说明了解背景的理由

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说明了解背景的理由

使AI超越实验，需要将agents连接到业务上下文，并确定能够提供最大价值的业务流程。

与塞洛尼斯合作

企业AI可以产生建议，自动化任务，帮助组织更快地作出决定。 但是，如果没有对企业实际运作方式的理解，即使是强大的模型也会错过决定一个决定是否是正确的限制和关系。 随着公司期望AI从实验转向日常业务，今后的挑战就是让技术在复杂的业务流程中发挥作用。

对Celonis的AI企业首席科学家Devavrat Shah来说，这种背景是“决策情报”的核心。 他认为，AI agents可以在更大的决策过程中支持个别任务，但他们需要了解每项任务所起的作用和围绕它的业务条件。 “你不能在真空中这样做，” 没有正确的操作环境，agents就可能忽略重要的制约，犯昂贵的错误，或者在试图解决他们需要知道的事情时消耗过多的计算资源。

解决方案始于将AI连接到已经使用的流程和数据企业。 一个背景层可以帮助agents理解工作流程，在定义的护栏内运行，并产生建议，人们可以审查和批准。 Shah建议各组织从具体的商业决定开始，确定最困难的步骤，并检验AI是否能够改进这些步骤。 然后衡量速度和价值的增益有助于确定应用程序是否值得扩大。

这种做法还可以改变雇员的时间使用情况。 将重复的手工工作和协调各小组的任务自动化，可以使人们腾出精力，集中精力作出更复杂的战略决定。 然而，要实现这一利益，就需要对劳动力教育进行投资，使雇员能够理解人工智能产出及其影响，而不只是将决定交给agents。

展望未来，Shah看到各种机会从日常业务任务扩大到财务、劳动力管理、营销和销售等领域的全机构规划。 在适当的情况下，大赦国际可以帮助各组织评估各种选择，并更快地作出相应的决定。 正如Shah所说的那样，“现在，也许[资源规划]不应该花那么长的时间，人们实际上正在迅速做出正确的选择。”

这集"商业实验室"是与Celonis合作制作的。

全文

Megan Tatum：从麻省理工学院的技术评论中，我是Megan Tatum，这是Business Lab，这个节目帮助商业领袖们理解新技术从实验室出来并进入市场。

这个播客是与Celonis合作制作的。

企业AI正处在十字路口经过多年的实验，演示，和飞行员，领导团队需要明确的证明，即扩大规模会对其底线产生积极影响，但许多人缺少谜题不可分割的一部分。 企业AI和AI agents在没有业务环境的情况下行事，表现严重不足。 他们将一般原则应用到特殊情景中，并且变得短暂，使得商界领袖可以理解地无法令人信服，AI的潜力也未能实现。

两个词给你：上下文很重要。

今天的嘉宾是德瓦夫拉特·沙阿塞洛尼斯企业AI首席科学家

欢迎，德瓦夫拉特。

哈罗，梅根。 谢谢你让我来

宾：非常感谢你今天和我们在一起 Devavrat，首先，为什么各组织现在考虑与企业AI有关的业务背景如此重要？是什么使它成为我所说的从实验到规模的过渡的关键部分？

德瓦夫拉特：这是一个很棒的问题。 正如你正确指出的，我们正处在成为实验性主流，就AI而言。 如你所知，我在麻省理工学院作为教授教了AI超过20年，所以我对AI在企业中的未来非常傲慢，这并不奇怪。

我想AI能给企业带来最大影响的方式是决策，帮助企业决策，或者换句话说，我们如何给企业带来决策智能？在我们考虑决定时，我们今天如何作出决定？带MIT Tech Review，带Celonis，带麻省理工，带任何大型组织。 我们有不同类型的决定，我们想在很宽的句子上作出。 有些决定是战略性决定，如零星决定，其他决定是可操作的，需要以高频方式作出。 在其中任何一个国家，我们都有一些进程。 你先做，你先做，你先做，你先做。 我们许多人帮助协调这些事情。 我们很多人都在其中扮演角色黄詹森称之为任务一旦你一步一步地完成这些事情，你就能做出决定。

现在，正是这些任务，或者我称之为角色，那就是AI可以带来一种原子方式的代理力量，这样我们至少可以做一些决定，如果不是所有的决定，我们可以增加支持，也许有时当它是一个痛苦的自动化。 为了达到这个目的，知道要发挥什么作用，要完成什么任务，我们确实需要了解背景。 你不能在真空中这样做。 这也是为什么与AI用正确的方式组合起来将我称之为决策情报的东西带到企业。 这将是最大的转变。

