WEBVTT NOTE Sentence-level transcript of https://www.youtube.com/watch?v=C2-v6wkHXhY NOTE One cue per sentence. Cue ids are the line anchors on /transcripts/C2-v6wkHXhY.html. A cue ends where the next begins, or 2 s after its last word. s1 00:00:01.309 --> 00:00:03.309 [music] s2 00:00:12.480 --> 00:00:15.240 Good afternoon everyone present here. s3 00:00:15.240 --> 00:00:22.680 It's It's been a It's been a great day and an absolute pleasure to be here. s4 00:00:22.960 --> 00:00:31.080 The topic of my talk today is everyone has AI who has the advantage? s5 00:00:31.080 --> 00:00:42.120 I'll be talking about what the orgs that are doing that are leading in AI with the implementation and adoption what are they doing differently? s6 00:00:42.120 --> 00:00:49.320 Um And so before that who am I and what's my POV? s7 00:00:49.320 --> 00:00:51.320 So, I'm Dr. Sid Patnekar. s8 00:00:51.320 --> 00:00:57.160 I am an AI technology and data exec in asset management. s9 00:00:57.160 --> 00:01:04.680 I've spent most of my tenure in hedge funds and investment and academic research. s10 00:01:04.680 --> 00:01:19.560 Uh what I've done and focused on is lead strategy and build specialized teams towards three main verticals, optimizing efficiency driving P&L and reducing costs. s11 00:01:19.560 --> 00:01:30.880 So, um I'll be following the discussion along with two hypothetical companies, company A and company B right at the slider below. s12 00:01:30.880 --> 00:01:42.440 Uh these are hypothetical, um you know, with with made-up workflows, but we'll be following their journey along um the rest of this presentation. s13 00:01:42.640 --> 00:01:47.400 So everyone has AI, who has the advantage? s14 00:01:47.800 --> 00:02:03.212 Um before we get started there, first let's see what the lay of the land is and recent studies have shown that only about 37% of enterprises that surveyed are showing a positive s15 00:02:03.212 --> 00:02:06.172 contribution to their EBIT. s16 00:02:06.172 --> 00:02:12.212 And only about 6% of those would be qualified as high performers. s17 00:02:12.212 --> 00:02:15.652 So, that's about 5% contribution to their EBIT. s18 00:02:15.652 --> 00:02:29.292 Other studies earlier in the year have shown that about 66% are saying they're having a productivity increase, but only about 40 and 20% are actually seeing cost reductions and s19 00:02:29.292 --> 00:02:30.892 revenue growth. s20 00:02:30.892 --> 00:02:39.372 So, as we know, access of AI is widespread, yet the impact remains uneven. s21 00:02:40.372 --> 00:02:42.372 So, s22 00:02:42.441 --> 00:02:43.332 [clears throat] s23 00:02:43.332 --> 00:02:51.972 the question I would ask, or how I think about this, is really in terms of three verticals. s24 00:02:51.972 --> 00:03:07.612 And the way I think about these three verticals should apply to organizations and individuals alike, and it should apply to functions regardless of where you are in that organization. s25 00:03:07.612 --> 00:03:15.572 Um So, the three main vertical pillars that I think of are number one, design the right problem. s26 00:03:15.572 --> 00:03:19.252 Number two, build shared capabilities. s27 00:03:19.252 --> 00:03:22.972 And number three, compound the advantage. s28 00:03:22.972 --> 00:03:31.692 Now, before we dive into each of these pillars, there is an idea that I think goes through all these verticals. s29 00:03:31.692 --> 00:03:38.572 And I think the best leading firms are taking advantage of that and capitalizing on it. s30 00:03:38.572 --> 00:03:44.772 As we know, throughout time, the only constant in this world has been change. s31 00:03:44.772 --> 00:03:52.172 What's different in the AI and superintelligence era is that intelligence has become cheap. s32 00:03:52.172 --> 00:04:03.694 Regardless of what your unit of intelligence is, whether it be software, analytics, recommendations, reports, AI is creating an abundance in all of it. s33 00:04:03.694 --> 00:04:10.614 And um as we know, more answers don't necessarily create more value. s34 00:04:10.614 --> 00:04:12.414 So, what is scarce? s35 00:04:12.414 --> 00:04:16.934 What is a scarce resource in an abundant world? s36 00:04:17.654 --> 00:04:22.774 What what are companies taking advantage