# 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=23321s)

[06:28:41](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23321s) Good afternoon, everyone present here.

[06:28:45](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23325s) It's been a great day and an absolute pleasure to be here.

[06:28:52](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23332s) The topic of my talk today is everyone has AI, who has the advantage?

[06:29:00](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23340s) I'll be talking about what the orgs that are doing, that are leading in AI with their implementation and adoption,

[06:29:09](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23349s) what are they doing differently?

[06:29:13](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23353s) so before that, who am I and what's my POV?

[06:29:18](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23358s) So I'm Dr. Sid Pednekar. I am an AI technology and data exec in asset management.

[06:29:26](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23366s) I've spent most of my tenure in hedge funds and investment in academic research.

[06:29:34](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23374s) What I've done and focused on is lead strategy and build specialized teams towards three main verticals,

[06:29:42](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23382s) optimizing efficiency, driving PNL, and reducing costs.

[06:29:49](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23389s) So I'll be following the discussion along with two hypothetical companies,

[06:29:56](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23396s) Company A and Company B, right at the slider below.

[06:30:00](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23400s) These are hypothetical, you know, with made-up workflows, but we'll be following their journey along the rest of this presentation.

[06:30:12](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23412s) So, everyone has AI. Who has the advantage?

[06:30:17](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23417s) Before we get started there, first let's see what the lay of the land is.

[06:30:22](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23422s) And recent studies have shown that only about 37% of enterprises that were surveyed are

[06:30:31](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23431s) showing a positive contribution to their EBIT.

[06:30:35](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23435s) And only about 6% of those would be qualified as high performers.

[06:30:41](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23441s) So that's about 5% contribution to their EBIT.

[06:30:44](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23444s) Other studies earlier in the year have shown that about 66% are saying they're having a productivity increase,

[06:30:53](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23453s) but only about 40% and 20% are actually seeing cost reductions and revenue growth.

[06:31:00](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23460s) So as we know, access of AI is widespread, yet the impact remains uneven.

[06:31:11](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23471s) So, the question I would ask, or how I think about this, is really in terms of three verticals.

[06:31:21](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23481s) And the way I think about these three verticals should apply to organizations and individuals

[06:31:28](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23488s) alike, and it should apply to functions, regardless of where you are in that organization.

[06:31:37](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23497s) So the three main vertical pillars that I think of are, number one, design the right problem.

[06:31:45](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23505s) Number two, build shared capabilities.

[06:31:48](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23508s) And number three, compound the advantage.

[06:31:52](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23512s) Now before we dive into each of these pillars, there is an idea that I think goes through all these verticals.

[06:32:01](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23521s) And I think the best leading firms are taking advantage of that and capitalizing on it.

[06:32:08](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23528s) As we know, throughout time, the only constant in this world has been change.

[06:32:14](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23534s) What's different in the AI and superintelligence era is that intelligence has become cheap.

[06:32:21](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23541s) Regardless of what your unit of intelligence is, whether it be software, analytics, recommendations, reports,

[06:32:30](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23550s) AI is creating an abundance in all of it.

[06:32:32](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23552s) And as we know, more answers don't necessarily create more value.

[06:32:40](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23560s) So what is scarce?

[06:32:42](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23562s) What is the scarce resource in an abundant world?

[06:32:47](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23567s) What are companies taking advantage of, the leaders at least?

[06:32:52](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23572s) It's judgment.

[06:32:54](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23574s) What is judgment?

[06:32:57](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23577s) Judgment is choosing what matters and how to act

[06:33:00](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23580s) when there's no single obvious answer.

[06:33:02](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23582s) It's the creativity that lets you explore what possibilities are worth exploring.

[06:33:07](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23587s) It's the taste that tells you what good looks like in your given context.

[06:33:12](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23592s) It's the decisions where you can weigh what trade-offs are worth making.

[06:33:20](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23600s) is ubiquitous.

[06:33:22](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23602s) It exists throughout your organization on a daily basis.

[06:33:26](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23606s) Your C-suite, the C-suite is asking questions, taking advantage of judgment all the time.

[06:33:31](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23611s) So are the software engineers, the AI leaders, the portfolio managers, and the data scientists.

[06:33:38](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23618s) So the question that they're focused on is, given judgment is a finite asset, a limited capacity,

[06:33:49](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23629s) the main question becomes, where is our best judgment worth spending?

[06:33:54](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23634s) I'd like for you to hold on to that thought.

[06:33:57](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23637s) With that, we'll dive into our first pillar.

