:10:23.399 Yeah. 00:10:23.700 --> 00:10:25.230 We're hearing a lot about that too, in the community. 00:10:25.230 --> 00:10:25.409 Right? 00:10:25.409 --> 00:10:28.379 It's like production's in force without the communication around it. 00:10:28.529 --> 00:10:28.830 Yeah. 00:10:28.830 --> 00:10:29.429 And then what. 00:10:29.789 --> 00:10:34.639 Can you this is a bit of a side quest, but can you at a high level, walk me through what that integration looks like? 00:10:34.639 --> 00:10:46.240 Because I'm picturing myself opening Excel and being like, okay, I need to like Zapier or make, or do some kind of transformation of data and have it like pasted into a sheet and I have to write a formula and all that. 00:10:46.289 --> 00:10:51.229 Would I still have to do that in TargetBoard or is integration a little bit more hands free? 00:10:51.798 --> 00:10:55.399 Hands free, might not be the right word, but less labor intensive. 00:10:55.639 --> 00:10:59.658 So TargetBoard.ai works in a very different way from any other system that exists there. 00:11:00.048 --> 00:11:08.239 We connect to your source data source system and we automatically build all the metrics and all the KPIs that are relevant for whatever you need to do. 00:11:08.479 --> 00:11:08.688 Right. 00:11:08.839 --> 00:11:10.278 And then we use AI to generate them. 00:11:10.714 --> 00:11:16.864 But it doesn't matter if you're looking, you know, at metrics for how your capacity changes, how your velocity changes, how the quality changes. 00:11:17.313 --> 00:11:22.594 If you're looking at one project, multiple projects, doesn't matter how you work, don't have to change anything about how you work. 00:11:22.894 --> 00:11:25.533 We automatically learn how you work your systems. 00:11:25.953 --> 00:11:29.313 It doesn't matter if you're using Monday or if you're using Jira or anything else, right? 00:11:29.583 --> 00:11:37.048 And then all your metrics, all your reports, all your insights are automatically generated and you can then customize. 00:11:37.259 --> 00:11:39.479 It's like DIY customization, or you can do it with a team. 00:11:40.169 --> 00:11:45.179 You'll get the exact metrics and the exact way of tracking everything that you want. 00:11:45.568 --> 00:11:53.938 It's zero effort, like it's like day one, you're up and running fully confident that the data you need is there and it's accurate and you don't need to worry about excels anymore. 00:11:54.328 --> 00:11:58.919 By the way, if you do use Excels for like manual tracking and that's like your source of truth, that's fine. 00:11:58.919 --> 00:11:59.849 We'll connect with Excel. 00:11:59.938 --> 00:12:00.809 We'll pull it in also. 00:12:01.379 --> 00:12:03.839 But you don't need to update anymore, right? 00:12:03.839 --> 00:12:05.568 You don't need to explain how things are. 00:12:06.303 --> 00:12:07.173 How did you measure this? 00:12:07.413 --> 00:12:08.163 What is this based on? 00:12:08.163 --> 00:12:10.203 Like all these questions, they're answered in the platform. 00:12:10.624 --> 00:12:13.173 Like you share it like with your executive with your C level. 00:12:13.533 --> 00:12:19.053 They'll have the answers of how things are measured, where they're coming from, where it's going, what's moving the needle. 00:12:19.384 --> 00:12:25.793 It's not you know, you don't have a situation of, you know, you're sitting in that meeting and you're getting asked a hard question and you don't have the answer. 00:12:25.808 --> 00:12:29.568 You have to say let me get back to you and let me get back to you. 00:12:29.568 --> 00:12:31.038 It is like the worst thing to ever say. 00:12:31.099 --> 00:12:31.719 It's I don't know. 00:12:32.168 --> 00:12:33.369 I'm sorry I didn't come prepared. 00:12:33.369 --> 00:12:33.849 I don't know. 00:12:34.389 --> 00:12:38.964 I didn't study for this test, so it's, although it was like one click and now you like the follow up, is there. 00:12:39.173 --> 00:12:41.514 What I like as well is like the human error component. 00:12:41.514 --> 00:12:43.943 Like I do track a bunch of stuff in a spreadsheet. 00:12:44.214 --> 00:12:55.614 There was an incident where I was tracking page views and Google Analytics for, and someone was putting in users, so we had this discrepancy of we thought we were measuring the same thing, but actually we had two different data sources.

