引言与背景
大家下午好。今天非常精彩，非常荣幸能来到这里。我今天演讲的主题是：人人都有 AI，谁拥有竞争优势？我将分享那些在 AI 实施和采纳方面处于领先地位的机构，究竟在采取哪些不同的做法。
英文原文
[music]Good afternoon everyone present here. It's It's been a It's been a great day and an absolute pleasure to be here. The topic of my talk today is everyone has AI who has the advantage? 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?
在此之前，我是谁，我的视角是什么？我是 Sid Patnekar 博士，资产管理领域的 AI、技术和数据高管。我的大部分职业生涯都在对冲基金、投资以及学术研究中度过。我的工作重点是领导战略并组建专业团队，主要围绕三个方向：优化效率、驱动损益（P&L）以及降低成本。
英文原文
Um And so before that who am I and what's my POV? So, I'm Dr. Sid Patnekar. I am an AI technology and data exec in asset management. I've spent most of my tenure in hedge funds and investment and academic research. 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.在接下来的讨论中，我将引入两家假设的公司——位于下方滑块的 A 公司和 B 公司。这些都是假设的，有着虚构的工作流，但我们将在本次演讲的剩余部分跟随他们的历程展开探讨。
英文原文
So, um I'll be following the discussion along with two hypothetical companies, company A and company B right at the slider below. 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.AI 落地现状与三大支柱
人人都有 AI，谁拥有竞争优势？在深入探讨之前，我们先来看看当前的整体现状。近期研究表明，在接受调查的企业中，只有约 37% 的企业显示 AI 对其息税前利润（EBIT）产生了正向贡献；而其中只有约 6% 可被称为高绩效者，对 EBIT 的贡献约为 5%。今年早些时候的其他研究表明，约 66% 的企业表示生产力有所提升，但只有约 40% 和 20% 的企业实际看到了成本削减和收入增长。因此正如我们所知，AI 的普及十分广泛，但其影响依然极不均衡。
英文原文
So everyone has AI, who has the advantage? 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 contribution to their EBIT. And only about 6% of those would be qualified as high performers. So, that's about 5% contribution to their EBIT. 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 revenue growth. So, as we know, access of AI is widespread, yet the impact remains uneven.那么我想问的问题，或者我对此的思考方式，主要是从三个支柱展开的。我认为这三个支柱应当同样适用于组织和个人，并且无论你在组织中处于什么职能部门，都应当适用。我考虑的三个核心支柱是：第一，设计正确的问题；第二，构建共享能力；第三，实现优势的复利累积。
英文原文
So,[clears throat]
the question I would ask, or how I think about this, is really in terms of three verticals. 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. Um So, the three main vertical pillars that I think of are number one, design the right problem. Number two, build shared capabilities. And number three, compound the advantage.
