1 00:00:00,320 --> 00:00:03,400 You and I both love yacht rock. Yes. 2 00:00:03,840 --> 00:00:09,280 And when during the time Tim and I are writing future proof and 3 00:00:09,320 --> 00:00:11,840 you know, when right after generative AI just came out, 4 00:00:11,840 --> 00:00:13,000 right? And I started to see the 5 00:00:13,000 --> 00:00:15,800 connection between generative AI and the previous forms of AI 6 00:00:15,800 --> 00:00:18,840 like analytical AI and machine learning, all that stuff and how 7 00:00:18,840 --> 00:00:23,120 it all links together. And I had this moment of like, 8 00:00:23,160 --> 00:00:25,560 Oh my gosh, this is it, this is it. 9 00:00:25,560 --> 00:00:28,840 The we get to be the BA as we want to be. 10 00:00:30,960 --> 00:00:33,080 Welcome to Business Analysis Live. 11 00:00:33,200 --> 00:00:36,320 I'm your host, Susan Moore, community engagement manager 12 00:00:36,320 --> 00:00:39,240 with the International Institute of Business Analysis. 13 00:00:39,320 --> 00:00:42,440 I explore topics with industry guests about the work of 14 00:00:42,440 --> 00:00:45,360 business analysis professionals and how their work helps 15 00:00:45,360 --> 00:00:47,600 organizations achieve better outcomes. 16 00:00:47,600 --> 00:00:49,520 And Yep, we take your questions too. 17 00:00:49,560 --> 00:00:51,520 Thanks for listening. Let's get started. 18 00:00:55,400 --> 00:01:01,600 You've seen a lot of talk here and in other spaces about AI and 19 00:01:01,600 --> 00:01:05,120 you might be thinking if you're a business analysis professional 20 00:01:05,120 --> 00:01:08,400 or if you are working with AI, what does that mean for me? 21 00:01:08,400 --> 00:01:12,280 What is the role that I need to be playing and what's going to 22 00:01:12,280 --> 00:01:14,560 change and maybe what won't change? 23 00:01:14,760 --> 00:01:18,560 That's a lot of questions and I've got the perfect person to 24 00:01:18,600 --> 00:01:21,840 answer those. And she has just published an, A 25 00:01:21,880 --> 00:01:27,320 very, it's kind of an academic article on the evolving role of 26 00:01:27,320 --> 00:01:32,080 business analysis in agentic AI. Specifically, I think this 27 00:01:32,080 --> 00:01:36,640 article is going to show you opportunities and possibilities 28 00:01:36,960 --> 00:01:40,680 for that role. And so I'm bringing on stage 29 00:01:41,040 --> 00:01:43,480 Miss Angela Wick. Hey, Miss Angela, how are you? 30 00:01:45,400 --> 00:01:47,400 It's good to see you. I'm, you know what? 31 00:01:47,400 --> 00:01:51,520 I am really happy that you are here to describe what this 32 00:01:51,520 --> 00:01:54,040 evolving role looks like, because I know that you talk 33 00:01:54,280 --> 00:01:57,680 with a lot of organizations and a lot of practitioners and a lot 34 00:01:57,680 --> 00:01:59,640 of people that are doing this kind of work. 35 00:02:00,000 --> 00:02:04,160 So you bring like you bring real examples. 36 00:02:05,840 --> 00:02:08,120 Thanks for having me. It's a fun topic. 37 00:02:08,600 --> 00:02:11,760 The paper was really fun to write and it's exciting to be 38 00:02:11,760 --> 00:02:15,240 able to talk about it and and keep this conversation going. 39 00:02:15,240 --> 00:02:19,400 It's so important for our role. Yeah, I'm, I am really excited 40 00:02:19,400 --> 00:02:22,840 because I think you do such a great job of, of helping us to 41 00:02:22,840 --> 00:02:26,360 understand what it looks like for individual practitioners. 42 00:02:26,360 --> 00:02:29,640 But what warms my heart is how you are speaking to leaders 43 00:02:29,960 --> 00:02:31,720 about what this needs to look like. 44 00:02:31,720 --> 00:02:35,440 I think we we've got to bring them with us as we are thinking 45 00:02:35,440 --> 00:02:38,000 about this evolving role. Absolutely. 46 00:02:38,000 --> 00:02:39,360 And that was one of the goals, right? 47 00:02:39,440 --> 00:02:41,920 Let's help our leaders understand how important 48 00:02:42,160 --> 00:02:44,480 business analysis is to agentic AI. 49 00:02:45,080 --> 00:02:48,400 It was clear to me that as you were putting that article 50 00:02:48,400 --> 00:02:52,240 together, you were thinking about just you were going broad. 51 00:02:52,240 --> 00:02:56,720 These are the skills and the things that a business analysis 52 00:02:56,720 --> 00:03:00,680 needs to do in order to move itself out of requirement 53 00:03:00,680 --> 00:03:04,440 gatherer to the next level. And I could see you thinking 54 00:03:04,440 --> 00:03:09,640 about, OK, so I've said AI literacy and I think where 55 00:03:09,640 --> 00:03:13,120 you're going next is, OK, what does AI literacy mean in 56 00:03:13,120 --> 00:03:15,520 practice? And then going deep separately, 57 00:03:15,600 --> 00:03:17,000 that's really what I was trying to say. 58 00:03:17,000 --> 00:03:19,680 Like I could see you thinking through that in the article. 59 00:03:19,920 --> 00:03:21,440 And I didn't know which came first. 60 00:03:21,440 --> 00:03:24,480 So it sounds like you've been. Thinking. 61 00:03:25,200 --> 00:03:28,800 Yeah, OK. I mean, both have been on my 62 00:03:28,800 --> 00:03:34,800 mind, I'd say for a year. And of course, what happens in 63 00:03:34,800 --> 00:03:38,520 this crazy AI world is that everything's changing so fast. 64 00:03:38,560 --> 00:03:41,600 Once you have a graft done of anything, you question it. 65 00:03:42,080 --> 00:03:45,800 So for a year I kept like rewriting a white paper and 66 00:03:45,800 --> 00:03:48,880 redoing this and redoing class outlines and re and like. 67 00:03:49,440 --> 00:03:51,840 And at one point I just had to say, you know, you just have to 68 00:03:51,840 --> 00:03:54,160 go with it. Yeah. 69 00:03:54,520 --> 00:03:57,800 And then the opportunity came up for that journal. 70 00:03:58,000 --> 00:04:01,800 Journal where I was like, oh, wait a minute, here's an 71 00:04:01,800 --> 00:04:04,960 opportunity to put a little deadline and fire under my feet 72 00:04:04,960 --> 00:04:08,280 to publish something. And you know, and we felt that 73 00:04:08,280 --> 00:04:09,760 way writing future proof too, right? 74 00:04:09,760 --> 00:04:11,640 It was like, Oh my gosh, you know? 75 00:04:12,400 --> 00:04:17,480 You told me when after, I think it was when we were meeting with 76 00:04:17,480 --> 00:04:20,040 you and after the book had come out or just before the book had 77 00:04:20,040 --> 00:04:24,720 come out, you said at that time I've really been thinking about 78 00:04:24,720 --> 00:04:28,960 agentic AI and I'm like, I don't know what that is. 79 00:04:30,720 --> 00:04:34,960 And so that was two years ago. So you've been thinking about 80 00:04:34,960 --> 00:04:36,400 these things. Thinking about it, playing 81 00:04:36,400 --> 00:04:38,560 around with it, working with clients on it. 82 00:04:38,920 --> 00:04:44,520 Yeah, yeah. So let's roll it back a little 83 00:04:44,520 --> 00:04:49,280 bit to just this article that you've put together and it's 84 00:04:49,280 --> 00:04:53,840 available on the IIBA website for our members. 85 00:04:54,080 --> 00:04:57,160 And it's the evolving role of business analysis and agentic 86 00:04:57,200 --> 00:04:59,720 AI, something that you've been thinking about for a while. 87 00:05:01,360 --> 00:05:04,960 My first question for you, it is a really interesting read. 88 00:05:04,960 --> 00:05:09,000 It is a really detailed read, but it's not like, because it's 89 00:05:09,000 --> 00:05:12,360 like, I don't know, 10-12 pages. I mean, it's yeah. 90 00:05:12,360 --> 00:05:15,560 And it's it, it's really good because I think you lay 91 00:05:15,560 --> 00:05:19,800 everything out the case for why business analysis needs to be 92 00:05:19,800 --> 00:05:22,600 involved. And then, you know, what got me 93 00:05:22,600 --> 00:05:26,560 excited was exactly what business analysis looks like 94 00:05:27,440 --> 00:05:31,200 when you wrote that. Who is your intended reader? 95 00:05:31,200 --> 00:05:33,360 Is it individuals? Is it leaders? 96 00:05:33,360 --> 00:05:38,400 Is it somebody else? As much as it's for business 97 00:05:38,400 --> 00:05:43,080 analysts to help us understand our role going forward, I also 98 00:05:43,080 --> 00:05:46,600 wrote it very intentionally for those outside of business 99 00:05:46,600 --> 00:05:50,640 analysis. So thank leaders, IT leaders, 100 00:05:50,640 --> 00:05:56,200 CTOSCIOS, business leaders as sort of a more global 101 00:05:56,200 --> 00:05:58,720 educational piece outside of our own ecosystem. 