00:12:56.229 --> 00:12:57.369 I was messing it up. 00:12:57.428 --> 00:13:08.969 But anyways, that notion of somebody might have a different interpretation of where that data comes from to measure that metric versus, I like the idea that like, okay, it's pulling directly from a source of truth. 00:13:09.239 --> 00:13:14.489 Oh, and by the way, like it generated some of the metrics using AI based on, you know, what the requirements are. 00:13:15.119 --> 00:13:16.948 To be like, you should probably measure this. 00:13:17.009 --> 00:13:26.999 And that gets sort of defined and integrated because I've been in a lot of conversations where we can't quite decide on what the right metric of success is, partly because we don't know what we can measure, right? 00:13:26.999 --> 00:13:31.948 We're pulling from our deck of cards that only has four options and we're like, okay, which one of these four? 00:13:31.948 --> 00:13:35.129 Whereas it could be a lot of different things To your earlier point. 00:13:35.489 --> 00:13:36.359 It's so common. 00:13:36.448 --> 00:13:42.589 Listen I work at a company and when I came in doing list was tracking like a hundred thousand visits per month. 00:13:42.619 --> 00:13:43.129 Be the big company. 00:13:43.698 --> 00:13:45.528 I said, well, where's this coming from? 00:13:45.528 --> 00:13:46.639 It's, it looks weird. 00:13:47.028 --> 00:13:54.229 And I was leading product there and turns out that the website was actually getting 10,000 visits, like 10%. 00:13:54.739 --> 00:13:58.129 That other 90% were like key automation bots. 00:13:58.609 --> 00:14:00.168 And you know, the business doesn't know. 00:14:00.948 --> 00:14:04.984 The business doesn't know, also means that when you find this, you have to explain it. 00:14:05.224 --> 00:14:12.274 So the CMO has to go and say, Hey, by the way, you know how I reported my CAC and everything, you know, for the last year, by the way, I like 90% off. 00:14:12.634 --> 00:14:13.653 Not a fun conversation. 00:14:13.894 --> 00:14:14.163 Yeah. 00:14:14.464 --> 00:14:20.254 I wonder if we could use that actually to zoom out a little bit, because I find that metrics are just a thing, right? 00:14:20.254 --> 00:14:30.244 Like the fact of the matter is that like even if a team or department is measuring the right thing, like there's often like this translation that needs to happen between different levels of an organization or different departments. 00:14:30.874 --> 00:14:35.163 For example, a scrum team might be tracking customer satisfaction like CSAT. 00:14:35.464 --> 00:14:41.903 Maybe they're tracking it release over release, but like maybe the leadership is tracking I don't know, like recurring revenue and like retention and churn. 00:14:42.264 --> 00:14:50.214 Can a tool like TargetBoard help translate KPIs for one audience in a way that makes them meaningful for other levels of an organization? 00:14:50.543 --> 00:14:53.813 Or is this just one of those things that like a tool on its own

just can't fix? 00:14:54.053 --> 00:14:55.614 I wanna take a step back on this question. 00:14:56.124 --> 00:15:02.019 If you think about metrics in general, the traditional way of doing BI in tracking metrics means that you have to build every metric. 00:15:02.528 --> 00:15:06.078 You have to do like a session and plan every one of your metrics. 00:15:06.408 --> 00:15:10.068 So, so there's an actual cost on anything you wanna track if you have to develop it. 00:15:10.068 --> 00:15:10.308 Right. 00:15:10.698 --> 00:15:15.739 And then when you think about programs like OKRs, qbr, they're sort of very top down. 00:15:16.188 --> 00:15:20.808 They tell you exactly what you want, need to track, and it gives you like a tunnel vision of this is what I'm gonna track. 00:15:21.288 --> 00:15:25.489 I might have a lot of other things that are gonna be really important for me because they have to keep the border flow. 00:15:25.844 --> 00:15:30.239 But I'm not gonna report 'em them, and maybe I'm not getting even track them because I don't have the resources to do that. 00:15:30.658 --> 00:15:35.068 So with TargetBoard, we track everything and we get it very easy for you to pick and choose. 00:15:35.188 --> 00:15:38.038 We even recommend what to pick and choose to focus on. 00:15:38.038 --> 00:15:38.308 Right? 00:15:38.308 --> 00:15:44.038 And then you can send alerts and get insights and notifications, all that to make sure that thing you care about on track are there. 00:15:44.399 --> 00:15:49.168 We also make it really easy to explain, like we actually have features saying, how do I explain this up? 00:15:49.168 --> 00:15:49.964 How do I explain this down? 00:15:50.668 --> 00:15:53.798 It's like AI generated, okay, take this master, help me explain this to my manager. 00:15:54.009 --> 00:16:00.278 Help me explain it to my developer or to my sales rep to understand why it's important for me and how I wanted to think about that. 00:16:00.668 --> 00:16:10.129 So absolutely like getting the whole semantic layer protecting you to make sure that everybody like, like we get this feature request every day. 00:16:10.339 --> 00:16:12.019 Can I rename my metric? 00:16:12.019 --> 00:16:14.629 No, you can't because somebody's already has this metric. 00:16:14.629 --> 00:16:15.499 They always have a name for it. 00:16:15.499 --> 00:16:17.658 You can't just go ahead and give it another name. 00:16:17.899 --> 00:16:22.249 And by the way, if that name is not representative, what you're ex tracking, you can't give it that name. 00:16:22.729 --> 00:16:25.369 So we have all these features to help you sort of protect. 00:16:25.729 --> 00:16:28.278 Make sure that you're actually tracking what you're saying. 00:16:28.278 --> 00:16:32.418 You're tracking, and you're actually able to explain what it is that you are tracking, why that is. 00:16:32.629 --> 00:16:38.808 I think that's such a cool feature because I don't know, I've worked with a lot of dashboarding software over the years. 