稀缺资产：智能过剩时代的判断力
在深入探讨每个支柱之前，有一个理念贯穿了所有这些方向。我认为最顶尖的领先企业正在充分利用并将其变现。众所周知，纵观历史，世界上唯一的恒量就是变化。在 AI 和超级智能时代，不同之处在于智能已经变得廉价。无论你的智能单位是什么——不管是软件、分析、建议还是报告——AI 都在创造这一切的过剩供给。正如我们所知，更多的答案并不必然创造更多的价值。那么什么是稀缺的？在一个过剩的世界里，什么是稀缺资源？领先的企业究竟在利用什么？答案是判断力。
英文原文
Now, before we dive into each of these pillars, there is an idea that I think goes through all these verticals. And I think the best leading firms are taking advantage of that and capitalizing on it. As we know, throughout time, the only constant in this world has been change. What's different in the AI and superintelligence era is that intelligence has become cheap. Regardless of what your unit of intelligence is, whether it be software, analytics, recommendations, reports, AI is creating an abundance in all of it. And um as we know, more answers don't necessarily create more value. So, what is scarce? What is a scarce resource in an abundant world? What what are companies taking advantage of, the leaders at least? It's judgment.什么是判断力？判断力就是在没有单一显而易见的答案时，选择什么才是重要的以及如何采取行动。它是让你探索哪些可能性值得探索的创造力；它是在特定背景下告诉你何为优秀的品味；它是在决策中权衡哪些取舍值得付出的能力。判断力无处不在，存在于组织的各个层面。每天，你的最高管理层（C-suite）都在提出问题并不断运用判断力；软件工程师、AI 负责人、投资组合经理和数据科学家也同样如此。因此，他们所聚焦的问题在于：鉴于判断力是一种有限的资产、一种有限的能力，核心问题就变成了——我们最优质的判断力值得投入在何处？ 希望大家能记住这个思考。接下来我们将进入第一个支柱。
英文原文
What is judgment? Judgment is choosing what matters and how to act when there's no single obvious answer. It's the creativity that lets you explore what possibilities are worth exploring. It's the taste that tells you what good looks like in your given context. It's the decisions where you can weigh what trade-offs are worth making. Judgment is ubiquitous. It exists throughout your organization. On a daily basis, your C-suite The C-suite is asking questions, taking advantage of judgment judgment all the time. So are the software engineers, the AI leaders, the portfolio managers, and the data scientists. 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? Like for you to hold on to that thought. With that, we'll dive into our first pillar.支柱一：设计正确的问题
设计正确的问题。领先机构时刻在问的一个问题、并且为之痴迷的问题就是：什么是真正值得解决的正确问题？从工具入手是很诱人的。今天我们被更好的模型和测试框架（harnesses）所包围，被各种各样的 AI 工具和 Co-pilot 所包围。但这不是正确的途径，至少领先者不是这样切入的。
英文原文
Design the right problem. 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? Right? It's tempting to start with the tool. Today we're surrounded with better models, harnesses. We're surrounded with all these AI tools. We're surrounded with co-pilots. But that isn't the way about, is it? At least that's not the way the leaders are approaching it.现在回到我们假设的两家公司。假设它们来自资产管理行业，我们追踪这两家假设机构中两位研究分析师的工作流程。假设 B 公司从工具、从 Co-pilot 开始。他们会问：“我们可以在分析师工作的哪个环节加入 AI？”他们看着现有的流程说：“财报会议纪要出来了，我发现了一个总结工作流，让我把所有这些内容都塞进我的输水管道里，分析师就能更快地拿到财报总结。双赢，对吧？”但事实并非如此，至少与领先者相比并非如此。而且，这种“追求一匹更快的马”的工作流引入了一个关键问题：即在作为总结一部分的信息压缩过程中，你正在丢失上下文。
英文原文
Now, let's go back to our hypothetical um two companies. Let's say they're from asset management. Let's say, uh, we're tracking the workflow of two research analysts in these hypothetical, um, organizations. Well, let's say organization B starts with the tool, the co-pilot. They're asking, "Where can we add AI to the analyst work?" They're looking at the current existing process to say, "Earning transcript comes in. I discovered a summarization workflow. Let me pass all of this in through my fire hose. Analysts get earning summaries faster. Win-win, right? Well, not so much, at least not compared to the guys who are leading. And such a workflow of of being a faster horse introduces one key, um, nuance. That is, you are losing context as you have information, um, I guess reduction as part of your summarization.那么假设中的 A 公司在此情境下在做什么？他们痴迷于提出正确的问题。他们可能会问：“为什么我们的实质性信号到达得太晚？我们应该把注意力、AI、带宽和能力聚焦在何处？我们的瓶颈在哪里？”于是，这就变成了对工作流程的重新设计，或者至少是对聚焦重点的重新评估。这就是他们不同的做法。这种考量并不局限于金融领域，而是贯穿各个领域和职能。你都在问同样的问题——聚焦于什么，无论是研究能力、决策速度、数据质量、延迟还是可靠性，它涵盖了所有这些方面。研究也证实了这一点：相关研究表明，领先企业平均将注意力花在更少、但更高质量的决策上。因此，领先者在何处聚焦以及聚焦于什么方面更具选择性。
