# Everyone Has AI. Who Has the Advantage?

Sid Pednekar · AI Engineer NYC 2026 · October 13

[Watch recording](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21724s)

[06:02:05](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21725s) Good afternoon, everyone present here.

[06:02:09](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21729s) It's been a great day and an absolute pleasure to be here.

[06:02:15](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21735s) The topic of my talk today is everyone has AI, who has the advantage?

[06:02:24](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21744s) I'll be talking about what the orgs that are doing, that are leading in AI with their implementation and adoption,

[06:02:33](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21753s) what are they doing differently?

[06:02:37](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21757s) so before that, who am I and what's my POV?

[06:02:41](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21761s) So I'm Dr. Sid Pednekar. I am an AI technology and data exec in asset management.

[06:02:50](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21770s) I've spent most of my tenure in hedge funds and investment in academic research.

[06:02:57](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21777s) What I've done and focused on is lead strategy and build specialized teams towards three main verticals,

[06:03:06](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21786s) optimizing efficiency, driving PNL, and reducing costs.

[06:03:12](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21792s) So I'll be following the discussion along with two hypothetical companies,

[06:03:20](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21800s) Company A and Company B, right at the slider below.

[06:03:24](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21804s) These are hypothetical, you know, with made-up workflows, but we'll be following their journey along the rest of this presentation.

[06:03:36](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21816s) So, everyone has AI. Who has the advantage?

[06:03:41](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21821s) Before we get started there, first let's see what the lay of the land is.

[06:03:46](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21826s) And recent studies have shown that only about 37% of enterprises that were surveyed are

[06:03:55](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21835s) showing a positive contribution to their EBIT.

[06:03:59](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21839s) And only about 6% of those would be qualified as high performers.

[06:04:05](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21845s) So that's about 5% contribution to their EBIT.

[06:04:08](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21848s) Other studies earlier in the year have shown that about 66% are saying they're having a productivity increase,

[06:04:17](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21857s) but only about 40% and 20% are actually seeing cost reductions and revenue growth.

[06:04:24](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21864s) So as we know, access of AI is widespread, yet the impact remains uneven.

[06:04:35](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21875s) So, the question I would ask, or how I think about this, is really in terms of three verticals.

[06:04:45](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21885s) And the way I think about these three verticals should apply to organizations and individuals

[06:04:52](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21892s) alike, and it should apply to functions, regardless of where you are in that organization.

[06:05:01](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21901s) So the three main vertical pillars that I think of are, number one, design the right problem.

[06:05:08](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21908s) Number two, build shared capabilities.

[06:05:12](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21912s) And number three, compound the advantage.

[06:05:16](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21916s) Now before we dive into each of these pillars, there is an idea that I think goes through all these verticals.

[06:05:24](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21924s) And I think the best leading firms are taking advantage of that and capitalizing on it.

[06:05:31](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21931s) As we know, throughout time, the only constant in this world has been change.

[06:05:38](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21938s) What's different in the AI and superintelligence era is that intelligence has become cheap.

[06:05:45](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21945s) Regardless of what your unit of intelligence is, whether it be software, analytics, recommendations, reports,

[06:05:54](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21954s) AI is creating an abundance in all of it.

[06:05:56](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21956s) And as we know, more answers don't necessarily create more value.

[06:06:03](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21963s) So what is scarce?

[06:06:06](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21966s) What is the scarce resource in an abundant world?

[06:06:11](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21971s) What are companies taking advantage of, the leaders at least?

[06:06:15](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21975s) It's judgment.

[06:06:18](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21978s) What is judgment?

[06:06:20](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21980s) Judgment is choosing what matters and how to act

[06:06:24](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21984s) when there's no single obvious answer.

[06:06:26](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21986s) It's the creativity that lets you explore what possibilities are worth exploring.

[06:06:31](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21991s) It's the taste that tells you what good looks like in your given context.

[06:06:36](https://www.youtube.com/watch?v=akEqiXFo3GM&t=21996s) It's the decisions where you can weigh what trade-offs are worth making.

[06:06:44](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22004s) is ubiquitous.

[06:06:45](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22005s) It exists throughout your organization on a daily basis.

[06:06:49](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22009s) Your C-suite, the C-suite is asking questions, taking advantage of judgment all the time.

[06:06:55](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22015s) So are the software engineers, the AI leaders, the portfolio managers, and the data scientists.

[06:07:01](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22021s) So the question that they're focused on is, given judgment is a finite asset, a limited capacity,

[06:07:13](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22033s) the main question becomes, where is our best judgment worth spending?