00:16:38.808 --> 00:16:41.568 Not like deep, but you know, I have exposure to it. 00:16:41.989 --> 00:16:44.089 And that's usually the gap, right? 00:16:44.089 --> 00:16:46.428 To your point, it's okay, well that's all fine and good. 00:16:46.479 --> 00:16:48.129 What is this dashboard telling me? 00:16:48.178 --> 00:16:49.408 Can you explain this? 00:16:49.408 --> 00:16:53.158 And then the data storytelling becomes the gap because not everyone is great at it. 00:16:53.428 --> 00:16:56.369 Some people are great at it, and maybe that's the superhero skill. 00:16:56.369 --> 00:17:09.259 But I like the idea that AI can help you educate someone about why this metric is important, where it comes from, what it means if it's wavering, and that it's not necessarily I'm sure it could deliver that answer directly from the tool. 00:17:09.539 --> 00:17:28.410 But I like the, your framing of arming a human to be able to explain their metric well to someone who is not them, to someone who is in a different department, has different specializations, has different knowledge, and still can frame that narrative of what this all means so that we're not just looking at a bunch of numbers and going, wow, that changed 45% week over week. 00:17:28.710 --> 00:17:29.490 That's interesting. 00:17:29.579 --> 00:17:30.299 All right, let's move on. 00:17:30.569 --> 00:17:30.660 Right. 00:17:31.714 --> 00:17:32.525 That's what happens. 00:17:32.674 --> 00:17:34.414 Everyone looks at it, they're like shrugging. 00:17:34.414 --> 00:17:36.694 They're like, wow, that's a big number. 00:17:36.994 --> 00:17:37.924 And then we like move on. 00:17:37.924 --> 00:17:39.034 But no one actually understood what. 00:17:39.450 --> 00:17:40.529 Never read the reports. 00:17:40.740 --> 00:17:42.900 You'll have scheduling reports to have a dashboard held. 00:17:42.900 --> 00:17:47.220 People don't really read the reports unless they have a very specific, they want the reports to be there. 00:17:47.430 --> 00:17:47.640 Right. 00:17:47.640 --> 00:17:54.210 They want to know that somebody is tracking that, that is there in case they need it, but they very rarely read the reports like, you know, it's the outlier. 00:17:54.628 --> 00:18:00.900 By the way, and when we send reports, we also have an AI agent that sort of analyzes them and summarizes them for the business user. 00:18:01.138 --> 00:18:04.619 So they get okay, so you just gave me this big report on what I to care about. 00:18:05.159 --> 00:18:05.369 Yeah. 00:18:05.369 --> 00:18:07.079 You act please summarize this for me. 00:18:07.259 --> 00:18:08.369 I like that it's inbuilt. 00:18:08.670 --> 00:18:12.000 I'm jumping ahead a little bit, but I remember you telling me in the green room. 00:18:12.430 --> 00:18:34.659 That there is like this conversational aspect to it as well, like not just the summary of here's what's important about, you know, this report, but I was complaining about, well Google Analytics for my favorite villain,

which looks like it's set up to have a conversation to be like, you know, especially in the world of web, I'd be like, how many unique visitors did I get to this page last week compared to this same time, you know, last year? 00:18:34.869 --> 00:18:35.648 Maybe there's a way to do it. 00:18:35.769 --> 00:18:36.878 It's never really worked for me. 00:18:37.809 --> 00:18:39.339 Or even please explain this metric to me. 00:18:39.654 --> 00:18:45.160 Is set a feature that you have developed are developing just a conversational sort of insights. 00:18:45.369 --> 00:18:47.410 So I think, you know, a lot of people are trying to crack it. 00:18:47.410 --> 00:18:55.450 I think what you're experiencing in the, in GA4, you know, you'll see the same thing in mixed panel table below and so, and the problem is that LLMs are really bad with data. 00:18:55.720 --> 00:19:03.150 They seem to be getting better, but you know, if you ask them, you know, how many you upload a CSV and you'll tell 'em, Hey, can you tell me how, what's my email open rate? 00:19:03.599 --> 00:19:08.960 They won't be able to give you the answer unless you really specify like each column, the CSV, what they did, so, and we think we solved that. 00:19:09.319 --> 00:19:11.180 So we haven't added that to the platform. 00:19:11.450 --> 00:19:13.039 You are able to ask questions. 00:19:13.640 --> 00:19:19.789 Get reliable, trustworthy information that the AI also checks to verify that it's not hallucinating. 00:19:19.845 --> 00:19:27.855 And we think that's sort of, you know, it's gonna be a killer feature because people are going to want to process and consume data in multiple ways. 00:19:27.855 --> 00:19:34.284 It's not gonna be only conversational or only the dashboard, but you have different ways and different needs to interact with that. 00:19:34.523 --> 00:19:36.625 And I think that they compliment each other very well. 00:19:36.834 --> 00:19:40.944 It's a little bit like, you know, retail 10 years ago where I said, am I you investing mobile or not? 00:19:41.269 --> 00:19:45.140 So, so everybody went into, did omnichannel and that's sort of, I think where data's going also. 00:19:45.529 --> 00:19:48.619 And it's working pretty cool, but not all of you are using it. 00:19:48.920 --> 00:19:57.349 I think like when you think about people being able to actually phrase in text the question that you want answered in data is not that straightforward. 00:19:57.799 --> 00:20:06.969 And because you have the waiting cycle you have you know, you're gonna have ask 10 questions by, you know, to get like the one answer you're gonna, you could have gone on a dashboard like with two clicks. 00:20:07.239 --> 00:20:07.419 Yeah. 00:20:07.419 --> 00:20:08.469 It's actually might not be shorter. 00:20:09.128 --> 00:20:10.598 How many deals did I close last month? 00:20:10.659 --> 00:20:12.638 Oh, by the way, I actually care about one region. 00:20:12.909 --> 00:20:21.344 So can you down to, and you know, can you show it to me by it's a lot of writing, only waiting, responsive where, you know on web, it just clicks. 