of, the leaders at least? s37 00:04:22.814 --> 00:04:25.014 It's judgment. s38 00:04:25.374 --> 00:04:27.494 What is judgment? s39 00:04:27.494 --> 00:04:33.694 Judgment is choosing what matters and how to act when there's no single obvious answer. s40 00:04:33.694 --> 00:04:38.254 It's the creativity that lets you explore what possibilities are worth exploring. s41 00:04:38.254 --> 00:04:43.254 It's the taste that tells you what good looks like in your given context. s42 00:04:43.254 --> 00:04:49.814 It's the decisions where you can weigh what trade-offs are worth making. s43 00:04:49.814 --> 00:04:51.974 Judgment is ubiquitous. s44 00:04:51.974 --> 00:04:54.654 It exists throughout your organization. s45 00:04:54.654 --> 00:05:02.214 On a daily basis, your C-suite The C-suite is asking questions, taking advantage of judgment judgment all the time. s46 00:05:02.214 --> 00:05:08.574 So are the software engineers, the AI leaders, the portfolio managers, and the data scientists. s47 00:05:08.574 --> 00:05:25.574 So, the question that they're focused on is given judgment is a finite asset, a limited capacity, the main question becomes where is our best judgment worth spending? s48 00:05:25.574 --> 00:05:28.094 Like for you to hold on to that thought. s49 00:05:28.094 --> 00:05:30.494 With that, we'll dive into our first pillar. s50 00:05:30.494 --> 00:05:32.414 Design the right problem. s51 00:05:32.414 --> 00:05:45.374 So, questions uh a question that um the leading organizations are asking all the time is they're obsessed over what is the right problem to solve? s52 00:05:45.374 --> 00:05:45.894 Right? s53 00:05:45.894 --> 00:05:47.294 It's tempting to start with the tool. s54 00:05:47.294 --> 00:05:51.974 Today we're surrounded with better models, harnesses. s55 00:05:51.974 --> 00:05:53.574 We're surrounded with all these AI tools. s56 00:05:53.574 --> 00:05:55.894 We're surrounded with co-pilots. s57 00:05:55.894 --> 00:05:59.254 But that isn't the way about, is it? s58 00:05:59.254 --> 00:06:02.214 At least that's not the way the leaders are approaching it. s59 00:06:02.214 --> 00:06:05.535 Now, let's go back to our hypothetical um two companies. s60 00:06:05.535 --> 00:06:07.335 Let's say they're from asset management. s61 00:06:07.335 --> 00:06:16.335 Let's say, uh, we're tracking the workflow of two research analysts in these hypothetical, um, organizations. s62 00:06:16.335 --> 00:06:21.575 Well, let's say organization B starts with the tool, the co-pilot. s63 00:06:21.575 --> 00:06:25.695 They're asking, "Where can we add AI to the analyst work?" s64 00:06:25.695 --> 00:06:31.815 They're looking at the current existing process to say, "Earning transcript comes in. s65 00:06:31.815 --> 00:06:34.055 I discovered a summarization workflow. s66 00:06:34.055 --> 00:06:37.455 Let me pass all of this in through my fire hose. s67 00:06:37.455 --> 00:06:41.695 Analysts get earning summaries faster. s68 00:06:41.695 --> 00:06:43.495 Win-win, right? s69 00:06:43.495 --> 00:06:47.855 Well, not so much, at least not compared to the guys who are leading. s70 00:06:47.855 --> 00:06:54.255 And such a workflow of of being a faster horse introduces one key, um, nuance. s71 00:06:54.255 --> 00:07:01.895 That is, you are losing context as you have information, um, I guess reduction as part of your summarization. s72 00:07:01.895 --> 00:07:06.655 So, what What is the hypothetical organization A doing in this? s73 00:07:06.655 --> 00:07:09.055 Well, they're obsessed with the right problem to ask. s74 00:07:09.055 --> 00:07:14.575 They could be asking, "Why are our material signals reaching us too late? s75 00:07:14.575 --> 00:07:19.375 Where do we focus the attention, the AI, the bandwidth, the capability on? s76 00:07:19.375 --> 00:07:21.495 Where is our bottleneck, right?" s77 00:07:21.495 --> 00:07:28.375 And so, it becomes this redesigning of the workflow or at least the reevaluation on what to focus on. s78 00:07:28.375 --> 00:07:30.415 That's what they're doing different. s79 00:07:30.415 --> 00:07:35.535 Now, this consideration isn't particularly defined finance, it's across the the realm and function. s80 00:07:35.535 --> 00:07:47.575 You know, you're asking the same question