102 00:05:58,760 --> 00:06:04,600 I wanted it to be spread out further than our IIBA bubbles, 103 00:06:04,680 --> 00:06:06,600 right? Yeah, I agree. 104 00:06:06,600 --> 00:06:10,720 And I clearly got that, which to me was really exciting because I 105 00:06:10,720 --> 00:06:15,280 know one of the myths that you talk about very early is we've 106 00:06:15,280 --> 00:06:19,360 got to move from just being people that write requirements 107 00:06:19,360 --> 00:06:22,960 to this new world. And so that's been an issue that 108 00:06:22,960 --> 00:06:28,280 we've had for a long time is people getting us out into out 109 00:06:28,280 --> 00:06:31,200 of that box. And I feel like this article 110 00:06:31,200 --> 00:06:34,440 gives a little more of a sense of urgency. 111 00:06:35,560 --> 00:06:37,200 Yeah. And, you know, a lot of it stems 112 00:06:37,200 --> 00:06:40,720 from, you know, it's a formal journal and journals are peer 113 00:06:40,720 --> 00:06:43,400 review. And the peers reviewing this 114 00:06:43,400 --> 00:06:48,560 article are not BAS, right? These are genetic AI experts and 115 00:06:48,640 --> 00:06:53,400 AI and robotics machine learning experts where that's what really 116 00:06:53,400 --> 00:06:57,000 forced me to think about what language do I need to use to 117 00:06:57,000 --> 00:06:59,800 help those outside of our business analysis bubble. 118 00:07:01,080 --> 00:07:05,080 Like, yeah, this is exactly it. And so I tested a lot of the 119 00:07:05,120 --> 00:07:07,560 ideas and conversations with similar types of folks 120 00:07:07,560 --> 00:07:10,920 beforehand. Like as I hear them talk about 121 00:07:11,120 --> 00:07:13,720 and discuss their challenges and work with clients on it, it's 122 00:07:13,720 --> 00:07:16,760 like Dove, it's business analysis. 123 00:07:16,960 --> 00:07:20,520 But no one's calling it that in the AI world, right? 124 00:07:21,760 --> 00:07:24,680 So yeah, it took a lot of discussions and work and 125 00:07:24,680 --> 00:07:28,120 research to figure out, OK, how do I talk about this in a way 126 00:07:28,120 --> 00:07:32,400 that those that don't call it business analysis see it that 127 00:07:32,400 --> 00:07:33,800 way? Yeah. 128 00:07:34,080 --> 00:07:36,880 And This is why this article is speaking to me at the moment, 129 00:07:36,880 --> 00:07:40,920 because this is as I am reaching out to adjacent communities, I'm 130 00:07:40,920 --> 00:07:44,800 having to figure out how do I talk about business analysis to 131 00:07:44,800 --> 00:07:47,160 people that don't know the business analysis language, 132 00:07:47,160 --> 00:07:49,600 don't use that term, don't see themselves as doing it. 133 00:07:49,600 --> 00:07:50,880 Yeah. And it's funny because you can 134 00:07:50,960 --> 00:07:54,320 hear someone outside of our world say something like, well, 135 00:07:54,840 --> 00:07:57,920 an agent, you want to break down their task into subtasks and 136 00:07:57,920 --> 00:08:00,680 break that down into subtasks. And then you need to identify 137 00:08:00,960 --> 00:08:03,360 the policies and rules that they need to follow and the 138 00:08:03,360 --> 00:08:07,560 guardrails and boundaries and the data that they need and the 139 00:08:07,560 --> 00:08:10,160 memory context they need at certain points in time. 140 00:08:10,160 --> 00:08:16,000 And I'm just, you know, this is, this is like, and so I'm like, 141 00:08:16,080 --> 00:08:18,680 sounds like business analysis. You can just blank stare looking 142 00:08:18,680 --> 00:08:22,160 back at you and you're like, so then I mean, I remember in some 143 00:08:22,160 --> 00:08:25,560 conversations, I'd stay up till like 2 in the morning, like 144 00:08:25,640 --> 00:08:28,160 drawing it all out. And then I'd come back to an 145 00:08:28,160 --> 00:08:32,240 expert and go, well, OK, when you build this enterprise agent, 146 00:08:33,120 --> 00:08:37,360 you have to identify like every data element and every business 147 00:08:37,360 --> 00:08:41,919 rule and every scenario and the boundaries of each, right? 148 00:08:41,919 --> 00:08:45,240 And I practice like talking about it in the non BA terms 149 00:08:45,240 --> 00:08:48,920 with some stuff mixed in. And then I draw some of our 150 00:08:48,920 --> 00:08:53,000 pictures that we draw and outline things like Gherkin 151 00:08:53,000 --> 00:08:56,680 scenarios and all of this and the source of truth of the data 152 00:08:56,680 --> 00:08:58,800 and where the machine learning is happening in the process. 153 00:08:58,800 --> 00:09:01,000 And they're like looking at me like, yeah, exactly. 154 00:09:01,000 --> 00:09:05,080 And I'm like, but do you ever talk about it that way or draw 155 00:09:05,120 --> 00:09:09,160 these visuals or outline all of this together where we put rules 156 00:09:09,160 --> 00:09:11,120 and data and people and workflow together? 157 00:09:12,440 --> 00:09:15,280 The funny light bulb moment Susan Wright is that for so many 158 00:09:15,280 --> 00:09:18,640 software engineering and AI experts, this is just very 159 00:09:18,640 --> 00:09:20,800 intuitive to them. Yeah. 160 00:09:21,120 --> 00:09:23,920 But a lot of them are also building tech products, startup 161 00:09:24,280 --> 00:09:29,840 culture, So where I feel like a lot of our audience comes in or, 162 00:09:29,840 --> 00:09:33,520 or legacy organizations with a lot more regulation and 163 00:09:33,520 --> 00:09:35,080 complexity. And of course, we hear that all 164 00:09:35,080 --> 00:09:37,400 the time, right? And so that means that our 165 00:09:37,400 --> 00:09:41,480 business analysis skills and techniques become even more 166 00:09:41,480 --> 00:09:43,080 apparent. But it's not just about creating 167 00:09:43,080 --> 00:09:47,280 the document for that model. It's the thinking that all these 168 00:09:47,280 --> 00:09:50,320 models and techniques have always invited. 169 00:09:50,680 --> 00:09:53,880 And then how do we shape conversations around our 170 00:09:53,880 --> 00:09:55,880 analysis doing those models, right? 171 00:09:55,880 --> 00:09:59,520 So that shift from I need to create this model to put in my 172 00:09:59,520 --> 00:10:03,960 document to get approval. We kind of migrate from that to 173 00:10:05,400 --> 00:10:09,360 I need to do this model and partner with AI to do the model 174 00:10:09,840 --> 00:10:13,600 to elevate my thinking so that I shape the right conversations 175 00:10:14,080 --> 00:10:17,520 and then help AI create the right specs to develop this 176 00:10:17,520 --> 00:10:20,400 agent. Like quite a shift, right? 177 00:10:20,760 --> 00:10:23,080 Yeah, it really isn't. And I know that you talk a 178 00:10:23,080 --> 00:10:25,800 little bit about that. We move into more of these 179 00:10:25,800 --> 00:10:29,920 thinking documents that they are not really the thing that some 180 00:10:29,920 --> 00:10:32,720 developer is going to go run off and build towards because 181 00:10:32,720 --> 00:10:35,760 frankly they're probably using AI for that. 182 00:10:35,760 --> 00:10:39,480 But what those thinking documents are doing are helping 183 00:10:39,480 --> 00:10:45,400 us to solidify context, decisions, bigger goals and that 184 00:10:45,400 --> 00:10:50,120 we're working from those, which is another quite different shift 185 00:10:50,120 --> 00:10:56,200 from requirement gathering. Yeah, because specs, specs that 186 00:10:56,240 --> 00:10:58,960 AI creates in the same environment to the development 187 00:10:58,960 --> 00:11:00,920 happens, which is kind of a newer shift right now. 188 00:11:00,920 --> 00:11:04,920 We're all, you know, learning about those are going to be the 189 00:11:04,920 --> 00:11:08,880 new source of truth, which means the work that we do is in 190 00:11:09,240 --> 00:11:12,840 helping prompt and create those and work with those AI systems. 191 00:11:12,840 --> 00:11:16,560 But we have to do this other work of models and techniques 192 00:11:16,560 --> 00:11:21,040 that we've always done to help us think, help others think, and 193 00:11:21,040 --> 00:11:24,600 help validate and review that AI is not missing things. 