00:20:21.674 --> 00:20:24.074 And I like that idea that data is different, right? 00:20:24.074 --> 00:20:32.653 And I have experienced that with quite simple data, like time sheet data into ChatGPT, and it's just it just starts hallucinating or, you know, like grabbing the wrong thing. 00:20:32.653 --> 00:20:35.854 And I'm like, oh, you know, like our brains are so wired to think in tabular data. 00:20:36.588 --> 00:20:38.118 Not necessarily these LLMs. 00:20:38.328 --> 00:20:46.818 I like this idea that A, yes, please don't just take that report necessarily and shove it into ChatGPT and expect it to kind of be good at the data analysis. 00:20:47.028 --> 00:20:48.739 That's why I like that you're building your own feature. 00:20:48.769 --> 00:20:50.808 BI like that it checks itself, right? 00:20:51.019 --> 00:20:57.199 Maybe that's sort of standard fair for a data scientist, but you know, I like the idea that it's like there is another process. 00:20:57.199 --> 00:21:02.598 It's gonna be like before I deliver this very important information about, you know, performance and metrics and impact. 00:21:02.989 --> 00:21:05.239 Is it right before I just sort of blurt it out? 00:21:05.509 --> 00:21:06.679 Trust, trust is everything. 00:21:06.799 --> 00:21:08.449 That's the risk, you know, with MCP servers. 00:21:08.838 --> 00:21:22.784 So like you're asking for data from like whatever system, you don't actually know what the AI is gonna do with it and like how accurate like the response is gonna, what we do with our MCP server by the way, and that it verifies that the data and by, that's across all your systems. 00:21:23.054 --> 00:21:29.354 So pull data from all your systems, combine it together and verify if that demonstrates and the response that you're getting is a hundred percent accurate. 00:21:29.653 --> 00:21:31.364 Yeah, it actually your MCP server. 00:21:32.054 --> 00:21:35.084 Model context, protocol, that server is the backbone. 00:21:35.084 --> 00:21:40.469 It's like the thing that's vetting all the data coming into it, not just the one player on the field coming in and coming up. 00:21:41.233 --> 00:21:41.804 That's very cool. 00:21:42.223 --> 00:21:46.084 I wanted to dig into the future of metrics. 00:21:46.213 --> 00:21:53.354 Just get your POV because the sense I get from the circles I travel in is that things are changing around metrics. 00:21:53.354 --> 00:21:57.163 You can't just have a static scoreboard anymore with the usual KPIs. 00:21:57.554 --> 00:22:09.134 But I was hoping if I could get your BOV and look a bit into the future, what are some unconventional metrics that you see emerging that might be setting the tone for what KPIs might look like in the future? 00:22:09.163 --> 00:22:16.364 Or what are your predictions about how organizations will track KPIs like 3, 5, 10 years from now? 00:22:16.604 --> 00:22:22.874 So I

think at a micro level you'll see much more like AI KPIs and we're already starting to see that, right? 00:22:22.874 --> 00:22:24.403 You know, how many agents am I running? 00:22:24.824 --> 00:22:28.034 How effective they are, what are they doing, you know, to the business KPIs. 00:22:28.753 --> 00:22:33.433 But at the business level, at the high level, I think you care less about how it's done, right? 00:22:33.433 --> 00:22:36.374 So I think not a lot's gonna change. 00:22:36.614 --> 00:22:44.054 You know, if you're tracking ARR, if you're tracking churn, you're tracking delivery times, the top metrics are gonna stay the same. 00:22:44.384 --> 00:22:46.314 So that's the language of your business. 00:22:46.614 --> 00:22:49.134 What happens underneath the hood is gonna change. 00:22:49.523 --> 00:22:50.693 And they're also, I think you know. 00:22:51.183 --> 00:23:01.154 So, so if you take it one step down saying, okay, like we need to deliver A, B, and C, but now we're tracking velocity or we're tracking quality, then the velocity and the quality are the second level of those metrics. 00:23:01.484 --> 00:23:06.345 If you're taking one, one level below, then you're starting to say, okay, so my velocity, what is it made up out of? 00:23:06.345 --> 00:23:06.555 Right. 00:23:06.615 --> 00:23:06.825 Okay. 00:23:06.825 --> 00:23:08.253 So it's in development work. 00:23:08.659 --> 00:23:12.720 It's you know, things that are blocked here and there, things that are stuck in review. 00:23:13.019 --> 00:23:22.799 Things that, you know, had their scope changed in and they had to circle back and, you know, I had to open the context and, oh, by the way, I have all these AIs that are doing stuff and some of them are working, so they're not working. 00:23:23.128 --> 00:23:31.169 And now I have to spend time and resources and figuring out like, why, you know, GPT-5 is not giving me the same results that I was expecting to before. 00:23:31.648 --> 00:23:34.259 So you know that it's that level of. 00:23:34.634 --> 00:23:39.584 Like the nitty gritty details of, you know, what's moving the needle behind the scenes. 00:23:39.913 --> 00:23:41.653 I love that sort of layering because I think you're right. 00:23:41.653 --> 00:23:47.804 I mean, you know, you're if, as long as your business model's the same, some of those top, I think you described earlier as like a top down metric, right? 00:23:48.134 --> 00:23:53.204 The OKR tunnel vision, sort of top of the chain might not change that much. 00:23:53.624 --> 00:23:56.924 I do love that idea that like beneath it could be changing a lot. 00:23:56.924 --> 00:24:01.934 And that's where, you know, when we started this conversation, you were like, you could just measure what's gonna help you get to your goal. 00:24:01.934 --> 00:24:11.744 If your goal is to have more people using AI to make better decisions and that's gonna move you towards your goal, then you know, track that at that lower level cycle time. 00:24:11.773 -->