on what to focus on, whether it be research capacity, decision speed, quality of your data, latency, reliability. s81 00:07:47.575 --> 00:07:50.775 It it it traverses all of that. s82 00:07:50.775 --> 00:07:52.495 And studies show this. s83 00:07:52.495 --> 00:08:03.095 They show the leading studies, um, on average spend their attention on fewer, higher-quality decisions than the other cohort. s84 00:08:03.095 --> 00:08:09.697 So, leaders are being more selective in where they focus their attention and what they focus their attention. s85 00:08:09.697 --> 00:08:10.137 So, it's great. s86 00:08:10.137 --> 00:08:15.617 The leading companies uh have um an idea of what the right problems are. s87 00:08:15.617 --> 00:08:17.857 Where do they go from there? s88 00:08:17.857 --> 00:08:24.457 Well, they also concentrate their investments in the highest value workflows. s89 00:08:25.017 --> 00:08:33.937 I get it, and I've been uh I've been culpable of of something similar where, you know, AI is almost creating too many opportunities. s90 00:08:33.937 --> 00:08:36.417 And experimentation can be a good thing. s91 00:08:36.417 --> 00:08:47.937 Um but one needs to separate experimentation and the resulting innovation theater, or worse, the productivity theater, with actual value generation. s92 00:08:48.097 --> 00:08:51.817 Going back to our two um hypothetical companies, what does this mean? s93 00:08:51.817 --> 00:08:57.097 Company B is looking at how do we launch the most number of AI pilots? s94 00:08:57.097 --> 00:09:06.377 And what that ends up looking like is a document summarization bot here, a co-pilot there, um a a data retrieval bot here. s95 00:09:06.377 --> 00:09:08.177 It's scattered all over the place. s96 00:09:08.177 --> 00:09:20.417 Whereas, what company A is focused on in their investments is potentially use cases like end-to-end workflows on the research um on the research pipeline. s97 00:09:20.417 --> 00:09:29.657 They're focused on aligning engineering, analytics, AI capacity, and judgment towards a singular focus. s98 00:09:29.657 --> 00:09:41.297 And research has shown that the leaders spend more than 80% of their investments on these high-value use cases. s99 00:09:41.297 --> 00:09:46.497 Transforming key business functions and creating new offerings. s100 00:09:46.737 --> 00:09:47.297 Perfect. s101 00:09:47.297 --> 00:09:56.697 We We know they're focusing on um finding the right problem, and they're also spending deep investment in select workflows. s102 00:09:56.697 --> 00:09:59.697 Well, they're also doing something different. s103 00:09:59.697 --> 00:10:02.977 They're reimagining how work is done. s104 00:10:03.577 --> 00:10:06.017 I think this is this is key. s105 00:10:06.017 --> 00:10:09.978 And it goes back to what we discussed previously, which is judgment. s106 00:10:09.978 --> 00:10:18.338 They're redefining and reorganizing functions, roles, capacities around this key element of judgement. s107 00:10:19.018 --> 00:10:21.618 What does that mean for our hypothetical company? s108 00:10:21.618 --> 00:10:29.258 Well, company B might be focused on as we discussed I'm getting my earnings transcript, I'm getting my research my research reports. s109 00:10:29.258 --> 00:10:30.658 How do I summarize it? s110 00:10:30.658 --> 00:10:32.018 Maybe I make that faster. s111 00:10:32.018 --> 00:10:34.618 Maybe I update my financial models faster. s112 00:10:34.618 --> 00:10:38.218 Maybe I send my recommendation to my PM faster. s113 00:10:38.218 --> 00:10:43.178 Company A is focused on an entirely different ballgame. s114 00:10:43.178 --> 00:10:47.698 Hypothetically, they could be asking the analyst to define the investment thesis. s115 00:10:47.698 --> 00:10:53.498 And they're watching and gathering evidence that could refute that thesis or support it. s116 00:10:53.498 --> 00:10:55.538 They'd be monitoring continuously. s117 00:10:55.538 --> 00:10:58.898 They'd be investigating exceptions. s118 00:10:58.898 --> 00:11:09.978 And they could be deep diving on you know, select examples that could lead to actual generation of value. s119 00:11:09.978 --> 00:11:13.258 So, the winners just don't just automate, they redesign. s120 00:11:13.258 --> 00:11:26.258 And