[06:34:00](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23640s) Design the right problem.

[06:34:02](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23642s) 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?

[06:34:15](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23655s) It's tempting to start with a tool.

[06:34:17](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23657s) Today we're surrounded with better models, harnesses.

[06:34:22](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23662s) We're surrounded with all these AI tools.

[06:34:23](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23663s) We're surrounded with co-pilots.

[06:34:26](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23666s) But that isn't the way about, is it?

[06:34:29](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23669s) At least that's not the way the leaders are approaching it.

[06:34:31](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23671s) Now, let's go back to our hypothetical two companies.

[06:34:35](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23675s) Let's say they're from asset management.

[06:34:37](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23677s) Let's say we're tracking the workflow of two research analysts

[06:34:41](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23681s) in these hypothetical organizations.

[06:34:45](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23685s) Well, let's say organization B starts with the tool, the co-pilot.

[06:34:51](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23691s) They're asking, where can we add AI to the analyst's work?

[06:34:55](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23695s) They're looking at the current existing process to say,

[06:34:59](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23699s) earning transcript comes in, I discovered a summarization workflow, let me pass all of

[06:35:05](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23705s) this in through my fire hose, analysts get earning summaries faster, win-win, right?

[06:35:13](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23713s) Well, not so much, at least not compared to the guys who are leading.

[06:35:17](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23717s) And such a workflow of being a faster horse introduces one key nuance.

[06:35:23](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23723s) That is, you are losing context as you have information, I guess, reduction as part of your summarization.

[06:35:31](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23731s) So what is a hypothetical organization A doing in this?

[06:35:36](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23736s) Well, they're obsessed on the right problem to ask.

[06:35:39](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23739s) They could be asking, why are our material signals reaching us too late?

[06:35:44](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23744s) Where do we focus the attention, the AI, the bandwidth, the capability on?

[06:35:49](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23749s) Where is our bottleneck, right?

[06:35:51](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23751s) And so it becomes this redesigning of the workflow,

[06:35:54](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23754s) or at least a re-evaluation on what to focus on.

[06:35:58](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23758s) That's what they're doing different.

[06:35:59](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23759s) Now, this consideration isn't particular to finance.

[06:36:02](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23762s) It's across the realm and function.

[06:36:05](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23765s) You're asking the same question on what to focus on,

[06:36:10](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23770s) whether it be research capacity, decision speed,

[06:36:13](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23773s) quality of your data, latency, reliability.

[06:36:17](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23777s) It traverses all of that.

[06:36:20](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23780s) studies show this.

[06:36:22](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23782s) They show leading studies, on average,

[06:36:26](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23786s) spend their attention on fewer higher quality decisions

[06:36:30](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23790s) than the other cohort.

[06:36:32](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23792s) So leaders are being more selective

[06:36:34](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23794s) in where they focus their attention

[06:36:36](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23796s) and what they focus their attention.

[06:36:39](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23799s) So it's great.

[06:36:40](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23800s) The leading companies have an idea

[06:36:43](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23803s) of what the right problems are.

[06:36:45](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23805s) Where do they go from there?

[06:36:47](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23807s) Well, they also concentrate their investments in the highest value workflows.

[06:36:54](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23814s) I get it, and I've been culpable of something similar,

[06:36:58](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23818s) where AI is almost creating too many opportunities.

[06:37:03](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23823s) And experimentation can be a good thing.

[06:37:06](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23826s) But one needs to separate experimentation and the resulting innovation theater,

[06:37:11](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23831s) or worse, the productivity theater, which actual value generation.

[06:37:17](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23837s) Going back to our two hypothetical companies, what does this mean?

[06:37:21](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23841s) Company B is looking at how do we launch the most number of AI pilots?

[06:37:26](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23846s) And what that ends up looking like is a document summarization bot here,

[06:37:31](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23851s) a co-pilot there, a data retrieval bot here.

[06:37:35](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23855s) It's scattered all over the place.

[06:37:37](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23857s) whereas what company A is focused on in their investments

[06:37:41](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23861s) is potentially use cases like end-to-end workflows

[06:37:46](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23866s) on the research pipeline.

[06:37:50](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23870s) They're focused on aligning engineering, analytics, AI, capacity,

[06:37:55](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23875s) and judgment towards a singular focus.

[06:37:59](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23879s) And research has shown that the leaders spend more than 80%

[06:38:05](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23885s) of their investments on these high-value use cases,

[06:38:10](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23890s) transforming key business functions

[06:38:13](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23893s) and creating new offerings.