00:24:17.068 And actually the thing you were mentioning earlier about, people don't always know how to ask for what they want. 00:24:17.099 --> 00:24:18.659 You had it in a data context. 00:24:18.659 --> 00:24:31.544 But I'd say, you know, in general for prompting and I was tagged into a post on LinkedIn by Jim Highsmith who was a co-author of the Agile Manifesto, and he had written this story you know, about these folks, kind of like thinking through new metrics. 00:24:31.874 --> 00:24:38.939 And I don't know if I'm coming from a place of ignorance on this if people are measuring this, but one of the things was prompt quality index. 00:24:38.969 --> 00:24:39.239 Right. 00:24:39.239 --> 00:24:45.209 Measuring the quality of people's prompts, because to your point, just because people are using AI doesn't mean it's actually making them more efficient. 00:24:45.209 --> 00:24:55.949 They could be in circular conversations with a robot and you know, they would still show up as using AI in their job, but maybe not necessarily having like high quality prompts. 00:24:56.068 --> 00:24:57.869 I dunno how you'd go about measuring that, by the way. 00:24:58.109 --> 00:25:04.949 But I like that notion that, yeah, what matters to measure at the lower levels will change and can change quite a bit. 00:25:05.338 --> 00:25:12.719 At the top levels might not change that much in terms of, you know, like business success and like growth and revenue metrics. 00:25:13.078 --> 00:25:18.078 But I guess maybe for me the question is isn't it a lot like you, you had mentioned it. 00:25:18.709 --> 00:25:25.878 I think I know what you can, I think I know what your answer might be, but there's a lot of overhead every time you like change a metric 'cause then you need to figure out a way. 00:25:26.239 --> 00:25:35.348 To connect it to a source and educate people on what it means and then get people in the cadence of reporting on it and understanding how it's pushing things forward. 00:25:35.648 --> 00:25:51.993 Is that going to be in the future or even now, a sort of like heavy operational overhead that businesses and teams should plan for to be like, yeah, we need like a data person on everything because when we change our metrics because of ChatGPT 5 releasing or whatever, like we need someone who's gonna be. 00:25:52.868 --> 00:25:56.648 They're ready to wire it up, you know, please wire up this new metric so that we can keep going. 00:25:56.828 --> 00:25:58.148 That's the stake today. 00:25:58.209 --> 00:25:59.199 That's what's happening now. 00:25:59.199 --> 00:26:00.278 It's been happening for years. 00:26:00.278 --> 00:26:00.459 Right. 00:26:00.459 --> 00:26:05.409 You have an analyst for, or analyst team for every department, and that's what they do, right? 00:26:05.469 --> 00:26:08.118 They just make sure that the metrics are working and it's. 00:26:08.618 --> 00:26:09.909 It's not even custom metrics, right? 00:26:09.913 --> 00:26:10.949 They're just reinventing the

wheel. 00:26:10.949 --> 00:26:15.778 Everybody's using pretty much the same metrics right there, but, you know, and you know, big companies, they love replatforming. 00:26:16.199 --> 00:26:17.459 They love replatforming. 00:26:17.459 --> 00:26:21.489 It's all they do all day is replatforming, like migrating from one system to another system does the same thing. 00:26:21.999 --> 00:26:29.679 So, but each time they're replatform it creates this whole big data project of oh, so now we have to update all our reports, right? 00:26:29.679 --> 00:26:31.628 So there's this huge cycle of job security there. 00:26:32.108 --> 00:26:35.858 And part of what we do is saying, oh yeah, you actually don't need all that. 00:26:35.858 --> 00:26:37.179 Like you can replatform. 00:26:37.759 --> 00:26:40.848 All your reporting, all your metrics are gonna stay the same. 00:26:40.848 --> 00:26:46.489 If you have to update it, you know, you, you can automatically set it up to notify everybody who needs to know that the metric has been updated. 00:26:46.878 --> 00:26:48.169 And you don't have to do anything to update it. 00:26:48.249 --> 00:26:49.179 Does it on its own. 00:26:49.659 --> 00:26:55.089 I think the world today is broken because people have too many systems or too many data that they can't really use. 00:26:55.538 --> 00:27:02.888 And I think where it's going is that tools like TargetBoard are going to make it much easier to use that data and to get to that data. 00:27:02.888 --> 00:27:04.554 And you don't need all those men in the middle. 00:27:04.884 --> 00:27:07.314 A lot of time don't actually understand your business. 00:27:07.314 --> 00:27:12.134 Like we, we had a conversation with the chief strategy officer of you know, a ma major company. 00:27:12.134 --> 00:27:13.003 I don't wanna say the thing. 00:27:13.394 --> 00:27:25.994 And he said, well, I have a hundred BI and LS people on my team, and every time he has a question, he goes, every time I have a new question, it takes me at least six months to get an answer that is there. 00:27:26.354 --> 00:27:31.999 The infrastructure is there, but that it has to go through so many people for them to understand what I'm actually asking. 00:27:32.443 --> 00:27:32.503 It. 00:27:33.314 --> 00:27:33.854 That's all off. 00:27:33.983 --> 00:27:35.963 We can give it to you today, by the way. 00:27:35.963 --> 00:27:36.413 He told me. 00:27:36.413 --> 00:27:38.663 Yeah, but I can't condemn the data I guys to take it. 00:27:39.023 --> 00:27:39.804 That's a different problem. 00:27:39.804 --> 00:27:42.124 It's my problem, but but if you, where I'm going. 00:27:42.189 --> 00:27:42.289 Yeah. 00:27:42.289 --> 00:27:42.919 No, absolutely. 00:27:42.954 --> 00:27:44.605 And I've seen that happen. 00:27:44.934 --> 00:27:47.335 I've seen it happen, especially in financial services. 00:27:47.335 --> 00:27:57.263 I've seen it in like CX and the customer experience craft, like they're trying to use data to create, you know, an intelligent customer experience based on stuff that's being gathered across many departments. 