[06:07:18](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22038s) I'd like for you to hold on to that thought.

[06:07:21](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22041s) With that, we'll dive into our first pillar.

[06:07:23](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22043s) Design the right problem.

[06:07:25](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22045s) So questions, a question that the leading organizations are asking all the time is they're obsessed over what is the right problem to solve, right?

[06:07:39](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22059s) It's tempting to start with a tool.

[06:07:41](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22061s) Today we're surrounded with better models, harnesses.

[06:07:45](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22065s) We're surrounded with all these AI tools.

[06:07:47](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22067s) We're surrounded with co-pilots.

[06:07:49](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22069s) But that isn't the way about, is it?

[06:07:52](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22072s) At least that's not the way the leaders are approaching it.

[06:07:55](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22075s) Now, let's go back to our hypothetical two companies.

[06:07:59](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22079s) Let's say they're from asset management.

[06:08:00](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22080s) Let's say we're tracking the workflow of two research analysts

[06:08:05](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22085s) in these hypothetical organizations.

[06:08:09](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22089s) Well, let's say organization B starts with the tool, the co-pilot.

[06:08:14](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22094s) They're asking, where can we add AI to the analyst's work?

[06:08:19](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22099s) They're looking at the current existing process to say,

[06:08:22](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22102s) earning transcript comes in, I discovered a summarization workflow, let me pass all of

[06:08:28](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22108s) this in through my fire hose, analysts get earning summaries faster, win-win, right?

[06:08:36](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22116s) Well, not so much, at least not compared to the guys who are leading.

[06:08:41](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22121s) And such a workflow of being a faster horse introduces one key nuance.

[06:08:47](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22127s) That is, you are losing context as you have information, I guess, reduction as part of your summarization.

[06:08:55](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22135s) So what is a hypothetical organization A doing in this?

[06:09:00](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22140s) Well, they're obsessed on the right problem to ask.

[06:09:02](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22142s) They could be asking, why are our material signals reaching us too late?

[06:09:07](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22147s) Where do we focus the attention, the AI, the bandwidth, the capability on?

[06:09:12](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22152s) Where is our bottleneck, right?

[06:09:14](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22154s) And so it becomes this redesigning of the workflow,

[06:09:18](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22158s) or at least a re-evaluation on what to focus on.

[06:09:21](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22161s) That's what they're doing different.

[06:09:23](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22163s) Now, this consideration isn't particular to finance.

[06:09:26](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22166s) It's across the realm and function.

[06:09:29](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22169s) You're asking the same question on what to focus on,

[06:09:33](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22173s) whether it be research capacity, decision speed,

[06:09:37](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22177s) quality of your data, latency, reliability.

[06:09:41](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22181s) It traverses all of that.

[06:09:44](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22184s) studies show this.

[06:09:46](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22186s) They show leading studies, on average,

[06:09:50](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22190s) spend their attention on fewer higher quality decisions

[06:09:54](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22194s) than the other cohort.

[06:09:56](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22196s) So leaders are being more selective

[06:09:58](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22198s) in where they focus their attention

[06:10:00](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22200s) and what they focus their attention.

[06:10:02](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22202s) So it's great.

[06:10:03](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22203s) The leading companies have an idea

[06:10:06](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22206s) of what the right problems are.

[06:10:09](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22209s) Where do they go from there?

[06:10:10](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22210s) Well, they also concentrate their investments in the highest value workflows.

[06:10:18](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22218s) I get it, and I've been culpable of something similar,

[06:10:22](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22222s) where AI is almost creating too many opportunities.

[06:10:27](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22227s) And experimentation can be a good thing.

[06:10:30](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22230s) But one needs to separate experimentation and the resulting innovation theater,

[06:10:35](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22235s) or worse, the productivity theater, which actual value generation.

[06:10:41](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22241s) Going back to our two hypothetical companies, what does this mean?

[06:10:45](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22245s) Company B is looking at how do we launch the most number of AI pilots?

[06:10:50](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22250s) And what that ends up looking like is a document summarization bot here,

[06:10:55](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22255s) a co-pilot there, a data retrieval bot here.

[06:10:59](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22259s) It's scattered all over the place.

[06:11:01](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22261s) whereas what company A is focused on in their investments

[06:11:05](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22265s) is potentially use cases like end-to-end workflows

[06:11:10](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22270s) on the research pipeline.

[06:11:13](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22273s) They're focused on aligning engineering, analytics, AI, capacity,

[06:11:19](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22279s) and judgment towards a singular focus.