194 00:11:25,000 --> 00:11:30,760 Yes, because I think that you also talk about with AI going 195 00:11:30,760 --> 00:11:34,440 where AI goes these days. And by the way, you bring up 196 00:11:35,120 --> 00:11:39,840 new, new terms, they may not be new to you, but they I think 197 00:11:39,840 --> 00:11:42,040 will be new to every to some of the readers. 198 00:11:42,200 --> 00:11:47,000 But things like, you know, AI drift and us being a part of 199 00:11:47,000 --> 00:11:51,040 ensuring that we are setting proper boundaries for something 200 00:11:51,040 --> 00:11:56,520 so our solution doesn't drift off and, you know, and, and do 201 00:11:56,520 --> 00:11:58,200 things we don't intend for it to do. 202 00:11:58,200 --> 00:12:02,840 So it's yeah, it's, it's in part knowing some new words, but 203 00:12:02,840 --> 00:12:07,120 really having to think about, I forget how you describe it, but 204 00:12:07,120 --> 00:12:13,040 we are working more in the realm of behavior as opposed to I am 205 00:12:13,040 --> 00:12:17,440 defining A predictable box that always does X. 206 00:12:17,480 --> 00:12:21,800 And now we are probabilistic. Is that the word that you used? 207 00:12:22,200 --> 00:12:25,520 Yeah. So, you know, when we when we 208 00:12:25,520 --> 00:12:32,480 introduce AI, we're introducing probabilistic behavior, which if 209 00:12:32,480 --> 00:12:36,000 we back up the antidote that is deterministic behavior, which is 210 00:12:36,000 --> 00:12:38,320 the world we've always worked in, right? 211 00:12:38,800 --> 00:12:43,000 For many of us in terms of the requirements are very little 212 00:12:43,240 --> 00:12:44,600 literal. They're binary. 213 00:12:44,600 --> 00:12:46,680 This input always equals this output. 214 00:12:47,800 --> 00:12:53,400 And probabilistic, when we leverage AI says, I can give you 215 00:12:53,400 --> 00:12:56,800 a same input and you might give me a different output every 216 00:12:56,800 --> 00:13:00,240 time, which is kind of scary for a lot of us as BAS, right? 217 00:13:00,880 --> 00:13:04,880 Because we can't test to a binary outcome at the same time. 218 00:13:05,240 --> 00:13:08,800 What we have to do is elevate our thinking to wait a second. 219 00:13:09,120 --> 00:13:13,480 There's opportunity here, right? So what steps in a business 220 00:13:13,480 --> 00:13:16,920 process would benefit and get a better outcome in a 221 00:13:16,920 --> 00:13:20,200 probabilistic nature? And a really classic example is 222 00:13:20,200 --> 00:13:23,920 a chat, right? So chat box can be deterministic 223 00:13:24,400 --> 00:13:29,200 when the customer says this, say exactly this back or 224 00:13:29,200 --> 00:13:32,000 deterministic where it's, I'm going to infer what I think the 225 00:13:32,000 --> 00:13:38,000 customer is saying and I'm going to respond with a degree of 226 00:13:38,000 --> 00:13:40,480 latitude as well. And it won't be exactly the same 227 00:13:40,480 --> 00:13:43,400 every single time. And that gives some benefits 228 00:13:43,400 --> 00:13:47,360 because we can detect emotion that we AI, right? 229 00:13:48,040 --> 00:13:50,320 Gosh, yeah. I guess we're embedded in part 230 00:13:50,320 --> 00:13:52,160 of the world if I'm saying we always like. 231 00:13:53,160 --> 00:13:57,520 Where are you Angela? Are you in the in reality or are 232 00:13:57,560 --> 00:14:00,600 you in AI? Yeah. 233 00:14:00,800 --> 00:14:05,200 So there's a whole new world of analysis for us to jump into 234 00:14:05,320 --> 00:14:10,960 that says what discrete interactions and pieces of a 235 00:14:10,960 --> 00:14:16,560 process can benefit from a probabilistic nature where we 236 00:14:16,560 --> 00:14:22,200 give an AI to a leeway to decide how to respond, where it can 237 00:14:22,200 --> 00:14:26,520 detect a nuance. And to me, that's a really 238 00:14:26,520 --> 00:14:30,440 exciting part about this, right, is how do we analyze a process, 239 00:14:30,440 --> 00:14:34,200 break it down step by step and say, OK, now which aspects are 240 00:14:34,200 --> 00:14:36,440 we going to have as deterministic, rule driven 241 00:14:36,440 --> 00:14:38,840 binary? And which aspects are we going 242 00:14:38,840 --> 00:14:42,040 to leverage this probabilistic nature of things that will 243 00:14:42,320 --> 00:14:44,640 benefit to get more value, more outcomes? 244 00:14:45,920 --> 00:14:50,320 And it's kind of exciting to think about, right, and design. 245 00:14:50,880 --> 00:14:55,320 Yes, and I think so and you use the word design there because I 246 00:14:55,320 --> 00:14:58,640 and I think that this is really important because we are not 247 00:14:59,160 --> 00:15:01,360 coming up with the static solutions anymore. 248 00:15:01,360 --> 00:15:05,360 So if we are thinking about the design almost of an experience 249 00:15:06,080 --> 00:15:10,440 it we really have to think about where we get involved 250 00:15:10,560 --> 00:15:13,480 differently. And we've always had the, you 251 00:15:13,480 --> 00:15:17,080 know, the argument that we don't get brought in early enough in 252 00:15:17,080 --> 00:15:23,360 our projects where AI demands our presence is as early as 253 00:15:23,360 --> 00:15:25,280 possible. Kind of for the same reason that 254 00:15:25,280 --> 00:15:28,280 we say you want to get your business analysis like you're 255 00:15:28,680 --> 00:15:31,920 around cybersecurity, you need to get that done early, you need 256 00:15:31,920 --> 00:15:34,480 to be building it in. And I think you say the same 257 00:15:34,480 --> 00:15:39,000 thing too about our our involvement in AI agentic AI 258 00:15:39,000 --> 00:15:42,120 projects. Yeah, absolutely. 259 00:15:42,120 --> 00:15:45,120 And you know, there's in some ways you can look at it like 260 00:15:45,120 --> 00:15:47,080 we're going to put this agent into this process. 261 00:15:47,080 --> 00:15:48,960 Other ways you can look at it like we need to completely 262 00:15:48,960 --> 00:15:54,680 redesign the process, right? And yeah, we got to be involved 263 00:15:54,960 --> 00:15:56,880 early on and understand the metrics. 264 00:15:56,880 --> 00:15:59,520 We have to track the evidence and the data to figure out how 265 00:15:59,520 --> 00:16:02,560 is it performing, how has it been performing, Where are the 266 00:16:02,560 --> 00:16:06,240 biggest areas for improvement. And so there's so many realms of 267 00:16:06,400 --> 00:16:08,080 AI involved in all of this, right? 268 00:16:08,080 --> 00:16:10,400 There's all the data analytics that have been around for a 269 00:16:10,400 --> 00:16:13,320 while, right? And machine learning has been 270 00:16:13,320 --> 00:16:16,360 around a long time and leveraging where do you put that 271 00:16:16,360 --> 00:16:18,520 into the back end part of the processes? 272 00:16:18,520 --> 00:16:22,560 Where does that info and results surface and insight surface into 273 00:16:22,560 --> 00:16:25,400 a user experience? And then where do we use all 274 00:16:25,400 --> 00:16:29,680 these other AI capabilities to enhance the journey overall? 275 00:16:30,000 --> 00:16:34,680 So exactly what we're talking about is business analysis being 276 00:16:34,840 --> 00:16:37,560 elevated such that we're seeing the bigger picture of the full 277 00:16:37,560 --> 00:16:41,000 journey, the full value stream because I'll bring back that 278 00:16:41,000 --> 00:16:47,160 term AI drift. Even one piece of AI embedded 279 00:16:47,160 --> 00:16:52,480 into an end to end customer workflow or journey or user can 280 00:16:52,480 --> 00:16:56,080 proliferate and exacerbate downstream. 281 00:16:56,360 --> 00:17:00,000 And as B as we are used to seeing that a deterministic 282 00:17:00,000 --> 00:17:04,240 binary result proliferated, but with AI when it's a 283 00:17:04,640 --> 00:17:07,640 probabilistic result proliferated. 284 00:17:07,839 --> 00:17:11,000 And then what if you have multiple probabilistic results 285 00:17:11,000 --> 00:17:14,560 like? So the analysis end to end is 286 00:17:14,560 --> 00:17:21,000 what's so interesting to me and where we have to level up and 287 00:17:22,319 --> 00:17:27,119 and refrain from analyzing within a silo that a lot of us 288 00:17:27,119 --> 00:17:28,680 are in. Yeah. 289 00:17:29,040 --> 00:17:31,960 Unless there are very. I had a really interesting 290 00:17:31,960 --> 00:17:35,360 discussion yesterday with someone about and they're using 291 00:17:35,360 --> 00:17:40,920 AIA lot and it was about, you know, can you work in silos 292 00:17:40,920 --> 00:17:44,200 effectively or not organizationally with AI. 293 00:17:45,160 --> 00:17:48,440 And the discussion went into well, it depends on your dev OPS 294 00:17:48,440 --> 00:17:52,640 infrastructure and how well the silos are boundary with 295 00:17:52,680 --> 00:17:55,800 agreements between silos of processes. 