00:27:57.894 --> 00:28:00.505 And yet to your point, they're gathering tons of data. 00:28:00.505 --> 00:28:05.384 No one knows what it is or what to do with it or how to turn it into something that could improve customer experience. 00:28:05.714 --> 00:28:06.464 It's just there. 00:28:06.615 --> 00:28:11.234 And like sometimes people are just scoreboard watching and then eventually they get disappointed and they replatform. 00:28:11.535 --> 00:28:11.625 Right? 00:28:12.224 --> 00:28:12.704 It's so. 00:28:13.184 --> 00:28:15.924 It takes me back to what you said before you know, what are people tracking? 00:28:16.555 --> 00:28:18.894 So a lot people tracking like costs, right? 00:28:18.894 --> 00:28:19.734 And cloud costs. 00:28:20.154 --> 00:28:24.654 Now people are tracking, you know, problems cost, like how much was they spending and how to optimize prompts. 00:28:24.654 --> 00:28:26.724 Do they spend less money on their l lms? 00:28:27.115 --> 00:28:35.404 And you have all this data that is just sitting there in companies and it has a cost, it has a storage cost and it tracking cost and amazing. 00:28:35.644 --> 00:28:36.845 And nobody knows why. 00:28:37.174 --> 00:28:38.809 And you have all these data, nobody's using that. 00:28:39.259 --> 00:28:44.844 So it's you know, these like the people who collect stuff and you know, in the garage and don't use them. 00:28:45.523 --> 00:28:49.023 So company become hoarders endless systems. 00:28:49.023 --> 00:28:50.250 They don't use them for anything. 00:28:51.730 --> 00:28:54.388 Because you know what we were taught to think it was cheap, right? 00:28:54.388 --> 00:28:55.409 That storage is cheap. 00:28:55.769 --> 00:28:56.849 This is just a few numbers. 00:28:56.849 --> 00:28:57.359 Yeah, exactly. 00:28:57.365 --> 00:28:59.939 It was cheap and now it's not anymore. 00:28:59.939 --> 00:29:04.739 And now we're thinking about Yeah, like cost per prompt and you know, these sort of transactional calls. 00:29:05.398 --> 00:29:09.990 And also the storage, because it's more complex now, and also we've been accruing it for decades. 00:29:10.199 --> 00:29:11.609 It's not doing anything with it. 00:29:11.939 --> 00:29:12.569 I can relate to that. 00:29:12.569 --> 00:29:13.648 I'm a pack rat as well. 00:29:13.684 --> 00:29:19.374 I try not to delete pretty much anything, so thank you cloud for making that a reality that I could do that. 00:29:19.673 --> 00:29:24.413 But also to your point, you know, even with some of that stuff, like I'm not doing anything with it. 00:29:24.534 --> 00:29:26.663 I just, it comforts me that it's there. 00:29:26.994 --> 00:29:27.683 Maybe a little bit. 00:29:27.683 --> 00:29:28.673 Maybe in 10 years you'll need it. 00:29:28.913 --> 00:29:29.153 Yep. 00:29:29.204 --> 00:29:29.214 Yeah. 00:29:29.214 --> 00:29:29.654 Exactly. 00:29:29.654 --> 00:29:30.845 Yeah, that's exactly right.

00:29:31.609 --> 00:29:38.559 I I had it here to ask you sort of about TargetBoard and your positioning as an AI enhanced KPI tool. 00:29:38.888 --> 00:29:43.179 You've already kinda walked us through some of the AI driven features. 00:29:43.179 --> 00:29:45.818 There's the sort of AI driven suggestions of metrics. 00:29:46.089 --> 00:29:51.159 There is the sort of insights summaries, I guess, from a data perspective. 00:29:51.489 --> 00:29:51.759 Yeah. 00:29:51.788 --> 00:29:57.939 I mean, is there any other like AI feature that you wanna talk through, or maybe even the other side of it is like. 00:29:58.328 --> 00:30:01.669 What made you decide to double down on AI? 00:30:01.699 --> 00:30:05.479 Like it's in the name, well, it's at least in the domain name, right? 00:30:05.479 --> 00:30:12.679 AI, like clearly was core to the DNA of the solution that you've built, what are you most proud of about it and where do you see it going? 00:30:13.068 --> 00:30:15.288 So I think it's a complicated question, right? 00:30:15.604 --> 00:30:17.058 I'm proud about a lot of things. 00:30:17.058 --> 00:30:23.719 I think we've been able to leverage AI from very early on to really augment what we're trying to build an experience. 00:30:23.723 --> 00:30:24.179 We're trying to create. 00:30:25.134 --> 00:30:32.204 There's pros and cons of being an AI first company, and I think we're AI second company, but AI is very prominent to everything we do. 00:30:32.684 --> 00:30:35.714 And every feature we build, we say, okay, how does AI make it better? 00:30:35.714 --> 00:30:41.325 Not how do we do this with AI, but why are we trying to build and how does AI make this better than you could have before? 00:30:41.924 --> 00:30:42.795 So lemme give you an example. 00:30:43.214 --> 00:30:44.414 So we have all your metrics, right? 00:30:44.414 --> 00:30:49.880 And for any metric, doesn't matter how you slice and dice it, you can create notifications, automations, and alerts and so on and on, right? 00:30:49.880 --> 00:30:51.500 So you have a deal that's second, a pipeline. 00:30:52.339 --> 00:30:58.710 You have a button in production, you have a support ticket that's, you know, for a very specific QF customer and you're not meeting your SLA. 00:30:59.130 --> 00:31:00.900 Create an alert, send it to wherever it needs to go. 00:31:01.380 --> 00:31:05.839 But having people think about the alerts that they want to create is a hassle, right? 00:31:05.839 --> 00:31:09.529 People need to think about need, having to think about what they want to create. 00:31:09.529 --> 00:31:18.888 How, so, for example, what we released this week is that the AI, when you're looking at a metric, it will suggest the automation that you can create. 00:31:19.398 --> 00:31:20.240 To improve that metric. 00:31:20.628 --> 00:31:23.388 We already have like recommendations, like strategies for how to improve metric. 00:31:23.388 --> 00:31:28.460 Like I actually recommend the automation that you can create, like one quick click of a button, create it for you. 00:31:28.970 --> 00:31:34.609 And this was relatively way harder to do pre AI and now it's much easier. 