对更详细地说，我们知道，许多组织，正如你说的，正在接受身份识别的AI，具有这种越来越自主的决策潜力。 如果上下文不是推出并行，那么商业风险会有什么作用？

德瓦夫拉特：太棒了。 我们来谈谈我描述的场景我们正在根据决定类型，视任务类型，努力使一个角色自动化或支持一个角色或增加一个角色。 我们再设想一下，所有这些所谓的前沿实验室已经建立了这些惊人的模型和惊人的AI基础设施，这样宇宙中发生的一切都有可能发生在宇宙中。

现在，我们有一个小的决定，我们想为这个非常具体的背景做决定。 这个庞大的系统无法进入，这个非常聪明的人最终会通过问很多问题来发现，我们作为人类这样做的方式，然后做正确的事情。 问题是，要做到这一点，它会做很多的计算在某种程度上，为了完成简单的任务，我们正在花费大量的计算。 所以，这是情景一。

在现实中，这不会是场景，我们将错过一些重要的背景，重要的制约，重要的护栏，然后我们就会犯错误。 有时候这个错误会好起来的这就像我给你的推荐说，去A餐厅，也许你可能不享受晚餐，有时错误会非常，非常昂贵。 这就是我告诉你财务预测是什么不知何故导致公司末日。

对因此，在企业环境中，这些都是真正的商业关键决定正在做出。 如果我们搞错了影响会更大不是吗那么，从实际的角度看，各组织在这个背景层中可以增加哪些不同的方式？例如，Celonis如何通过一个组织的实时数字双胞胎提供这种业务环境？

当然我想只要双击塞洛尼斯的历史也许有用如你所知，Celonis作为一个组织随使命而诞生，其目标是帮助各组织坚持其理想进程，建立更好的进程，使人们能够以相互交织的方式与多个进程合作。 这就是他们的主要，让我们称之为DNA，一直关于我所谓的过程智能。 把握企业如何帮助将多种事物组合起来，实现数字化，让人们更好地掌握自己的进程，了解问题所在，然后如何重新设计组织内部的进程，使事情发生得更好。

现在，如果你想把决策智能带进这个过程，这个过程的每一个原子步骤，都有可能是由专家，人们做的，现在我们希望agents帮助我们实现这个目标。 那就是在企业内部了解这个过程的背景，然后把AI agents 和它一起，我想称之为agents，确切地提供了正确的方法，将决策情报带给企业。 这也是Celonis 带来背景如何帮助企业做出更好的决定。

对然后从这个方法的角度讲，上下文层与下面的层有什么关系，也就是记录层的系统，以及上面的层，那个身份层？

德瓦夫拉特：这是个很棒的问题，梅根。 下面的层主要是，如果我们称之为记录系统，主要是工作流程工具在那里，然后是它们围绕它生成的事件日志。 上面的一层要么是商业用户专家消费信息，要么是他们消费信息也许通过agents工作在上下文层。 正如Celonis所说的，这里的上下文模型被设计为同时做三件事。 一是获得下面层的正确抽象，使过程的正确片段能够被代理。 二是以结构化的护栏方式为每块带回滚动的agents. 然后三个是agent，首先这样，如果有其他的代理基础设施已经与之交谈，他们可以自然地与之交谈，这样他们就可以给人类或专家带来正确的产出来审查，看，批准，然后也许有时可以自动化。

赞极了而对于我们在谈论的这些好处，在执行上下文层时是否也存在挑战？例如，大多数组织是否有适当的数据向大赦国际提供它所需要的背景？如果没有，我想知道它们可能需要采取哪些实际步骤来解决这一问题。

德瓦夫拉特(Devavrat)：这又是，一个关键的问题，让这成为现实。 我们可以想象一个理想的世界，但是如果我们不能到达理想的世界，那它就只是理想。 因此，在这个背景下，当我们讨论这三层时，第一层是记录和工作流程工具系统下层。 从某种意义上说，这些东西已经存在。 他们不需要完整。 他们不需要最干净的方式。 上下文模型与代理建筑的设计方式，它能够与那些东西连接起来，并让它有意义。

然后上面的事情就非常控制着塞洛尼斯的基础设施，所以没那么复杂。 这里的关键是，当人们进入企业，当人们打开，让我们说一个系统记录或工作流程工具，事件日志或类似的东西，一个人必须经历，我称之为古典信息塞克，以确保你能够相信一个供应商的软件进入你的组织。 我认为，这是主要的进程，是介于我可以说是前后的一步。

对因此，这是真正的关键步骤人们将在这里采取。 既然有背景的AI agents能够做出更复杂，更准确的决定，我想知道你认为人类智力和投入的作用会如何向前发展，而组织如何在两者之间达成平衡？

达瓦

本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。

Sponsored

Making the case for context-aware AI

Moving AI beyond experimentation requires connecting agents to operational context and identifying the business processes where they can deliver the greatest value.