296 00:17:56,280 --> 00:18:02,120 I know it's really nerdy, right? And so it's just fascinating and 297 00:18:02,120 --> 00:18:05,080 I'm listening to this and I'm like, most companies don't do 298 00:18:05,080 --> 00:18:07,760 this. And so most companies are in 299 00:18:07,760 --> 00:18:11,600 silos but don't have these well established boundaries between 300 00:18:11,600 --> 00:18:13,560 technical and business process silos. 301 00:18:14,280 --> 00:18:16,920 So we have to look at it more end to end. 302 00:18:17,000 --> 00:18:20,600 Well, anyway, we do what's the value end to end, right? 303 00:18:21,680 --> 00:18:25,240 I think you were talking about, or perhaps I inferred this from 304 00:18:25,240 --> 00:18:29,840 the article, that we might still be thinking about AI development 305 00:18:29,840 --> 00:18:32,320 in the same way that we think about software development 306 00:18:33,120 --> 00:18:38,080 phased. Oh, or in that is like off the 307 00:18:38,080 --> 00:18:39,960 table now. Yeah. 308 00:18:40,040 --> 00:18:43,160 So let me back up. There's a 2 themes that were 309 00:18:43,160 --> 00:18:45,240 kind of intertwining between, right? 310 00:18:45,640 --> 00:18:49,160 So. As BAS, we talk a lot about the 311 00:18:49,160 --> 00:18:53,760 software development life cycle, the SDLC and how AI is changing 312 00:18:53,760 --> 00:18:57,680 that, right? And then the other aspect is how 313 00:18:57,680 --> 00:19:01,520 is AI changing our business processes through all AI 314 00:19:01,520 --> 00:19:04,360 capabilities that we can embed into business processes and 315 00:19:04,360 --> 00:19:09,040 redesign them So it's easy to get them mixed up and put 316 00:19:09,040 --> 00:19:12,680 together in different ways. But the, yeah, the software 317 00:19:12,680 --> 00:19:18,800 development life cycle is changing dramatically and partly 318 00:19:18,800 --> 00:19:21,240 because of the coding tools available with AI. 319 00:19:21,240 --> 00:19:26,800 And in the AI world, this is the first set of professions, the 320 00:19:26,800 --> 00:19:30,680 first set of processes that are being dramatically changed with 321 00:19:30,680 --> 00:19:33,920 AI and we're part of it. Obviously, we're impacted. 322 00:19:34,280 --> 00:19:40,080 So yes, we have to be really careful not to work in a silo as 323 00:19:40,080 --> 00:19:45,000 a business analyst. So what that means is we can't 324 00:19:45,000 --> 00:19:47,760 work in phases anymore. We have to work continuously 325 00:19:47,760 --> 00:19:53,280 more in parallel and in collaboration with developers. 326 00:19:53,280 --> 00:19:55,800 And a lot of people tell me, Oh yeah, I collaborate with 327 00:19:55,800 --> 00:20:01,840 developers, but I mean something like different so. 328 00:20:02,480 --> 00:20:04,760 Can I throw out there like I'm gonna throw out there a vision 329 00:20:04,760 --> 00:20:08,280 that I know there are plenty of BA teams already doing? 330 00:20:08,800 --> 00:20:11,920 OK, I'm giving you the floor. You're not. 331 00:20:12,320 --> 00:20:16,680 But you know, the vision that I think is going to become more 332 00:20:16,680 --> 00:20:18,760 and more common, or I'm already seen it become more and more 333 00:20:18,760 --> 00:20:21,760 common, is that when the business has an idea 334 00:20:22,480 --> 00:20:25,720 immediately, as fast as possible, and I mean, it could 335 00:20:25,720 --> 00:20:29,360 be within minutes, the business technology and BA are in the 336 00:20:29,360 --> 00:20:32,120 room together building a prototype together with an AI 337 00:20:32,120 --> 00:20:36,840 prototyping tool. And then we can reverse engineer 338 00:20:36,840 --> 00:20:43,080 specs from our prototyping working prototyping tool and get 339 00:20:43,080 --> 00:20:47,200 that into production quite fast. That might seem like what, 340 00:20:48,040 --> 00:20:49,440 right? What about the analysis? 341 00:20:50,160 --> 00:20:54,560 And what we're doing is we're facilitating decision making and 342 00:20:54,560 --> 00:20:59,320 analysis in parallel such that with the business using real 343 00:20:59,320 --> 00:21:02,800 working prototypes and then our business analysis, thinking 344 00:21:02,800 --> 00:21:07,520 about the structure discipline of users, scenarios, flows, data 345 00:21:07,520 --> 00:21:12,000 and rules and getting a scenario set together somewhat on the 346 00:21:12,000 --> 00:21:15,800 fly, somewhat right after that meeting back at our desks using 347 00:21:15,920 --> 00:21:18,000 AI to help us. In the meantime, the technical 348 00:21:18,000 --> 00:21:22,800 team is going, OK, I'm understanding what the business 349 00:21:22,800 --> 00:21:26,040 is asking for and thinking about the technical layers and how it 350 00:21:26,040 --> 00:21:30,960 fits into the architecture. And it's just a totally 351 00:21:30,960 --> 00:21:36,320 different way of looking at the SDLC such that the fastest we 352 00:21:36,320 --> 00:21:39,600 can get to a working prototype and then the more we work in 353 00:21:39,600 --> 00:21:42,920 parallel together, that's what it is. 354 00:21:42,920 --> 00:21:45,640 And then the continuous nature is really about evaluating and 355 00:21:45,640 --> 00:21:48,960 monitoring, where our famous knowledge area from the BA box 356 00:21:48,960 --> 00:21:52,120 solution evaluation and monitoring becomes a bigger 357 00:21:52,120 --> 00:21:56,560 circle back into elicitation. Yes, and you've got a great 358 00:21:56,560 --> 00:22:00,280 diagram also for that in the article, which I thought was 359 00:22:00,280 --> 00:22:03,200 really good in that. So you brought up a couple of 360 00:22:03,200 --> 00:22:05,120 things there. I feel like what you've 361 00:22:05,120 --> 00:22:11,800 described was always the dream with air quotes, agile and I 362 00:22:11,800 --> 00:22:16,520 don't know how close we all really got to that goal of I, I, 363 00:22:16,960 --> 00:22:21,720 but now we are talking. That's totally possible with AI. 364 00:22:22,040 --> 00:22:25,880 It's always been possible. I think that the trick is really 365 00:22:25,880 --> 00:22:29,760 going to be like, sure. Are we at risk for it never 366 00:22:30,160 --> 00:22:33,040 happening with some organizations this way as well? 367 00:22:33,040 --> 00:22:37,960 With AI, yes. I'm not sure those organizations 368 00:22:37,960 --> 00:22:41,320 will be around very long because, right, because 369 00:22:41,320 --> 00:22:43,120 organizations are already working in this way. 370 00:22:43,120 --> 00:22:45,960 And so their cost structures and their delivery time frames are 371 00:22:45,960 --> 00:22:49,760 just dramatically different. And the feedback cycle getting 372 00:22:49,760 --> 00:22:54,360 condensed just totally changes the value proposition, right? 373 00:22:54,680 --> 00:22:59,760 So I think what it is the yeah, it is, it's describing a much 374 00:22:59,760 --> 00:23:01,880 more agile, nimble way of working. 375 00:23:02,480 --> 00:23:06,640 And organizations that are not able to shift to that will 376 00:23:06,640 --> 00:23:10,080 suffer in terms of results, right, and outcomes. 377 00:23:11,040 --> 00:23:13,680 And that's where the org design matters. 378 00:23:13,680 --> 00:23:16,800 And but you know, the more organizations I talk to, a lot 379 00:23:16,800 --> 00:23:18,840 of them don't feel like they need to redesign the org. 380 00:23:18,840 --> 00:23:21,880 They just want their team to work together more and do this. 381 00:23:22,640 --> 00:23:25,240 But we all have to understand what that means and get out of 382 00:23:25,240 --> 00:23:27,800 our own way. Yeah, because I do think we are 383 00:23:27,800 --> 00:23:32,120 probably stuck in some patterns, which by the way, I think is one 384 00:23:32,120 --> 00:23:34,920 of the things that your article does well when it speaks to 385 00:23:34,920 --> 00:23:37,280 leaders and people kind of in this spot. 386 00:23:37,600 --> 00:23:42,360 You and we have all been just like we were all promised speed 387 00:23:42,400 --> 00:23:47,480 with agile like here AI gives, truly gives us speed. 