英文原文
So, what What is the hypothetical organization A doing in this? Well, they're obsessed with the right problem to ask. They could be asking, "Why are our material signals reaching us too late? Where do we focus the attention, the AI, the bandwidth, the capability on? Where is our bottleneck, right?" And so, it becomes this redesigning of the workflow or at least the reevaluation on what to focus on. That's what they're doing different. Now, this consideration isn't particularly defined finance, it's across the the realm and function. 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. It it it traverses all of that. And studies show this. They show the leading studies, um, on average spend their attention on fewer, higher-quality decisions than the other cohort. So, leaders are being more selective in where they focus their attention and what they focus their attention.这非常好，领先公司清楚正确的问题是什么。那么接下来他们走向何方？他们还将投资集中在最高价值的工作流上。我理解这一点，我也曾有过类似的经历——AI 带来了太多的机会。实验可能是一件好事，但我们需要将实验以及随之而来的“创新剧场”（甚至更糟的“生产力剧场”）与实际的价值创造区分开来。回到这两家假设公司，这意味着什么？B 公司关注的是如何启动最多数量的 AI 试点项目。最终呈现出来的形态就是：这里搞一个文档总结机器人，那里搞一个 Co-pilot，这里再搞一个数据检索机器人，到处都是分散的项目。而 A 公司在投资中所聚焦的，可能是像研究流程端到端工作流这样的用例。他们专注于将工程、分析、AI 能力和判断力对齐到一个单一的焦点上。研究表明，领先者将其 80% 以上的投资投入在这些高价值用例上，从而改造关键业务职能并创造新产品/服务。
英文原文
So, it's great. The leading companies uh have um an idea of what the right problems are. Where do they go from there? Well, they also concentrate their investments in the highest value workflows. 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. And experimentation can be a good thing. Um but one needs to separate experimentation and the resulting innovation theater, or worse, the productivity theater, with actual value generation. Going back to our two um hypothetical companies, what does this mean? Company B is looking at how do we launch the most number of AI pilots? And what that ends up looking like is a document summarization bot here, a co-pilot there, um a a data retrieval bot here. It's scattered all over the place. 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. They're focused on aligning engineering, analytics, AI capacity, and judgment towards a singular focus. And research has shown that the leaders spend more than 80% of their investments on these high-value use cases. Transforming key business functions and creating new offerings.太棒了。我们知道他们专注于寻找正确的问题，并且在精选的工作流上进行深度投资。不仅如此，他们还在做一些不同的事情：他们正在重构工作的开展方式。我认为这一点至关重要，它又回到了我们之前讨论的核心要素——判断力。他们围绕判断力这一关键要素，重新定义和重组职能、角色与能力。对我们的假设公司而言这意味着什么？正如我们所讨论的，B 公司可能专注于：我收到了财报纪要，收到了研究报告，如何对其进行总结？也许我能做得更快；也许我能更快地更新财务模型；也许我能更快地将投资建议发送给投资组合经理。
英文原文
Perfect. We We know they're focusing on um finding the right problem, and they're also spending deep investment in select workflows. Well, they're also doing something different. They're reimagining how work is done. I think this is this is key. And it goes back to what we discussed previously, which is judgment. They're redefining and reorganizing functions, roles, capacities around this key element of judgement. What does that mean for our hypothetical company? Well, company B might be focused on as we discussed I'm getting my earnings transcript, I'm getting my research my research reports. How do I summarize it? Maybe I make that faster. Maybe I update my financial models faster. Maybe I send my recommendation to my PM faster.而 A 公司关注的则是一个完全不同的层级。假设他们可能会要求分析师去定义投资逻辑（investment thesis），并密切观察和收集可能反驳或支持该逻辑的证据。他们会进行持续监控，调查异常情况，并深入探究特定案例，从而带来实际的价值创造。因此，赢家不仅仅是实现自动化，他们是在重新设计。研究表明，高绩效群体中有 75% 的企业实际重新设计了工作流（可能性高出很多），而在非高绩效群体中这一比例仅为 25%。那么在设计正确的问题上，这两家假设公司形成了怎样的对比？B 公司是在现有工作流中加入 AI，完成单个项目后就转向下一个方案；而 A 公司则在重新思考工作是如何开展的，围绕 AI 重新设计流程，并且始终痴迷于正确的问题。这就是组织在运用 AI 时的本质差异。
英文原文