studies show that the cohort of high performers are 75% more likely or in practice, they actually do redesign their workflow versus 25% of the cohort that doesn't. s121 00:11:26.738 --> 00:11:32.658 So, where does this leave us with our two hypothetical companies and designing the right problem? s122 00:11:32.658 --> 00:11:35.858 Company B adds AI to existing workflows. s123 00:11:35.858 --> 00:11:43.018 They're completing individual projects and then moving to the next initiative, whereas company A is rethinking how work is done. s124 00:11:43.018 --> 00:11:48.098 They're redesigning processes around AI and they're obsessing of the the the right problem. s125 00:11:48.098 --> 00:11:53.338 That's that's the difference in how organizations put AI to work. s126 00:11:53.698 --> 00:11:53.938 Great. s127 00:11:53.938 --> 00:11:55.858 So, they've picked the right problem. s128 00:11:55.858 --> 00:11:58.738 What what happens next? s129 00:11:58.738 --> 00:12:06.818 Well, we all know in order to actually enable that capacity, you need to build shared capabilities across the org. s130 00:12:06.818 --> 00:12:16.899 And what's what's a trend that's seemingly emerging for for some time now is that the lines between technology and business are converging. s131 00:12:16.899 --> 00:12:18.339 And we're seeing this everywhere. s132 00:12:18.339 --> 00:12:21.259 We're seeing engineers that are moving closer to business outcomes. s133 00:12:21.259 --> 00:12:25.739 We're seeing business professionals that are building tools and automations. s134 00:12:25.739 --> 00:12:36.579 And as you can see and imagine, you can have forward deployed engineers, you can have forward deployed product managers, but you could also have the inverse, which is forward deployed s135 00:12:36.579 --> 00:12:38.059 portfolio managers. s136 00:12:38.059 --> 00:12:50.739 And it's really this combination of getting the right people in the room by breaking down organization silos that the leaders are really leaning on. s137 00:12:50.739 --> 00:12:59.619 Studies have shown that more and more of the large enterprises are viewing their technology arm as a business growth function. s138 00:12:59.619 --> 00:13:03.939 And that's creating an edge for these for these companies. s139 00:13:04.739 --> 00:13:13.019 So, the other sort of vertical inside building shared capabilities that is probably a personal favorite of mine. s140 00:13:13.019 --> 00:13:17.859 It's standardizing the core and personalizing the edges. s141 00:13:17.859 --> 00:13:21.299 And this is something that isn't new to the technology world. s142 00:13:21.299 --> 00:13:25.819 We've we've built infrastructure, we've built, you know, shared modules and system design. s143 00:13:25.819 --> 00:13:34.779 But what's interesting here today is that's taking a very different shape with the capabilities out there and with the org and the breaking down of silos. s144 00:13:34.779 --> 00:13:39.739 And this could look very different depending on where you sit. s145 00:13:39.739 --> 00:13:47.219 You could be building shared cores for you know, a team that looks very different with analysts and and clients. s146 00:13:47.219 --> 00:13:55.019 You could be doing that with you know, a bunch of analysts or you can do be doing that for software engineers or data scientists. s147 00:13:55.019 --> 00:14:08.899 With the data scientist example in particular, that could look very interesting where instead of having one-off workflows that that is really common in the industry, you could build a shared core s148 00:14:08.899 --> 00:14:21.061 of the data sets, their semantic layers, the tradeoffs of what worked, what didn't, what you know, what what some data sets do good off, where are the gaps, and and so on and so forth, such that s149 00:14:21.061 --> 00:14:31.741 each new personnel becomes almost a plug-and-play module in the system where they can then stand on the shoulder of their predecessors. s150 00:14:31.741 --> 00:14:35.741 So, what should the next person not have to rebuild? s151 00:14:35.745 --> 00:14:36.821 [clears throat] s152 00:14:36.821 --> 00:14:43.101 Now, what's also interesting is, um, you know, the enterprise wasn't really built for agents. s153 00:14:43.101 --> 