In partnership withCelonis

Enterprise AI can generate recommendations, automate tasks, and help organizations make decisions faster. But without an understanding of how a business actually operates, even powerful models can miss the constraints and relationships that determine whether a decision is the right one. As companies look to move AI from experimentation into everyday operations, the challenge ahead is giving technology the context to act usefully within complex business processes.

For Devavrat Shah, chief scientist of enterprise AI at Celonis, that context is central to what he calls “decision intelligence.” AI agents can support individual tasks within larger decision-making processes, he argues, but they need to understand the role each task plays and the business conditions surrounding it. “You can't just do that in a vacuum,” he says. Without the right operational context, agents risk overlooking important constraints, making costly mistakes, or consuming excessive computing resources as they try to work out what they need to know.

The solution begins with connecting AI to the processes and data enterprises already use. A contextual layer can help agents understand workflows, operate within defined guardrails, and produce recommendations that people can review and approve. Shah advises organizations to start with a specific business decision, identify its most difficult steps, and test whether AI can improve them. Measuring gains in speed and value can then help determine whether an application is worth scaling.

This approach could also change how employees spend their time. Automating repetitive, manual work and coordinating tasks across teams could free people to focus on more complex strategic decisions. Realizing that benefit, however, will require investment in workforce education, so employees can understand AI outputs and their implications instead of just handing decisions over to agents.

Looking to the future, Shah sees opportunities extending from routine operational tasks to enterprise-wide planning in areas such as finance, workforce management, marketing, and sales. With the right context, AI could help organizations evaluate options and make consequential decisions more quickly. As Shah puts it, “Now, maybe [resource planning] should not take that much time, and people are actually making the right choices quickly.”

This episode of Business Lab is produced in partnership with Celonis.

Full Transcript

Megan Tatum: From MIT Technology Review, I'm Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.

This podcast is produced in partnership with Celonis.

Now, enterprise AI is at something of a crossroads. After years of experimentation, demos, and pilots, leadership teams want clear proof that scaling up will positively impact their bottom line, but many are missing an integral part of the puzzle. By acting without operational context, enterprise AI and AI agents are performing at a significant deficit. They're applying generalized principles to specialized scenarios and coming up short, leaving business leaders understandably unconvinced and AI's potential unrealized.

Two words for you: context matters.

My guest today is Devavrat Shah, chief scientist of enterprise AI at Celonis.

Welcome, Devavrat.

Devavrat Shah: Hello, Megan. Thanks for having me here.

Megan: Thank you so much for being here with us today. Devavrat, just to start, why is it so crucial that organizations are thinking about operational context as it relates to enterprise AI right now? What makes it such a critical part of that transition I was talking about from experimentation to scale?

Devavrat: It's a fantastic question. And as you correctly noted, we're at the cusp of becoming experimental to mainstream as far as AI is concerned. As you know, I've been teaching AI at MIT as a professor for more than the last two decades, so it's no surprise that I'm very bullish about AI's future in enterprises.

The way I think about where AI can make the biggest difference in enterprises is in decision making, helping enterprise make decisions, or putting it other way, how do we bring decision intelligence to enterprises? As we think about decisions, how do we make decisions today? Take MIT Tech Review, take Celonis, take MIT, take any large organization. We have different types of decisions we want to make in very broad strokes. There are decisions which are strategic decisions like episodic, other decisions which are operational that need to be done with a high frequency. In either of them, we have some processes in place. You do this first, you do that first, you do that first, you do that first. Many of us help coordinate those things. Many of us play roles within it, what Jensen Huang calls task. And once you get those things done step by step, then you reach decision.

Now, it's those tasks, or what I would call roles, that's where AI can bring in an atomic manner agentic power so that we can at least parts of decisions, if not all of the decisions, we can augment support and maybe sometimes when it's a painful automate. And to get to that, to know which role to do, which task to do, we really need to know the context. You can't just do that in a vacuum. And that's why context with AI put together in the right way would bring what I would call decision intelligence to enterprise. And that's where I think there'll be the biggest transformation that would come.