Company A is focused on an entirely different ballgame. Hypothetically, they could be asking the analyst to define the investment thesis. And they're watching and gathering evidence that could refute that thesis or support it. They'd be monitoring continuously. They'd be investigating exceptions. And they could be deep diving on you know, select examples that could lead to actual generation of value. So, the winners just don't just automate, they redesign. 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. So, where does this leave us with our two hypothetical companies and designing the right problem? Company B adds AI to existing workflows. They're completing individual projects and then moving to the next initiative, whereas company A is rethinking how work is done. They're redesigning processes around AI and they're obsessing of the the the right problem. That's that's the difference in how organizations put AI to work.支柱二：构建共享能力
很好。他们选对了问题，接下来会发生什么？众所周知，为了真正释放这种能力，你需要在整个组织内构建共享能力。一段时间以来明显显现的一个趋势是：技术与业务之间的界限正在趋于融合。我们随处可见这种现象：工程师越来越靠近业务结果；业务专业人员正在构建工具和自动化应用。你可以想象，既可以有前置部署工程师（forward deployed engineers），有前置部署产品经理，也可以有反向的前置部署投资组合经理。打破组织壁垒，把正确的人聚集在同一个房间里，这正是领先者真正倚重的方式。研究表明，越来越多的大型企业正将技术部门视为推动业务增长的职能，这为这些公司创造了竞争优势。
英文原文
Great. So, they've picked the right problem. What what happens next? Well, we all know in order to actually enable that capacity, you need to build shared capabilities across the org. 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. And we're seeing this everywhere. We're seeing engineers that are moving closer to business outcomes. We're seeing business professionals that are building tools and automations. 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 portfolio managers. 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. Studies have shown that more and more of the large enterprises are viewing their technology arm as a business growth function. And that's creating an edge for these for these companies.构建共享能力内部的另一个方向可能是我个人最喜欢的，那就是：核心标准化，边缘个性化（standardizing the core and personalizing the edges）。这在技术领域并非新鲜事，我们一直在构建基础设施、共享模块和系统设计。但今天有趣的是，随着现有能力、组织架构以及壁垒的打破，它呈现出了非常不同的形态。取决于你所处的岗位，它的形态可能会大不相同：你可以为一个包含分析师和客户的多元团队构建共享核心，也可以为一群分析师构建，或者为软件工程师或数据科学家构建。
英文原文
So, the other sort of vertical inside building shared capabilities that is probably a personal favorite of mine. It's standardizing the core and personalizing the edges. And this is something that isn't new to the technology world. We've we've built infrastructure, we've built, you know, shared modules and system design. 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. And this could look very different depending on where you sit. You could be building shared cores for you know, a team that looks very different with analysts and and clients. 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.特别以数据科学家为例，这看起来会非常有趣：行业中非常普遍的做法是各自进行一次性工作流，而你可以构建一个共享的核心，涵盖数据集、语义层、权衡取舍（什么有效、什么无效、某些数据集擅长什么、差距在哪里等等），从而使每位新员工在系统中几乎都成为即插即用的模块，能够站在前人的肩膀上工作。那么，后来者不需要重新构建什么？
英文原文
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 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 each new personnel becomes almost a plug-and-play module in the system where they can then stand on the shoulder of their predecessors. So, what should the next person not have to rebuild?同样有趣的是，企业过去并非为智能体（agents）而构建。智能体跨越工作流和应用程序运作，它们获取上下文、进行权衡深思、采取行动；而在过去，企业真正聚焦的是单个员工或一群人与单一应用程序之间的交互。因此，领先者清醒地意识到必须做出改变，他们需要实现基础设施现代化，并且对自身所处的阶段有着清晰的自我认知。研究表明，71% 的企业正在对其核心基础设施进行现代化改造以支持 AI；23% 的企业将年收入的 6% 到 10% 投入于核心系统现代化，以充分利用这一优势。在此过程中，一个常见的抱怨或挑战是数据质量以及数据的可复用性。
英文原文
>> [clears throat]Now, what's also interesting is, um, you know, the enterprise wasn't really built for agents. 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 or a collection of them interacting with the single application. 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. And so, studies show that, you know, 75 71% of the orgs are modernizing their core infrastructure to support AI. 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. And a common complaint here or a common challenge here is, um, you know, data quality and and the ability to reuse data.