[06:38:16](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23896s) Perfect.

[06:38:17](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23897s) We know they're focusing on finding the right problem,

[06:38:21](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23901s) and they're also spending deep investment

[06:38:24](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23904s) in select workflows.

[06:38:26](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23906s) Well, they're also doing something different.

[06:38:29](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23909s) They're reimagining how work is done.

[06:38:32](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23912s) I think this is key.

[06:38:35](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23915s) And it goes back to what we discussed previously, which is judgment.

[06:38:40](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23920s) They're redefining and reorganizing functions, roles, capacities

[06:38:44](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23924s) around this key element of judgment.

[06:38:48](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23928s) What does that mean for our hypothetical company?

[06:38:51](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23931s) Well, company B might be focused on, as we discussed,

[06:38:55](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23935s) I'm getting my earnings transcript, I'm getting my research reports.

[06:38:59](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23939s) How do I summarize it? Maybe I make that faster.

[06:39:02](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23942s) Maybe I update my financial models faster.

[06:39:04](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23944s) Maybe I send my recommendation to my PM faster.

[06:39:07](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23947s) Company A is focused on an entirely different ballgame.

[06:39:13](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23953s) Hypothetically, they could be asking the analyst to define the investment thesis,

[06:39:16](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23956s) and they're watching and gathering evidence that could refute that thesis or support it.

[06:39:22](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23962s) They'd be monitoring continuously, they'd be investigating exceptions,

[06:39:28](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23968s) and they could be deep diving on select examples

[06:39:35](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23975s) that could lead to actual generation of value.

[06:39:39](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23979s) So the winners don't just automate, they redesign.

[06:39:43](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23983s) studies show that the cohort of high performers

[06:39:46](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23986s) are 75% more likely, or in practice,

[06:39:49](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23989s) they actually do redesign their workflow

[06:39:51](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23991s) versus 25% of the cohort that doesn't.

[06:39:56](https://www.youtube.com/watch?v=akEqiXFo3GM&t=23996s) So where does this leave us with our two hypothetical companies

[06:40:00](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24000s) 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.

[06:40:23](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24023s) Great, so they've picked the right problem.

[06:40:25](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24025s) What happens next?

[06:40:28](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24028s) Well, we all know in order to actually enable that capacity,

[06:40:31](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24031s) you need to build share capabilities across the org.

[06:40:36](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24036s) And what's a trend that's seemingly emerging for some time now

[06:40:41](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24041s) is that the lines between technology and business are converging.

[06:40:46](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24046s) And we're seeing this everywhere.

[06:40:48](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24048s) We're seeing engineers that are moving closer to business outcomes.

[06:40:51](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24051s) We're seeing business professionals that are building tools and automations.

[06:40:55](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24055s) And as you can see and imagine, you can have forward deployed engineers,

[06:41:00](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24060s) you can have forward deployed product managers,

[06:41:02](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24062s) but you could also have the inverse, which is forward deployed portfolio managers.

[06:41:07](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24067s) And it's really this combination of getting the right people in the room

[06:41:12](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24072s) by breaking down organization silos that the leaders are really leaning on.

[06:41:20](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24080s) studies have shown that more and more of the large enterprises are viewing their technology arm as a business growth function

[06:41:28](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24088s) and that's creating an edge for these companies

[06:41:34](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24094s) so the other sort of vertical inside building shared capabilities that is probably a personal favorite of mine

[06:41:42](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24102s) it's standardizing the core and personalizing the edges

[06:41:47](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24107s) And this is something that isn't new to the technology world.

[06:41:51](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24111s) We've built infrastructure.

[06:41:52](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24112s) We've built shared modules and system design.

[06:41:55](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24115s) But what's interesting here today is that's taking a very different shape with the capabilities out there

[06:42:01](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24121s) and with the org and the breaking down of silos.

[06:42:04](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24124s) And this could look very different depending on where you sit.

[06:42:09](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24129s) You could be building shared cores

[06:42:11](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24131s) for a team that looks very different

[06:42:14](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24134s) with analysts and clients.

[06:42:16](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24136s) You could be doing that with a bunch of analysts,

[06:42:19](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24139s) or you could be doing that for software engineers

[06:42:22](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24142s) or data scientists.