[06:11:23](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22283s) And research has shown that the leaders spend more than 80%

[06:11:29](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22289s) of their investments on these high-value use cases,

[06:11:34](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22294s) transforming key business functions

[06:11:36](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22296s) and creating new offerings.

[06:11:39](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22299s) Perfect.

[06:11:40](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22300s) We know they're focusing on finding the right problem,

[06:11:44](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22304s) and they're also spending deep investment

[06:11:48](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22308s) in select workflows.

[06:11:49](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22309s) Well, they're also doing something different.

[06:11:52](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22312s) They're reimagining how work is done.

[06:11:56](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22316s) I think this is key.

[06:11:59](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22319s) And it goes back to what we discussed previously, which is judgment.

[06:12:03](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22323s) They're redefining and reorganizing functions, roles, capacities

[06:12:08](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22328s) around this key element of judgment.

[06:12:12](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22332s) What does that mean for our hypothetical company?

[06:12:15](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22335s) Well, company B might be focused on, as we discussed,

[06:12:18](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22338s) I'm getting my earnings transcript, I'm getting my research reports.

[06:12:22](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22342s) How do I summarize it? Maybe I make that faster.

[06:12:25](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22345s) Maybe I update my financial models faster.

[06:12:27](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22347s) Maybe I send my recommendation to my PM faster.

[06:12:31](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22351s) Company A is focused on an entirely different ballgame.

[06:12:36](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22356s) Hypothetically, they could be asking the analyst to define the investment thesis,

[06:12:40](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22360s) and they're watching and gathering evidence that could refute that thesis or support it.

[06:12:46](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22366s) They'd be monitoring continuously, they'd be investigating exceptions,

[06:12:52](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22372s) and they could be deep diving on select examples

[06:12:59](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22379s) that could lead to actual generation of value.

[06:13:03](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22383s) So the winners don't just automate, they redesign.

[06:13:07](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22387s) studies show that the cohort of high performers

[06:13:10](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22390s) are 75% more likely, or in practice,

[06:13:13](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22393s) they actually do redesign their workflow

[06:13:15](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22395s) versus 25% of the cohort that doesn't.

[06:13:20](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22400s) So where does this leave us with our two hypothetical companies

[06:13:23](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22403s) and designing the right problem? Company B adds AI to existing workflows, they're completing individual projects and they're moving to the next initiative, whereas Company A is rethinking how work is done. They're redesigning processes around AI and they're obsessing over the right problem. That's the difference in how organizations put AI to work.

[06:13:46](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22426s) Great, so they've picked the right problem.

[06:13:49](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22429s) What happens next?

[06:13:51](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22431s) Well, we all know in order to actually enable that capacity,

[06:13:55](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22435s) you need to build share capabilities across the org.

[06:14:00](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22440s) And what's a trend that's seemingly emerging for some time now

[06:14:05](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22445s) is that the lines between technology and business are converging.

[06:14:10](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22450s) And we're seeing this everywhere.

[06:14:11](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22451s) We're seeing engineers that are moving closer to business outcomes.

[06:14:14](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22454s) We're seeing business professionals that are building tools and automations.

[06:14:19](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22459s) And as you can see and imagine, you can have forward deployed engineers,

[06:14:24](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22464s) you can have forward deployed product managers,

[06:14:26](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22466s) but you could also have the inverse, which is forward deployed portfolio managers.

[06:14:31](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22471s) And it's really this combination of getting the right people in the room

[06:14:36](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22476s) by breaking down organization silos that the leaders are really leaning on.

[06:14:43](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22483s) studies have shown that more and more of the large enterprises are viewing their technology arm as a business growth function

[06:14:52](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22492s) and that's creating an edge for these companies

[06:14:58](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22498s) so the other sort of vertical inside building shared capabilities that is probably a personal favorite of mine

[06:15:06](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22506s) it's standardizing the core and personalizing the edges

[06:15:11](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22511s) And this is something that isn't new to the technology world.

[06:15:15](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22515s) We've built infrastructure.

[06:15:16](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22516s) We've built shared modules and system design.

[06:15:19](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22519s) But what's interesting here today is that's taking a very different shape with the capabilities out there

[06:15:25](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22525s) and with the org and the breaking down of silos.

[06:15:27](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22527s) And this could look very different depending on where you sit.

[06:15:33](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22533s) You could be building shared cores

[06:15:35](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22535s) for a team that looks very different

[06:15:38](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22538s) with analysts and clients.

[06:15:40](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22540s) You could be doing that with a bunch of analysts,

[06:15:43](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22543s) or you could be doing that for software engineers

[06:15:46](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22546s) or data scientists.