太棒了，我们把正确的人召集到了房间里，也拥有了正确的知识。接下来做什么？随后而来的概念是：“我们如何让这些知识可复用且易于获取？”这就是智能体发挥作用的地方。根据采纳程度和所处阶段，你可以拥有不同类型的团队智能体：你可以拥有充当领域专家的专家智能体；你可以拥有跨流程工作的流程智能体，比如在 Slack 中创建、打开和关闭工单的机器人；你还可以拥有真正的“幕僚长”（chief of staff）型智能体，时刻把握组织的运行状态以及需要完成的任务。回到我们的工作流，A 公司在共享能力方面再次展现出截然不同的考量。
英文原文
Um, so, great. We, you know, we we have the right people in the room, we, um, have you know, we have the right knowledge there. What is What do we do next? And then comes the concept of, "Hey, how do we make this knowledge reusable and accessible?" 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, 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 a tab on what the state of the organization is and what needs to be done. So, um you know, going back to our workflows, A again is looking at this very differently in terms of shared capabilities.支柱三：实现优势的复利累积
我想提请大家注意的最后一个支柱是：这个系统究竟何时真正实现复利累积？部署之后会发生什么？领先的企业在思考方式上稍有不同：哪些失败可以成为我们的优势并从中学习？如果取得了成功，如何将其沉淀为系统的一部分？他们的思考方式是：个人学到了，但系统学到了吗？当遇到需要修正的情况时，是否有办法将这些知识反馈给组织——无论是通过新工具、新上下文还是其他方式？而这里的关键在于：在保留这些学习成果的同时，必须保留相应的证据。
英文原文
>> [clears throat]Now, the last pillar here that I want to draw your attention to is well, when does this system really compound? What happens when it's deployed? Well, the leading companies, what they're doing is they're actually thinking about it slightly differently. What are failures that can give us an edge that we can learn from? If if there's a success, how do you make it part of the system? And the way they think about this is, you know, the individual learned, but did the system? 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, or or or what have you. And um what's what's key here is preserving the evidence along with those learnings.
因此，最后一个核心方向在于：AI 释放了能力，这些能力流向了何处？必须清醒地认识到，你需要重新设计系统，并将其真正聚焦于你正在评估、监控并据此形成飞轮效应的关键指标上。回到 A 公司和 B 公司，现状如何？A 公司重新设计了其全部工作方式，他们正在构建能够产生复利的能力，并且建立了共享能力。因此，我想给大家留下最后一个问题：人人都有 AI，谁拥有竞争优势？谢谢大家。
英文原文
So, um really the last vertical here is AI frees capacity. Where does it go? 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. So, where does this leave us for companies A and B? You know, company A has their entire work redesigned. They're building capabilities that compound, and they have shared capabilities built. So, I want to leave you off with one final question. Everyone has AI. Who has the advantage? Thank you.[applause]