00:14:57.981 Um, agents, they they work across workflows and applications, they they take contacts, they uh deliberate, they take actions, whereas really the enterprises are focused, um, previously on uh single human s154 00:14:57.981 --> 00:15:01.701 or a collection of them interacting with the single application. s155 00:15:01.701 --> 00:15:14.221 So, the leaders are cognizant that something needs to change and that they need to modernize, um, their infrastructure, and they're very self-aware of where they are in their journey. s156 00:15:14.221 --> 00:15:21.981 And so, studies show that, you know, 75 71% of the orgs are modernizing their core infrastructure to support AI. s157 00:15:21.981 --> 00:15:34.141 And, um, you know, 6 to 10% or 23% of them are investing 6 to 10% of their annual revenue in in core system modernization, um, to take advantage of this. s158 00:15:34.141 --> 00:15:43.061 And a common complaint here or a common challenge here is, um, you know, data quality and and the ability to reuse data. s159 00:15:43.261 --> 00:15:44.621 Um, so, great. s160 00:15:44.621 --> 00:15:51.901 We, you know, we we have the right people in the room, we, um, have you know, we have the right knowledge there. s161 00:15:51.901 --> 00:15:53.821 What is What do we do next? s162 00:15:53.821 --> 00:15:58.981 And then comes the concept of, "Hey, how do we make this knowledge reusable and accessible?" s163 00:15:58.981 --> 00:16:09.821 And that's where we have, you know, depending on on the adoption and where where they are, you can have different types of team agents, where you can have expert agents being subject matter experts, s164 00:16:09.821 --> 00:16:22.062 you can have workflow agents that work across streams like Slack bots that are creating and opening tickets, they're closing them, and what have you, and then you can have really chief of staff kind of agents that keep s165 00:16:22.062 --> 00:16:26.542 a tab on what the state of the organization is and what needs to be done. s166 00:16:26.542 --> 00:16:34.301 So, um you know, going back to our workflows, A again is looking at this very differently in terms of shared capabilities. s167 00:16:34.301 --> 00:16:34.662 [clears throat] s168 00:16:34.662 --> 00:16:42.462 Now, the last pillar here that I want to draw your attention to is well, when does this system really compound? s169 00:16:42.462 --> 00:16:46.422 What happens when it's deployed? s170 00:16:46.422 --> 00:16:51.942 Well, the leading companies, what they're doing is they're actually thinking about it slightly differently. s171 00:16:51.942 --> 00:16:56.742 What are failures that can give us an edge that we can learn from? s172 00:16:56.742 --> 00:17:00.382 If if there's a success, how do you make it part of the system? s173 00:17:00.382 --> 00:17:05.542 And the way they think about this is, you know, the individual learned, but did the system? s174 00:17:05.542 --> 00:17:14.982 Is there a way perhaps, when they've encountered something where they need to make a correction, that that knowledge is fed back into the org, either through a new tool, a context, s175 00:17:14.982 --> 00:17:17.102 or or or what have you. s176 00:17:17.102 --> 00:17:24.102 And um what's what's key here is preserving the evidence along with those learnings. s177 00:17:24.302 --> 00:17:30.662 So, um really the last vertical here is AI frees capacity. s178 00:17:30.662 --> 00:17:32.302 Where does it go? s179 00:17:32.302 --> 00:17:45.902 Being cognizant that you need to redesign your system and really focus it on key metrics that you're evaluating, you're monitoring, and you're creating a fly flywheel off. s180 00:17:45.902 --> 00:17:49.142 So, where does this leave us for companies A and B? s181 00:17:49.142 --> 00:17:53.702 You know, company A has their entire work redesigned. s182 00:17:53.702 --> 00:17:59.742 They're building capabilities that compound, and they have shared capabilities built. s183 00:17:59.742 --> 00:18:02.622 So, I want to leave you off with one final question. s184 00:18:02.622 --> 00:18:03.942 Everyone has AI. s185 00:18:03.942 --> 00:18:05.622 Who has the advantage? s186 00:18:05.622 --> 00:18:07.312 Thank you. s187 00:18:07.312 --> 00:18:09.312 [applause]