388 00:23:48,720 --> 00:23:52,480 Do you think that have you seen with the any of the companies 389 00:23:52,480 --> 00:23:55,320 you've worked with? Are they giving any? 390 00:23:55,480 --> 00:23:58,520 Are they only focused on speed in? 391 00:23:59,000 --> 00:24:04,000 Is that impacting at all the outcomes that you're seeing? 392 00:24:04,880 --> 00:24:07,960 Oh, absolutely right. Some are, and they're paying for 393 00:24:07,960 --> 00:24:11,800 it, right? Especially with agentic AI, one 394 00:24:11,800 --> 00:24:17,000 of the common things you see is teams to build AI agents, but 395 00:24:17,000 --> 00:24:20,360 not building something called human in the loop and governance 396 00:24:20,360 --> 00:24:24,200 processes around them, which is super important and another huge 397 00:24:24,200 --> 00:24:28,880 opportunity for BAS, right? So AI agents in a business 398 00:24:28,880 --> 00:24:33,600 process, the amount of monitoring and proactive 399 00:24:33,600 --> 00:24:38,320 monitoring of the agents work increases hugely. 400 00:24:39,000 --> 00:24:43,000 And that means workflows and people who have to monitor and 401 00:24:43,000 --> 00:24:47,720 be empowered to make decisions and monitor and that those are 402 00:24:47,720 --> 00:24:51,400 additional workflows, right. So it's not just a report that 403 00:24:51,400 --> 00:24:55,240 someone gets every week or every month or an escalation thrown to 404 00:24:55,240 --> 00:24:57,520 their inbox. I mean, there has to be much 405 00:24:57,520 --> 00:25:02,880 more robust monitoring and roles of people supervising the agent 406 00:25:02,880 --> 00:25:08,640 with transparency and explain ability into it and processes to 407 00:25:08,640 --> 00:25:14,520 view, manage, pull things out of the agentic process, put them 408 00:25:14,520 --> 00:25:18,000 back in, escalate, change, right? 409 00:25:18,000 --> 00:25:21,120 Like whole processes, Who's going to do it? 410 00:25:21,120 --> 00:25:23,560 What permissions do they need? What data do they need to see? 411 00:25:23,600 --> 00:25:25,120 What actions do they need to take? 412 00:25:25,120 --> 00:25:27,000 What rules and boundaries do we have? 413 00:25:27,280 --> 00:25:30,560 How does it get integrated back into the automation process? 414 00:25:32,120 --> 00:25:33,760 That's a. Whole project itself. 415 00:25:34,320 --> 00:25:36,880 Yeah. And it, it is it, it's A and you 416 00:25:36,880 --> 00:25:39,080 know what? It's almost as you describe it. 417 00:25:39,080 --> 00:25:43,520 It is a new reality because I don't know that we all, even 418 00:25:43,520 --> 00:25:46,600 when we build these deterministic systems, I don't 419 00:25:46,600 --> 00:25:49,560 know that I've been involved in a lot of projects where I think 420 00:25:49,560 --> 00:25:54,640 about the human stuff that's got to happen afterwards. 421 00:25:55,000 --> 00:26:01,120 Yeah, here that's exclusively a whole piece of work because now 422 00:26:01,120 --> 00:26:03,880 I am excluding humans from parts of it. 423 00:26:04,320 --> 00:26:07,640 Now I need to know where the humans have been involved 424 00:26:07,640 --> 00:26:09,840 because I've somehow got to replace them. 425 00:26:10,080 --> 00:26:12,840 You talk about implicit knowledge that one of the things 426 00:26:12,840 --> 00:26:16,240 that we will have to do is business analysis professionals 427 00:26:16,360 --> 00:26:19,640 is not just help to determine all these guidelines and rules 428 00:26:19,640 --> 00:26:22,800 of engagement, but also we've got to go find the knowledge 429 00:26:22,800 --> 00:26:26,400 that nobody has written down, that people just know to do. 430 00:26:26,400 --> 00:26:29,920 Like that's a the whole reality of things that we've got it now 431 00:26:30,240 --> 00:26:34,240 find and build. It's more prevalent in legacy 432 00:26:34,240 --> 00:26:36,840 large organizations too, where you typically find business 433 00:26:36,840 --> 00:26:38,600 analysts. Yeah, right. 434 00:26:39,760 --> 00:26:42,240 Like a lot of the big tech companies will be like, Oh yeah, 435 00:26:42,240 --> 00:26:43,920 it's super easy to build an agent. 436 00:26:43,920 --> 00:26:46,680 You just do this, drag and drop this, describe this. 437 00:26:47,120 --> 00:26:50,440 But then somewhere in that presentation, somewhere they 438 00:26:50,440 --> 00:26:53,720 will mention ever so slightly, as long as your process is 439 00:26:53,720 --> 00:26:58,480 perfectly documented and known, which any of us in the VA world 440 00:26:58,480 --> 00:27:02,880 know that that's a far cry from reality and most organizations 441 00:27:02,880 --> 00:27:04,800 that we work with. Right. 442 00:27:04,840 --> 00:27:08,920 Well, and again, another opportunity for us that you talk 443 00:27:08,920 --> 00:27:13,800 about is all of this does depend on there being documentation. 444 00:27:13,800 --> 00:27:17,400 And if we have legacy systems, nobody updated that 445 00:27:17,400 --> 00:27:19,240 documentation. You probably don't have the 446 00:27:19,240 --> 00:27:21,560 whole thing documented end to end anyway. 447 00:27:21,560 --> 00:27:24,720 And you certainly know, don't know about Barbara's checklist 448 00:27:24,720 --> 00:27:29,120 that she keeps on her desk where she works around stuff or knows 449 00:27:29,120 --> 00:27:31,800 the secret codes to get a transaction through. 450 00:27:33,040 --> 00:27:35,720 It's a whole different. It's a whole different reality 451 00:27:36,000 --> 00:27:41,680 that somebody us needs to be accountable for and it has the 452 00:27:41,680 --> 00:27:44,680 skill set. But this, I think one thing 453 00:27:44,680 --> 00:27:48,560 that's really important for our community to understand is that 454 00:27:48,560 --> 00:27:54,240 doesn't mean that we're necessarily should or going to 455 00:27:54,240 --> 00:27:59,320 get some current state phase to document it all right? 456 00:27:59,320 --> 00:28:02,120 Like I know we're used to that after decades and decades, but 457 00:28:02,480 --> 00:28:05,520 even about 15 years ago I started seeing that fall apart 458 00:28:05,520 --> 00:28:08,640 where projects were being cancelled if BAS took too long 459 00:28:08,640 --> 00:28:11,760 to document the current state because people wanted to see 460 00:28:11,760 --> 00:28:14,760 progress, they don't want to keep paying to document the 461 00:28:14,760 --> 00:28:17,640 current state. Well OK, that pain point just 462 00:28:17,640 --> 00:28:23,520 got a lot easier because we can have AI read the code bases and 463 00:28:23,520 --> 00:28:25,320 tell us what the current state is. 464 00:28:25,320 --> 00:28:29,000 Wait a second though, we still have that checklist on Barb's 465 00:28:29,000 --> 00:28:32,480 desk. You know, the non systematic 466 00:28:32,480 --> 00:28:35,480 things that are happening. So then it's like, well then OK, 467 00:28:35,480 --> 00:28:38,920 do we need a phase to then, OK, let's reverse engineer the code. 468 00:28:39,040 --> 00:28:41,280 AI tells us what the current state is of the code. 469 00:28:42,840 --> 00:28:45,640 OK, now we have to go figure out where's the human things 470 00:28:45,640 --> 00:28:48,040 happening. But there's something else 471 00:28:48,200 --> 00:28:52,200 because I still think we can do it faster than a phase. 472 00:28:52,840 --> 00:28:56,400 And that is scenario and user group identification, which has 473 00:28:56,400 --> 00:29:00,560 always been about practice in our world, but not practice 474 00:29:01,000 --> 00:29:02,680 commonly enough. OK. 475 00:29:03,120 --> 00:29:07,000 We tend to start with systems instead of people, but if we 476 00:29:07,000 --> 00:29:10,760 take a, a people user 1st and then we can say, OK, we're going 477 00:29:10,760 --> 00:29:13,360 to focus on this user group and these scenarios. 