Megan: Right. To drill down into that into a bit more detail, we know many organizations are, as you say, embracing identic AI right now with this potential for increasingly autonomous decision making. If context isn't introduced in parallel, what are the commercial risks at play?

Devavrat: Fantastic. Let's just take the setting I was describing. We are trying to automate a role or support a role or augment a role depending on the type of decision, depending upon type of task. And let's for a second imagine that all these, as they're called, frontier labs have built these amazing models and amazing AI infrastructure so that everything that's happened in the universe and it's likely to happen in the universe is all there.

Now, we have a small decision that we want to make for that very specific context is there. And without having access to that, this massive system, this extremely bright person would figure that out eventually by asking lots of questions, the way we do it as humans, and then finally do the right thing. The problem is to get to that, it will do so much compute at some level that to get simple tasks done, we are spending massive amount of compute. So that's scenario one.

In reality, that's not going to be the scenario, and we'll miss some important context, important constraint, important guardrail, and then we'll make a mistake. And sometimes that mistake can be okay. It's like me giving you a recommendation saying, go restaurant A, and maybe you may not enjoy the dinner, versus sometimes mistakes will be very, very expensive. That is me telling you what the financial forecast is and somehow that leads to the company's doom.

Megan: Right. So in an enterprise setting, these are really business critical decisions that are being made. So the impact is that much more if we get this wrong, isn't it? And on a practical level then, what are the different ways that organizations can add in this context layer we're talking about? How does Celonis, for example, deliver that operational context via a real time digital twin of an organization?

Devavrat: Absolutely. I think if it might be useful just to double click on the history of Celonis. As you know, Celonis, as an organization, came into existence with the mission, with the goal of helping organizations adhere to their ideal processes, build better processes, enable people to work with multiple processes in an intertwined manner. And that's where their primary, let's call it a DNA, has been about what I would call process intelligence. Take how enterprises help put multiple things together, digitize them, enable people to get their processes better, understand where the issues are, and then how do you re-engineer the process within organizations so that things can happen better.

Now, if you want to bring decision intelligence into this, each atomic step of this process that was potentially was done before by experts, people, now we want agents to help us enable that. And that's where knowing this context of a process within enterprise and then bringing AI agents along with that, what I would like to call it as a roled agents, exactly provide the right way to bring decision intelligence to enterprises. And this is how Celonis brings context to help enterprise make better decision.

Megan: Right. And then in terms of how this works as well, how does the context layer relate to both the layer below, that's the systems of record layer, and also the layer above, that identic layer?

Devavrat: That's a fantastic question, Megan. The layer below primarily is, if we call it system of record, primarily it's the workflow tools that are there and then the event logs that they generate around it. The layer above is either business user experts consuming information, or they're consuming that information maybe through agents working on top of that context layer. And this is where the way context model, as Celonis calls it, is designed is to do three things simultaneously. One is to get the right abstraction of the layer below, so that the right pieces of the process can be agentified. Two is to bring the rolled agents for each of these pieces in a structured guardrailed manner. And then the three is to be agent first so that if there are other agentic infrastructure that are already talking to it, they can naturally talk to it so that then they can bring the right output for humans or experts to review, look at it, approve, and maybe then at times automate.

Megan: Fantastic. And for all the benefits that we're talking about with this, are there also challenges when it comes to implementing a context layer? For example, do most organizations have the right data in place to provide AI with the context it needs? And if not, I wonder what some of the practical steps are that they might need to take to address that.

Devavrat: It's again, a crucial question to make this real. We can imagine an ideal world, but if we cannot reach the ideal world, then it's just ideal. So now in this setting, as we discussed the three layers, one being the layer which is the layer below system of record and workflow tools. In some sense, those things already exist. They don't need to be complete. They don't need to be in the cleanest possible manner. The way context model is designed with agentic's architecture, it manages to connect to those things and make sense of it.

And then things on top is very much in control of Celonis's infrastructure, so it's not that complicated. The crucial thing here though is when one goes to enterprise, when one opens up, let's say a system of record or workflow tool, event logs or things like that, one has to go through, what I would call, classical InfoSec to make sure that you can trust a vendor's software that's coming into your organization. And I think that is the primary process that is a step between, before and after, if I may call it.

Megan: Right. So that's the real crucial step for people to be taking here. And as AI agents with context are able to make more complex, more accurate hopefully decisions, I wonder what you think the role of human intelligence and input will be moving forward, and how can organizations perhaps strike the balance between the two?

Deva

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