00:31:34.609 --> 00:31:34.759 Right? 00:31:34.789 --> 00:31:38.240 We're moving faster today than I've ever moved any other company. 00:31:39.394 --> 00:31:41.255 That's fair and also insane. 00:31:41.644 --> 00:31:45.515 Based on what I know about your background, it is a good use of it. 00:31:45.515 --> 00:31:56.134 I mean, I think like the, what I find is that there's a natural sort of cultural inclusion of AI in the world of data because, you know, coming from like the benefits of machine learning. 00:31:56.509 --> 00:31:59.150 And frankly, just dealing with swaths and swaths of data. 00:31:59.150 --> 00:31:59.390 Right? 00:31:59.390 --> 00:32:04.339 You know, like even in your previous roles where like your tools are like, they're processing petabytes of data. 00:32:04.490 --> 00:32:09.829 We don't even have a like conceptual, tangible model for what that even looks like in our brains. 00:32:10.069 --> 00:32:12.420 So it makes sense that okay, we're gonna need like compute power. 00:32:12.659 --> 00:32:16.169 But what I really like is that sort of and I like it actually in the LLMs too right now. 00:32:16.169 --> 00:32:16.409 Right? 00:32:16.409 --> 00:32:18.710 Where it's suggest to me what to do next. 00:32:19.025 --> 00:32:23.255 Whether it's right or wrong, and then I will learn what you think is the right or wrong next step. 00:32:23.585 --> 00:32:32.045 And then I will continue to suggest it because, you know, to this day I, you know, I see a lot of folks and especially when I'm outta my element, like I do a thing and I'm like, what should I do next? 00:32:32.045 --> 00:32:32.555 I'm not sure. 00:32:32.555 --> 00:32:34.414 But if something, you know, helpful clippy right? 00:32:34.414 --> 00:32:37.474 Is going, Hey, you writing a letter, do you wanna put a letterhead on? 00:32:37.474 --> 00:32:48.954 It actually is a great way to I dunno, just kind of cut through discomfort or doubt or hesitation or just like knowledge gaps of actually I wasn't sure what to do next, but actually a little alert would be great. 00:32:48.954 --> 00:32:49.704 Thank you very much. 00:32:49.855 --> 00:32:54.775 A little notification and then to your point, at least I'm assuming it also sets up that notification. 00:32:54.805 --> 00:32:56.454 It's great, I'm on it, I've done it, it's done. 00:32:56.845 --> 00:33:00.875 Every Tuesday you're gonna get this notification about this metric or when it's out of tolerance or, you know. 00:33:00.970 --> 00:33:05.095 It's a very different approach than, you know, what data science was like, you know, three years ago. 00:33:05.095 --> 00:33:15.055 So taking an example, you know, from Gettacar, you know, we had a team of data scientists that, you know, did autofinance optimization which every time you get a credit application which lender did you send it to?

00:33:15.444 --> 00:33:24.315 That was, you know, a team that worked on it nonstop trying to maximize, you know, the single digit percentages of how do you get more applications approved. 00:33:24.615 --> 00:33:26.369 And that was like a data science project. 00:33:26.369 --> 00:33:29.490 Now it's every feature you build saying, okay, how do I make this better? 00:33:30.089 --> 00:33:36.750 And it's not that it's not simple, but if you do it right, you're able to get like just amazing results and very quickly. 00:33:37.049 --> 00:33:38.369 I love how fast these things are changing. 00:33:38.400 --> 00:33:38.700 Right. 00:33:40.200 --> 00:33:42.990 Maybe just for fun, do you have a question that you want to ask me? 00:33:43.470 --> 00:33:47.625 What are the most impactful trends that you're seeing as project management today? 00:33:48.045 --> 00:33:48.914 That's a good one. 00:33:48.914 --> 00:33:59.375 I think the biggest one is actually the one I started out with, which is this notion that project managers are not just, you know, iron Triangle people trying to deliver projects on time, on budget, you know, within scope. 00:33:59.775 --> 00:34:04.934 This sort of like this revelation that actually we can be more strategic, but we need to know more. 00:34:04.934 --> 00:34:06.615 We need to know more, we need to do more. 00:34:06.914 --> 00:34:09.195 And we were already doing a lot and. 00:34:09.554 --> 00:34:13.815 Now we feel like we have to be sort of strategists or like delivery strategists. 00:34:13.815 --> 00:34:16.695 There's the onus is on us to understand the business more. 00:34:16.965 --> 00:34:25.144 And then there's this mysterious question of cool I'm accountable in some way for impact, but you know, I'm not necessarily involved in measuring that impact. 00:34:25.534 --> 00:34:32.255 In a lot of cases, we lead a project and then we move on, it launches and we, you know, we go away onto the next thing, off into the sunset. 00:34:32.630 --> 00:34:37.909 And now there's this big mindset shift of okay, well actually we are value delivery specialists. 00:34:37.909 --> 00:34:46.280 We actually are meant to create the value, even if that means the plan was wrong and we had to change it, even if it means the scope was wrong, we had to change it along the way. 00:34:46.280 --> 00:34:54.375 Even if it means battling personalities in the boardrooms and you know, or you know, in the, you know, in the dev pit, there's a much more strategic component to it. 00:34:54.375 --> 00:35:00.534 And I think we're still trying to figure it out, but then I think it dovetails very nicely with like our adoption of AI. 00:35:00.969 --> 00:35:11.980 Where, you know, right now it's helping us be more efficient, shorten our workflows, create a bit more speed, but fundamentally help out with some of the stuff that we got bogged down with. 00:35:12.070 --> 00:35:17.500 You know, a lot of project managers are like, I'm too busy to then also be a strategic partner for