478 00:29:14,120 --> 00:29:17,160 It doesn't all have to be done before the project starts to 479 00:29:17,160 --> 00:29:20,960 make progress in development. So really learning how we can 480 00:29:20,960 --> 00:29:25,560 work differently to chunk out and continuously analyze in a 481 00:29:25,560 --> 00:29:30,080 structured disciplined way through users, user group 482 00:29:30,080 --> 00:29:35,960 scenarios and scope incrementally through this is a 483 00:29:35,960 --> 00:29:38,960 lot another example of that continuing of continuous 484 00:29:38,960 --> 00:29:42,760 analysis. Yeah, I'm, as you're talking 485 00:29:42,760 --> 00:29:48,320 about some of the realities of business analysis with AI, the 486 00:29:48,320 --> 00:29:52,560 good news is I think I don't hear you talking about a lot of 487 00:29:52,920 --> 00:29:56,800 crazy new techniques or skills. In fact, I hear you saying, 488 00:29:57,160 --> 00:30:00,320 well, those things that we've been doing, we'll still need to 489 00:30:00,320 --> 00:30:03,840 do them. I hear you saying though we 490 00:30:03,840 --> 00:30:05,800 might, we need to do them earlier. 491 00:30:06,080 --> 00:30:10,920 The focus of where we start something needs to be different 492 00:30:11,280 --> 00:30:16,200 and there might be new areas of analysis that we need to focus 493 00:30:16,200 --> 00:30:18,400 on that we have not previously thought of. 494 00:30:19,360 --> 00:30:22,800 Do you think is that fair it and do you think there are new 495 00:30:22,800 --> 00:30:29,080 skills competencies we need? Yeah, it's tricky because as 496 00:30:29,080 --> 00:30:32,680 much as it is about leveraging all these current techniques and 497 00:30:32,680 --> 00:30:36,240 techniques we've always used, the biggest danger to that 498 00:30:36,240 --> 00:30:40,640 statement is people think they don't need to change because the 499 00:30:40,640 --> 00:30:43,760 shift is massive. Yes, we're going to still use a 500 00:30:43,760 --> 00:30:46,600 lot of these thinking patterns and cognitive patterns we've 501 00:30:46,720 --> 00:30:50,160 always used to get through complexity and to understand the 502 00:30:50,160 --> 00:30:53,520 relationship between people, work flows, data, rules and 503 00:30:53,520 --> 00:30:58,800 technology. The trick is we have to execute 504 00:30:58,800 --> 00:31:04,720 on them very differently. It's, it's not about a phase and 505 00:31:05,160 --> 00:31:09,440 our PC with a Vizio license, right. 506 00:31:11,160 --> 00:31:15,520 It's about using AI as a thinking partner to get that 507 00:31:15,520 --> 00:31:19,400 done and often doing that live in the room with business and 508 00:31:19,400 --> 00:31:24,520 technical folks present and knowing when that's happening 509 00:31:24,520 --> 00:31:27,440 and the reaction to it. What parts of those 510 00:31:27,440 --> 00:31:32,360 conversations matter to shaping decision making at velocity? 511 00:31:33,440 --> 00:31:36,200 It's funny, 'cause I'm, you know, like anything with big 512 00:31:36,200 --> 00:31:38,120 change, aren't we all new to articulating all of. 513 00:31:38,440 --> 00:31:40,680 This right? Well, that's OK. 514 00:31:40,840 --> 00:31:45,160 Also, you you used the term decision velocity a long time 515 00:31:45,160 --> 00:31:48,520 ago and I quote you on that because I like that concept. 516 00:31:49,120 --> 00:31:50,720 I wish it would have made it in the future. 517 00:31:51,280 --> 00:31:52,200 Book. It's almost a. 518 00:31:52,560 --> 00:31:58,120 Term that came out of my mouth while describing the book after 519 00:31:58,120 --> 00:32:01,080 the final proof had been submitted for print AM. 520 00:32:02,000 --> 00:32:04,320 I seeing a second edition, maybe I. 521 00:32:04,760 --> 00:32:08,640 Don't know about that, but like decision velocity, right? 522 00:32:08,640 --> 00:32:14,800 That's really huge term because there's a lot of places I CBA is 523 00:32:14,800 --> 00:32:17,480 needing to use it. And the first one is an intake 524 00:32:18,240 --> 00:32:21,040 all the ideas that come in. So it's AI doing the 525 00:32:21,040 --> 00:32:23,560 development. Wow. 526 00:32:23,600 --> 00:32:28,280 We can take every idea and build it quickly, but what actually 527 00:32:28,280 --> 00:32:30,120 matters? What? 528 00:32:30,600 --> 00:32:34,520 So I feel like in some organizations, business analysis 529 00:32:34,520 --> 00:32:37,560 is part of the intake process. Intake, right? 530 00:32:38,000 --> 00:32:41,200 Analyzing all the requests that come in, which ones should we be 531 00:32:41,200 --> 00:32:44,920 doing and in what order and in what priority and analyzing them 532 00:32:44,920 --> 00:32:48,440 holistically for their relationship to other ideas, 533 00:32:48,440 --> 00:32:52,720 projects, etcetera. The intake process, the question 534 00:32:52,720 --> 00:32:56,120 is, OK, wait, so we could literally take an intake item 535 00:32:56,600 --> 00:33:00,400 and have it developed in a couple of hours conceivably, 536 00:33:01,400 --> 00:33:05,040 should we? So I feel like our business 537 00:33:05,040 --> 00:33:08,160 analysis is going to have to shift left up into this intake 538 00:33:08,160 --> 00:33:13,680 process to say what is our process to evaluate ideas 539 00:33:13,800 --> 00:33:17,800 quickly and quickly identify which ones we should be doing 540 00:33:18,120 --> 00:33:21,560 and get to that rapid AI prototyping phase as fast as 541 00:33:21,560 --> 00:33:23,120 possible. But we need to make sure we have 542 00:33:23,120 --> 00:33:26,000 a good problem or opportunity to find an evidence behind it. 543 00:33:27,040 --> 00:33:32,160 And so that whole process to me is a big area for us. 544 00:33:32,600 --> 00:33:36,280 And then knowing that the requirements piece in the middle 545 00:33:36,280 --> 00:33:39,760 is highly automated. Now with AI, it's that bringing 546 00:33:39,760 --> 00:33:43,680 that downstream with developers and making sure that strategic 547 00:33:43,680 --> 00:33:47,760 alignment and the detailed scenarios play out the way we 548 00:33:47,760 --> 00:33:52,200 want them to, as intended, evaluating it as it goes through 549 00:33:52,200 --> 00:33:54,160 development, testing and in production. 550 00:33:55,360 --> 00:33:59,520 And so decision velocity comes into play with like, well, how 551 00:33:59,520 --> 00:34:03,760 are we helping others make decisions fast on which projects 552 00:34:03,760 --> 00:34:06,400 and features and defects and enhancements to do? 553 00:34:07,080 --> 00:34:09,320 And then which scenarios are in scope and in what order? 554 00:34:09,320 --> 00:34:12,120 And how do we incrementally and iteratively bring them through? 555 00:34:12,120 --> 00:34:17,000 Like all those decisions, where do we have this rule conflicts 556 00:34:17,000 --> 00:34:18,600 and how are we going to make those decisions? 557 00:34:18,600 --> 00:34:21,080 Like the amount of decisions? Oh my gosh. 558 00:34:22,120 --> 00:34:26,600 Well, OK, Susan, you talked to any group of BAS today and what 559 00:34:26,600 --> 00:34:28,320 do they struggle getting decisions made? 560 00:34:29,960 --> 00:34:32,199 They can't get decisions made from their business leaders that 561 00:34:32,199 --> 00:34:34,040 makes the requirements late and blah, blah. 562 00:34:34,840 --> 00:34:39,120 So this is a skill we're really going to have to get into is our 563 00:34:39,120 --> 00:34:41,239 new skills. So we have AI literacy. 564 00:34:41,239 --> 00:34:43,400 We've got to think about how to do our work differently. 565 00:34:43,400 --> 00:34:45,360 We have to use different tools. We have to. 566 00:34:46,280 --> 00:34:51,679 So there's thought threads of our legacy BA techniques that 567 00:34:51,679 --> 00:34:55,600 run through it, but how we thread them through all of the 568 00:34:55,600 --> 00:34:58,680 new things we need to be thinking about and tools and 569 00:34:58,680 --> 00:35:02,880 literacy and the way projects run is the shift. 570 00:35:02,880 --> 00:35:07,000 That to me is really big. Yeah, it is because it leans so 571 00:35:07,000 --> 00:35:12,840 heavily into, I hate the term soft skills, but human skills, 572 00:35:12,840 --> 00:35:16,800 let's call them human skills. What I hear you saying is that 573 00:35:16,800 --> 00:35:20,080 the new reality for us is leaning heavily onto our human 574 00:35:20,080 --> 00:35:24,840 skills of things like discretion, validation, so 575 00:35:24,840 --> 00:35:26,880 critical thinking, systems thinking. 576 00:35:26,880 --> 00:35:29,080 We've got dealing with ambiguity. 577 00:35:29,080 --> 00:35:33,040 We've got, you know, I don't know in any number of these 578 00:35:33,040 --> 00:35:38,240 skills that I think a lot of legacy organizations have said, 579 00:35:38,240 --> 00:35:40,520 I just need for you to write stuff down. 580 00:35:40,600 --> 00:35:44,760 And so some of us have been overburdened by, OK, my role is 581 00:35:44,760 --> 00:35:46,840 to show up with a pen and a piece of paper. 