[06:15:48](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22548s) With the data scientist example in particular,

[06:15:51](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22551s) that could look very interesting

[06:15:53](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22553s) where instead of having one-off workflows

[06:15:57](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22557s) that is really common in the industry,

[06:16:00](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22560s) you could build a shared core

[06:16:02](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22562s) of the data sets, their semantic layers,

[06:16:05](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22565s) the trade-offs of what worked, what didn't,

[06:16:08](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22568s) what some data sets do good of,

[06:16:10](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22570s) where are the gaps, and so on and so forth,

[06:16:13](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22573s) such that each new personnel

[06:16:15](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22575s) becomes almost a plug-and-play module

[06:16:19](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22579s) in the system where they can then stand

[06:16:21](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22581s) on the shoulder of their predecessors.

[06:16:24](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22584s) So what should the next person not have to rebuild?

[06:16:29](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22589s) now what's also interesting is um you know the enterprise wasn't really built for agents um agents

[06:16:37](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22597s) they they work across workflows and applications they they take context they uh deliberate they

[06:16:43](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22603s) take actions whereas really the enterprises are focused um previously on a single human

[06:16:51](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22611s) or a collection of them interacting with a single application.

[06:16:55](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22615s) So the leaders are cognizant that something needs to change

[06:16:59](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22619s) and that they need to modernize their infrastructure.

[06:17:02](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22622s) And they're very self-aware of where they are in their journey.

[06:17:07](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22627s) And so studies show that 71% of the orgs

[06:17:11](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22631s) are modernizing their core infrastructure to support AI.

[06:17:16](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22636s) and 6% to 10% or 23% of them are investing 6% to 10% of their annual revenue

[06:17:23](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22643s) in core system modernization to take advantage of this.

[06:17:27](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22647s) And a common complaint here or a common challenge here is data quality

[06:17:33](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22653s) and the ability to reuse data.

[06:17:36](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22656s) So, great. We have the right people in the room.

[06:17:40](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22660s) We have the right knowledge there.

[06:17:45](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22665s) What do we do next?

[06:17:47](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22667s) And then comes the concept of, hey, how do we make this knowledge reusable and accessible?

[06:17:52](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22672s) And that's where we have, depending on the adoption and where they are, you can have different types of team agents,

[06:17:59](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22679s) where you can have expert agents being subject matter experts.

[06:18:03](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22683s) You can have workflow agents that work across streams like Slack bots that are creating and opening tickets.

[06:18:09](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22689s) they're closing them, and what have you.

[06:18:11](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22691s) And then you can have really chief of staff kind of agents

[06:18:14](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22694s) that keep a tab on what the state of the organization is

[06:18:18](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22698s) and what needs to be done.

[06:18:20](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22700s) So, you know, going back to our workflows,

[06:18:23](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22703s) A, again, is looking at this very differently

[06:18:25](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22705s) in terms of shared capabilities.

[06:18:27](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22707s) Now, the last pillar here that I want to draw your attention to is,

[06:18:32](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22712s) well, when does this system really compound?

[06:18:35](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22715s) What happens when it's deployed?

[06:18:39](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22719s) Well, the leading companies, what they're doing is they're actually thinking about it slightly differently.

[06:18:45](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22725s) What are failures that can give us an edge that we can learn from?

[06:18:50](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22730s) If there's a success, how do we make it part of the system?

[06:18:54](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22734s) And the way they think about this is, you know, the individual learned but did the system.

[06:18:58](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22738s) Is there a way, perhaps, when they've encountered something where they need to make a correction,

[06:19:03](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22743s) that that knowledge is fed back into the org,

[06:19:06](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22746s) either through a new tool or context or what have you.

[06:19:10](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22750s) And what's key here is preserving the evidence along with those learnings.

[06:19:18](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22758s) So really the last vertical here is AI freeze capacity.

[06:19:24](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22764s) Where does it go?

[06:19:25](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22765s) Being cognizant that you need to redesign your system

[06:19:29](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22769s) and really focus it on key metrics that you're evaluating,

[06:19:35](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22775s) you're monitoring, and you're creating a flywheel off.

[06:19:39](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22779s) So where does this leave us for companies A and B?

[06:19:42](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22782s) You know, company A has their entire work redesigned.

[06:19:46](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22786s) They're building capabilities at Compound,

[06:19:49](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22789s) and they have shared capabilities built.

[06:19:52](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22792s) So I want to leave you off with one final question.

[06:19:55](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22795s) Everyone has AI. Who has the advantage?

[06:19:59](https://www.youtube.com/watch?v=akEqiXFo3GM&t=22799s) Thank you.