my stakeholders. 00:35:17.980 --> 00:35:20.559 And now there's this sort of rise that where it's okay. 00:35:21.219 --> 00:35:21.880 What are you doing? 00:35:21.880 --> 00:35:22.329 Well, okay. 00:35:22.329 --> 00:35:25.179 Yeah, like all my status reports oh, I've gotta update the dashboard. 00:35:25.179 --> 00:35:27.280 I've gotta go into Excel and update that spreadsheet. 00:35:27.340 --> 00:35:27.710 It's cool. 00:35:27.769 --> 00:35:32.119 You don't have to do that anymore because we can set up agents for that. 00:35:32.119 --> 00:35:38.840 We can, you know, look to AI for solutions, and then the next thing is okay, but now what do I do with that time? 00:35:39.079 --> 00:35:45.230 How can I then learn the skills to be a bit more of a leader, a strategic leader in the delivery of value? 00:35:45.530 --> 00:35:48.380 I think that's an interesting trend in my world that. 00:35:48.755 --> 00:35:57.755 I think intersects with a lot of this because in a perfect world, we spend more of our time asking ourselves the question, are we doing the right thing to achieve the mission and have the impact? 00:35:57.755 --> 00:36:02.105 And that's almost exactly where you started about how do we know that we're measuring the right thing? 00:36:02.135 --> 00:36:05.224 You're measuring the right thing if it's helping you move forward. 00:36:05.554 --> 00:36:09.934 And I think that's kind of like an interesting aspect of project management and project leadership today. 00:36:10.324 --> 00:36:10.715 Very cool. 00:36:10.985 --> 00:36:11.980 I'm gonna ask you another question. 00:36:12.550 --> 00:36:14.599 So, I know what I think, but I wanna hear what you think. 00:36:14.960 --> 00:36:21.045 We've been talking about AI and you know, talking about, you know, how AI can help project managers automate and become more efficient and so on. 00:36:21.320 --> 00:36:28.250 Do you think there is a stage where like an AI agent becomes a project manager or replaces the project manager like completely? 00:36:28.639 --> 00:36:33.019 I think for some people's definition of what a project manager does, yes. 00:36:33.260 --> 00:36:38.059 Because there is the sort of level of, oh, project managers are just administrators. 00:36:38.059 --> 00:36:42.170 They're taking notes, doing meeting minutes, they're following up with action items. 00:36:42.170 --> 00:36:48.500 They are measuring, you know, an estimate against, you know, an actual, how long it actually took to do all that stuff. 00:36:48.500 --> 00:36:49.105 Yeah, definitely. 00:36:49.190 --> 00:36:52.429 I think is almost easily replaced by AI. 00:36:53.150 --> 00:36:57.170 I think the parts that are not are like the human to human decision making. 00:36:57.409 --> 00:37:01.460 Like I think for a while at least some of the big decisions, right? 00:37:01.489 --> 00:37:06.829 Replatforming decisions, risks around, you know, implementation, I think will be conversations between humans. 00:37:06.980 --> 00:37:15.880 That's why I

really like your data storytelling feature because it's not oh, I, it'll be like a little circle with a question mark in it, and like the executive can just click it and it goes, this metric is this. 00:37:16.465 --> 00:37:20.125 Instead, you're arming the person who owns that metric to tell the story about it. 00:37:20.394 --> 00:37:38.815 And I think that is the like decision making layer in projects and in business that I think will continue to stay the same, like humans making their case you know, battling things out, convincing one another, persuading one another to make good decisions and maybe think a little bit outside the box and think about, you know, things that maybe haven't been done yet. 00:37:38.875 --> 00:37:40.344 And those sort of innovative thoughts. 00:37:40.344 --> 00:37:43.315 And I think that comes from humans interacting with one another. 00:37:43.675 --> 00:37:51.204 Not to say that AI won't or isn't innovative, like I think it can create ideas that, you know, most of its users haven't thought of yet. 00:37:51.565 --> 00:37:58.454 But fundamentally I think it's still remixing and I think fundamentally we still treat it as a helper and not someone who's gonna make a decision for us. 00:37:58.875 --> 00:37:59.565 That's changing. 00:37:59.565 --> 00:38:01.034 But I think for now, I think. 00:38:01.195 --> 00:38:01.889 I agree with that. 00:38:02.355 --> 00:38:10.364 It also what makes a difference between a good project manager and a bad one and you know, was telling people, oh, you know, we can't afford a project manager, you. 00:38:10.465 --> 00:38:13.820 Do that on the side, like you manage this project and then like it doesn't work. 00:38:13.820 --> 00:38:15.460 It doesn't worry because you know it's a profession. 00:38:15.940 --> 00:38:16.809 Can't do it on the side. 00:38:17.170 --> 00:38:17.829 Yeah, I like that. 00:38:17.829 --> 00:38:22.780 I like the idea that it's like, it might not replace all project managers, but it will probably replace the bad ones. 00:38:23.380 --> 00:38:24.159 It's true for everything. 00:38:24.630 --> 00:38:26.800 Lior, thanks so much for spending the time with me today. 00:38:26.800 --> 00:38:27.880 This has been so much fun. 00:38:27.880 --> 00:38:28.840 I love nerding out with you. 00:38:29.260 --> 00:38:31.690 Before you go though, where can people learn more about you? 00:38:31.929 --> 00:38:35.860 LinkedIn, I get back to almost anybody who reaches out. 00:38:36.280 --> 00:38:37.150 Happy to speak to anybody. 00:38:37.510 --> 00:38:37.960 That's awesome. 00:38:38.380 --> 00:38:39.280 I commend you. 00:38:39.579 --> 00:38:41.619 My LinkedIn inbox is such a mess. 00:38:43.449 --> 00:38:43.960 Thanks again. 00:38:44.170 --> 00:38:45.070 Bye Galen, it's been a pleasure. 00:38:47.019 --> 00:38:49.989 That's it for today's episode of The Digital Project Manager Podcast. 00:38:50.230 --> 00:38:53.739 If you enjoyed this conversation, make sure to subscribe wherever you're listening. 00:38:54.039 --> 00:39:00.320 And if you want even more tactical insights, case studies and playbooks, head over to thedigitalprojectmanager.com. 00:39:00.340 --> 00:39:01.929 Until next time, thanks for listening.