582 00:35:47,080 --> 00:35:50,240 And now what we're saying is that's we don't even need you to 583 00:35:50,240 --> 00:35:52,400 do that because we've got something that can do that. 584 00:35:52,400 --> 00:35:57,400 We need you to bring your highest, best set of human 585 00:35:57,400 --> 00:36:02,240 skills to help us think through this non human thing that is 586 00:36:02,640 --> 00:36:04,800 changing our organization like that. 587 00:36:05,320 --> 00:36:07,400 Yeah. So like when this comes to life, 588 00:36:07,400 --> 00:36:11,560 it's like you're shaping conversations in order to make 589 00:36:11,560 --> 00:36:14,120 decisions. And when our stakeholders 590 00:36:14,520 --> 00:36:17,560 struggle to make decisions, there's usually a few things at 591 00:36:17,560 --> 00:36:20,240 play. A the wrong decision is when 592 00:36:20,240 --> 00:36:22,880 you're asking to be made. So when leaders and business 593 00:36:22,880 --> 00:36:25,760 stakeholders won't make decisions for us, when we're 594 00:36:25,760 --> 00:36:29,400 waiting on those decisions, we often get frustrated and think 595 00:36:29,400 --> 00:36:31,480 I'm waiting on them. Oh my gosh, how am I going to 596 00:36:31,480 --> 00:36:32,720 get all this done by my deadline? 597 00:36:32,720 --> 00:36:34,120 I didn't get all these decisions. 598 00:36:34,840 --> 00:36:38,520 And a lot of times we simply haven't put the right decision 599 00:36:38,520 --> 00:36:41,400 in front of them. So we think it's the one thing 600 00:36:41,400 --> 00:36:45,040 holding us back. But in reality, they read the 601 00:36:45,040 --> 00:36:47,240 decision in the e-mail or whatever and they're like, 602 00:36:47,720 --> 00:36:49,960 either they don't understand the decision to be made or they 603 00:36:49,960 --> 00:36:51,920 don't agree it's the right decision, but they were too busy 604 00:36:51,920 --> 00:36:55,640 to reframe it and meet with you. And the other thing is decision 605 00:36:55,640 --> 00:36:59,160 timing. So it could be either too early 606 00:36:59,240 --> 00:37:02,160 to make the decision or maybe too late to make the decision 607 00:37:02,360 --> 00:37:05,160 and dealing with those things and how to build consensus and 608 00:37:05,160 --> 00:37:08,960 get alignment and right. So the right decision at the 609 00:37:08,960 --> 00:37:11,960 right time with the right people, is it the right person 610 00:37:12,360 --> 00:37:14,960 to be making the decision with the right people as input? 611 00:37:15,400 --> 00:37:18,080 And then do we have the right context for them to make it? 612 00:37:18,080 --> 00:37:21,720 So I think decisions don't get made off and because we don't 613 00:37:21,720 --> 00:37:25,000 have the right context in front of the decision maker when they 614 00:37:25,000 --> 00:37:28,520 don't have the right data, the right risk information. 615 00:37:29,280 --> 00:37:33,760 So yeah, all of those. And when you think about that 616 00:37:33,760 --> 00:37:36,360 weaves in with everything else we do, right? 617 00:37:36,360 --> 00:37:39,160 We are going to need to do process models and analysis and 618 00:37:39,160 --> 00:37:43,640 decision rule analysis and all these things that we do to get 619 00:37:43,640 --> 00:37:48,520 that context and to evaluate and shape the timing and path of 620 00:37:48,520 --> 00:37:52,480 these decisions. It's a lot of leadership skills. 621 00:37:53,080 --> 00:37:55,040 It is a lot of leadership skills. 622 00:37:56,080 --> 00:38:02,840 And so I, you know, I wonder where do you see new BAS, entry 623 00:38:02,840 --> 00:38:06,200 level BAS fitting into this? Because so much of what you, 624 00:38:06,320 --> 00:38:11,920 what we've just talked about to me are pretty senior skills, but 625 00:38:12,600 --> 00:38:16,080 we need, we, we've got to grow those junior BAS from somewhere. 626 00:38:16,160 --> 00:38:18,520 How do they fit into all of this? 627 00:38:19,000 --> 00:38:23,640 It's a very good question, you know, and it's a discussion that 628 00:38:23,640 --> 00:38:26,120 comes up a lot. I think I've had this question 629 00:38:26,120 --> 00:38:28,480 come up three times this week already in various 630 00:38:28,480 --> 00:38:35,280 conversations, right. So the good news is knowledge is 631 00:38:35,280 --> 00:38:40,920 more accessible than ever with AI and then AI connected to all 632 00:38:40,920 --> 00:38:45,440 the enterprise knowledge. The learning curve to get at the 633 00:38:45,440 --> 00:38:49,480 knowledge our junior BAS will find a much easier time with. 634 00:38:50,520 --> 00:38:54,200 OK. One aspect now the judgement 635 00:38:54,200 --> 00:38:59,560 about that takes many years to develop, but then we think about 636 00:38:59,560 --> 00:39:01,880 what about all these special TBA skills? 637 00:39:01,880 --> 00:39:07,760 How are we going to teach someone to model well, AI can 638 00:39:07,760 --> 00:39:11,240 model for them. Question is, do they recognize 639 00:39:11,240 --> 00:39:17,240 when the model is not optimized? Like lots of process models, for 640 00:39:17,240 --> 00:39:20,280 example, that I've done with AI, when I sit back and look at 641 00:39:20,280 --> 00:39:23,240 them, it's like, that's a horrible process. 642 00:39:23,320 --> 00:39:27,640 It's so good, the research. Can get into this, right? 643 00:39:28,200 --> 00:39:32,400 Yeah, it modeled it for me. But the tricky part's going to 644 00:39:32,400 --> 00:39:37,920 be teaching the juniors how to think like our thought process. 645 00:39:38,520 --> 00:39:43,880 So we have kind of a new mandate as senior BAS to bring junior 646 00:39:43,880 --> 00:39:48,640 BAS along with thinking out loud in front of them. 647 00:39:50,400 --> 00:39:55,280 And so the I think the better that type of thing happens, the 648 00:39:55,280 --> 00:39:59,120 quicker they're going to learn. But they don't need the 649 00:39:59,120 --> 00:40:02,480 knowledge uptake that has all, you know, been present for a lot 650 00:40:03,080 --> 00:40:05,720 of folks. They won't have to spend time. 651 00:40:05,720 --> 00:40:09,400 I mean, like when I was a junior BA, what kind of work did I get 652 00:40:09,400 --> 00:40:11,120 given? Yes, it was work that taught me 653 00:40:11,120 --> 00:40:14,080 these skills. But it was also a lot of grunt 654 00:40:14,080 --> 00:40:16,760 work that AI can now do. Yeah. 655 00:40:18,320 --> 00:40:25,320 So I think it's really hard for us to imagine junior BAS going 656 00:40:25,320 --> 00:40:27,440 through it quicker than we did, but I think it's totally 657 00:40:27,440 --> 00:40:29,040 possible. Yeah. 658 00:40:30,800 --> 00:40:33,400 And so for leaders that are listening to this, that is 659 00:40:33,400 --> 00:40:38,360 something for you to be thinking about is building those 660 00:40:38,360 --> 00:40:43,480 opportunities for those to develop, those junior folks that 661 00:40:43,760 --> 00:40:47,040 take advantage of the technology, but also don't short 662 00:40:47,040 --> 00:40:50,560 circuit important learning, especially around the soft 663 00:40:50,560 --> 00:40:54,960 skills like in fact, creating programs that develop soft 664 00:40:54,960 --> 00:40:59,320 skills and build confidence there and getting the knowledge, 665 00:40:59,320 --> 00:41:03,880 you know, because they can get knowledge at at a good pace. 666 00:41:03,880 --> 00:41:07,160 It's the human stuff that becomes the focus, I think. 667 00:41:07,800 --> 00:41:11,320 Yeah, I mean. How we learned it too, right? 668 00:41:11,320 --> 00:41:14,400 We had to have role models like to learn how to facilitate 669 00:41:14,400 --> 00:41:17,520 highly interactive, engaging requirements workshop. 670 00:41:17,520 --> 00:41:21,960 I don't know about you, but I had to see it and experience it. 671 00:41:22,360 --> 00:41:24,240 Yes. That's how I learned. 672 00:41:24,400 --> 00:41:27,200 And then eventually, hey, I really like how that person's 673 00:41:27,200 --> 00:41:29,200 doing it. I want them to be my mentor with 674 00:41:29,200 --> 00:41:32,600 it and then they might invite me to be a note taker at one just 675 00:41:32,600 --> 00:41:35,080 so I can see a few more of these things and how they play out. 676 00:41:35,440 --> 00:41:37,720 And then the next release of a project, it's like, Hey, will 677 00:41:37,720 --> 00:41:40,480 you leave this small portion of this workshop, right? 