[06:42:24](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24144s) With the data scientist example in particular,

[06:42:28](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24148s) that could look very interesting

[06:42:29](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24149s) where instead of having one-off workflows

[06:42:34](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24154s) that is really common in the industry,

[06:42:36](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24156s) you could build a shared core

[06:42:38](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24158s) of the data sets, their semantic layers,

[06:42:41](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24161s) the trade-offs of what worked, what didn't,

[06:42:44](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24164s) what some data sets do good of,

[06:42:47](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24167s) where are the gaps, and so on and so forth,

[06:42:49](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24169s) such that each new personnel

[06:42:52](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24172s) becomes almost a plug-and-play module

[06:42:55](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24175s) in the system where they can then stand

[06:42:58](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24178s) on the shoulder of their predecessors.

[06:43:01](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24181s) So what should the next person not have to rebuild?

[06:43:05](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24185s) now what's also interesting is um you know the enterprise wasn't really built for agents um agents

[06:43:13](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24193s) they they work across workflows and applications they they take context they uh deliberate they

[06:43:20](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24200s) take actions whereas really the enterprises are focused um previously on a single human

[06:43:27](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24207s) or a collection of them interacting with a single application.

[06:43:31](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24211s) So the leaders are cognizant that something needs to change

[06:43:35](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24215s) and that they need to modernize their infrastructure.

[06:43:38](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24218s) And they're very self-aware of where they are in their journey.

[06:43:43](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24223s) And so studies show that 71% of the orgs

[06:43:48](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24228s) are modernizing their core infrastructure to support AI.

[06:43:52](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24232s) and 6% to 10% or 23% of them are investing 6% to 10% of their annual revenue

[06:43:59](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24239s) in core system modernization to take advantage of this.

[06:44:03](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24243s) And a common complaint here or a common challenge here is data quality

[06:44:09](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24249s) and the ability to reuse data.

[06:44:13](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24253s) So, great. We have the right people in the room.

[06:44:16](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24256s) We have the right knowledge there.

[06:44:21](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24261s) What do we do next?

[06:44:23](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24263s) And then comes the concept of, hey, how do we make this knowledge reusable and accessible?

[06:44:28](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24268s) And that's where we have, depending on the adoption and where they are, you can have different types of team agents,

[06:44:35](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24275s) where you can have expert agents being subject matter experts.

[06:44:39](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24279s) You can have workflow agents that work across streams like Slack bots that are creating and opening tickets.

[06:44:45](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24285s) they're closing them, and what have you.

[06:44:48](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24288s) And then you can have really chief of staff kind of agents

[06:44:50](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24290s) that keep a tab on what the state of the organization is

[06:44:54](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24294s) and what needs to be done.

[06:44:56](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24296s) So, you know, going back to our workflows,

[06:44:59](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24299s) A, again, is looking at this very differently

[06:45:01](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24301s) in terms of shared capabilities.

[06:45:04](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24304s) Now, the last pillar here that I want to draw your attention to is,

[06:45:09](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24309s) well, when does this system really compound?

[06:45:12](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24312s) What happens when it's deployed?

[06:45:16](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24316s) Well, the leading companies, what they're doing is they're actually thinking about it slightly differently.

[06:45:21](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24321s) What are failures that can give us an edge that we can learn from?

[06:45:26](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24326s) If there's a success, how do we make it part of the system?

[06:45:30](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24330s) And the way they think about this is, you know, the individual learned but did the system.

[06:45:35](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24335s) Is there a way, perhaps, when they've encountered something where they need to make a correction,

[06:45:39](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24339s) that that knowledge is fed back into the org,

[06:45:42](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24342s) either through a new tool or context or what have you.

[06:45:46](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24346s) And what's key here is preserving the evidence along with those learnings.

[06:45:54](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24354s) So really the last vertical here is AI freeze capacity.

[06:46:00](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24360s) Where does it go?

[06:46:01](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24361s) Being cognizant that you need to redesign your system

[06:46:05](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24365s) and really focus it on key metrics that you're evaluating,

[06:46:11](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24371s) you're monitoring, and you're creating a flywheel off.

[06:46:15](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24375s) So where does this leave us for companies A and B?

[06:46:19](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24379s) You know, company A has their entire work redesigned.

[06:46:23](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24383s) They're building capabilities at Compound,

[06:46:25](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24385s) and they have shared capabilities built.

[06:46:29](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24389s) So I want to leave you off with one final question.

[06:46:32](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24392s) Everyone has AI. Who has the advantage?

[06:46:35](https://www.youtube.com/watch?v=akEqiXFo3GM&t=24395s) Thank you.