678 00:41:40,480 --> 00:41:44,160 Like you have to kind of peel your way into those skills. 679 00:41:44,520 --> 00:41:48,280 And I think that's going to have to be a lot more focus to to 680 00:41:48,280 --> 00:41:51,240 learn those leadership skills and, you know, continuous 681 00:41:51,240 --> 00:41:56,080 mentoring, continuous feedback. We are super excited that we're 682 00:41:56,080 --> 00:41:59,520 going to be able to share this article because it's really, I 683 00:41:59,520 --> 00:42:03,120 mean, I was, I took like 5 pages of notes, Angela. 684 00:42:03,400 --> 00:42:06,560 So I like, yes, I loved every piece of it. 685 00:42:06,560 --> 00:42:10,320 I really did because I think it's, it is the kind of article 686 00:42:10,320 --> 00:42:14,360 that I think if you are a leader particularly that is thinking 687 00:42:14,360 --> 00:42:19,240 about how do I need to help the BAS in my organization really 688 00:42:19,240 --> 00:42:24,560 get ready or be ready for our AI initiatives, our agentic AI 689 00:42:24,560 --> 00:42:28,360 initiatives, like where do we need to help them focus, but 690 00:42:28,360 --> 00:42:31,440 also where can we give them confidence that they they've got 691 00:42:31,440 --> 00:42:33,800 their skills. Your article does that. 692 00:42:34,800 --> 00:42:36,400 It's a start. It's a start. 693 00:42:36,400 --> 00:42:40,800 It's a, it's a lot of shift. I've been, you know, teaching 694 00:42:40,880 --> 00:42:43,840 more about AI stuff and way beyond the surface of generative 695 00:42:43,840 --> 00:42:46,440 AI stuff. And it's really fun to Cbas have 696 00:42:46,440 --> 00:42:50,800 the light bulb moments and go, oh, and anything from literacy 697 00:42:50,800 --> 00:42:55,800 to the role shift with the SDLC process changes to understanding 698 00:42:55,800 --> 00:43:00,680 that AI integration and the agentic AI and really 699 00:43:00,680 --> 00:43:04,880 understanding and see the light bulb click of the complexity and 700 00:43:04,880 --> 00:43:09,200 the analysis and go, whoa, yes, we have to be part of this. 701 00:43:09,480 --> 00:43:13,040 But it's writing requirements documents like it used to be. 702 00:43:13,040 --> 00:43:17,760 There's it's truly analysis. And that's why I got excited. 703 00:43:17,760 --> 00:43:21,320 I made all those noises, by the way, when I was reading the your 704 00:43:21,320 --> 00:43:24,920 article, because I was that excited about the opportunities. 705 00:43:25,000 --> 00:43:27,680 And in some ways I was like, this is this, these are the 706 00:43:27,680 --> 00:43:30,680 kinds of things that we should always have been doing and 707 00:43:30,680 --> 00:43:35,280 always involved in. And now we have, I think, real, 708 00:43:35,280 --> 00:43:38,720 not just permission, like we really have a duty and it's 709 00:43:38,720 --> 00:43:41,720 evident. Yeah, I I agree. 710 00:43:41,720 --> 00:43:44,160 It's you and I both love yacht rock. 711 00:43:45,160 --> 00:43:48,640 Yes. And when during the time Tim and 712 00:43:48,640 --> 00:43:52,880 I are writing future proof and you know, when right after 713 00:43:52,920 --> 00:43:54,480 generative AI just came out, right? 714 00:43:54,480 --> 00:43:56,800 And I started to see the connection between generative AI 715 00:43:56,800 --> 00:44:00,000 and the previous forms of AI like analytical AI and machine 716 00:44:00,000 --> 00:44:02,240 learning, all that stuff and how it all links together. 717 00:44:03,800 --> 00:44:06,880 And I had this moment of like, Oh my gosh, this is it. 718 00:44:06,880 --> 00:44:11,120 This is it the we get to be the B as we want to be. 719 00:44:11,920 --> 00:44:16,120 And I remember having a very strong desire to to for my next 720 00:44:16,120 --> 00:44:19,960 conference session to play the Kenny Loggins song This is it 721 00:44:20,160 --> 00:44:24,280 from the yacht rocket genre. But I peeled back on that idea 722 00:44:24,280 --> 00:44:26,200 because I just didn't think anyone would quite be there with 723 00:44:26,200 --> 00:44:28,600 me on it. Like, right. 724 00:44:28,600 --> 00:44:31,680 But I feel like you. You're a singer, You're a 725 00:44:31,720 --> 00:44:33,920 singer. And I don't know if everybody 726 00:44:33,920 --> 00:44:36,440 knows that you sing. I mean, you don't seem like 727 00:44:36,440 --> 00:44:37,880 professionally or anything, but in the. 728 00:44:37,880 --> 00:44:41,400 Car in the shower occasionally currently, yeah. 729 00:44:42,480 --> 00:44:46,000 And. By myself a little bit I did in 730 00:44:46,000 --> 00:44:48,840 high school, I was that was kind of my big sport was yeah, I 731 00:44:49,280 --> 00:44:52,560 mean, yeah, but yeah, I think this is it. 732 00:44:52,560 --> 00:44:54,880 This is our moment, this. Is our moment. 733 00:44:55,280 --> 00:44:59,480 It is make no mistake who you are, make no mistake where we 734 00:44:59,480 --> 00:45:03,680 are. This is our time as BAS to like 735 00:45:03,720 --> 00:45:07,400 be exactly who we think we should be, who we want to be, 736 00:45:08,320 --> 00:45:13,440 the analysis, the influence decision velocity. 737 00:45:13,480 --> 00:45:19,360 I mean, we have a chance to step into a governance role. 738 00:45:19,360 --> 00:45:22,240 And I don't know if it's going to end up in in the business 739 00:45:22,240 --> 00:45:25,560 analysis or project management practices or where it'll end up. 740 00:45:26,200 --> 00:45:29,160 But you think about, I mean, of course I'm partial to business 741 00:45:29,160 --> 00:45:33,440 analysis, but like the intake of ideas, the feasibility, 742 00:45:33,440 --> 00:45:36,720 viability, the value and prioritization, decision making 743 00:45:37,440 --> 00:45:40,800 and the monitoring of solutions and figuring out where to focus 744 00:45:40,800 --> 00:45:44,320 our energy as an organization, that's all business analysis at 745 00:45:44,560 --> 00:45:47,240 many detailed levels too, right? Yes. 746 00:45:47,640 --> 00:45:51,640 Yes, and that that's really the stuff that I have always loved 747 00:45:51,640 --> 00:45:55,400 in this work is being at that level where I felt that I was 748 00:45:55,400 --> 00:46:00,040 most useful, which is what came through very clearly in this 749 00:46:00,040 --> 00:46:02,400 article. So if people read this article 750 00:46:02,560 --> 00:46:06,000 or if you're thinking about downloading it members and 751 00:46:06,000 --> 00:46:08,600 you're thinking you're going to be afraid this is not that kind 752 00:46:08,600 --> 00:46:10,880 of article. It is to me, it's really 753 00:46:10,880 --> 00:46:14,880 uplifting and there's lots of opportunities and possibilities. 754 00:46:14,880 --> 00:46:17,960 All right. Well, thank you for joining us 755 00:46:17,960 --> 00:46:21,200 today. If you are an IIBA member, I 756 00:46:21,200 --> 00:46:24,720 just want to let you know that article that we have talked 757 00:46:24,720 --> 00:46:28,560 about today, The evolving Role of Business Analysis in the Age 758 00:46:28,560 --> 00:46:32,080 of Enterprise Agentic AI. It is available for 759 00:46:32,080 --> 00:46:36,600 membersonlythroughiba.org. So download it. 760 00:46:36,800 --> 00:46:39,680 It is a great read. It's an easy read, and I think 761 00:46:39,680 --> 00:46:44,440 that you're going to feel really positive about where our skill 762 00:46:44,440 --> 00:46:47,400 set can take us into the age of AI. 763 00:46:47,400 --> 00:46:49,240 Thanks for joining us today. Bye. 764 00:46:50,320 --> 00:46:52,600 Thanks for listening. Do you have any questions, 765 00:46:52,600 --> 00:46:54,520 comments or thoughts about today's topic? 766 00:46:54,520 --> 00:46:57,360 We'd love to hear them. Drop us a review or leave a note 767 00:46:57,360 --> 00:46:59,880 in the comments. Then like, subscribe or share 768 00:46:59,880 --> 00:47:01,640 this podcast if you like what you heard. 769 00:47:01,640 --> 00:47:04,120 And hey, you can help us shape future episodes. 770 00:47:04,120 --> 00:47:05,840 What do you want to know more about? 771 00:47:05,840 --> 00:47:10,120 Send us an e-mail at Live at iiba.org with your ideas. 772 00:47:10,120 --> 00:47:11,920 See you again on our next episode, So.