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博斯敦 — — 在这张与Matthias Patzak,AWS执行官一起居住的QQA中，来自周三供应商的媒体活动，Patzak讨论了AWS Reimagine的一些发现和见解：9月将AI变成价值报告发布。
对话涉及AI治理，AI agents，并从代理系统衍生出ROI.
Patzak是一位资深的德国技术学家，他的职业生涯专注于敏捷和组织重新设计以及AI技术。
AWS 客户曾向您提出过哪些关于AI的最突出的关注？.
Matthias Patzak：我想说，最常见的关注是治理，但它不同。 在一些组织，费用是一个更大的问题。 其他人则更关心收养问题。 大部分，也许我治理后的第二个话题，是他们看不到任何价值。 所有这些话题都是非常有趣的对话。 多数情况下，我一直专注于创新和驱动价值，因为如果你解决了价值问题，那么你就可以把对话从成本对话转变为AI的回报。 对客户来说，ROI的对话越来越容易了。
至于治理，你是否与不同国家的组织交谈？他们怎么缓解呢？
Patzak：是的，所以我确实在国际上工作。 我主要与客户合作，他们面临着与客户一样的挑战，因为我们团队提供的价值是基于经验，而不是书籍知识。 我和汽车公司合作我和银行合作在软件开发中使用AI，对治理有模糊理解和规则模糊的组织，大多在模糊的政策中界定，也不太擅长为其agents建立善治。
令人惊讶的是，在采用和规模化AI方面最先进的组织在金融服务行业，因为它们有许多治理的写法，因此可以将这些指导文件作为背景提供给agent，这实际上并不是最佳做法。 但agents需要知道他们必须遵守什么规则。 因此，重要的是要把治理建设成一个基础设施，一个容器，在那里你可以部署agent。 然后将治理放到平台上，为agents设定界限。 因此，制造业，汽车业，金融服务公司在为其客户创造适当治理方面最为先进。 这些组织，特别是在德国，还有它们的汽车公司，现在成本压力很大，中国竞争激烈。 他们对部署AI重新设计业务流程感到好奇和雄心勃勃。 他们不仅仅利用AI为员工提供聊天机；他们真的开始将AI治理视为一个重要的业务流程，以及如何改进它。
我想和你谈谈重新设计决定的想法。 我认为这是Reimagine：把AI变成价值报告的一部分。 看起来像什么？它意味着什么重新设计决定，或让AI 浏览您正在重新设计或工作的决定？
帕特扎克：我看到了几种不同的方式。 主题专家使用AI工具将记录不全的流程和应用程序数字化，大多取消Excel表。 这是第一部分，但这是非常局部的优化。 但我已经看到一些客户在租赁部门或汽车制造厂中设有主题专家，制造agent，之后被200位同事使用。 这真是自下而上的数字化，当我问这两个不同组织的人为什么他们以前没有这样做时，答案是没有预算。 要求预算是极其复杂的。 这需要很多时间。 另一种做法是各组织采取业务流程，而不是试图优化当地业务流程，而是真正通过代理AI优化目前的业务流程。 然后，我看到另一个团体。 例如，有一个德国大银行的CIO. 他把50名最能干的人员带出办公室一个星期，把他们送进酒店，称，“嘿，我们最复杂的业务流程是什么？” 称是抵押贷款如果一个私人要求抵押，它是一个 复杂的过程。 通常，你要马上得到答案。 你有抵押贷款吗？利率为多少？但有时它可能需要几天甚至几周的时间，因为它是一个复杂的过程。 而他称，“从头开始这个商业过程。”
当你说人工智能本地，你如何定义呢？因为这个词经常被打乱
帕扎克：很多。 有趣的问题。 因此，它从尽可能少的人类参与这个商业过程开始，最大限度地实现自动化。
关于数据，你似乎说我们有不同的agents. 数据附于该agent. 但是，他们是否有时间一起工作，或者你如何确保这些agents的管弦乐顺利进行？
帕扎克(Patzak)：目前的最佳做法是，这是一个不断演变的空间，那就是你有一个agent。 真正行不通的是假设你创建了agent,agent不会出任何错误。 因此，你需要第二个agent和第三个agent在不同的角色。 特定角色中的Agents需要深刻的知识，但其背景windows是有限的。 他们需要agent来监督整个过程，但目前它作为各组织和开发者必须建立的单个软件组件来执行。 我们可能很快就会看到产品。
AI Business and TechTalget AI的主新闻编辑Shaun Sutner编辑了这次采访，以明确和简洁。
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。
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BOSTON – In this Q&A with Matthias Patzak, AWS executive in residence, from the vendor’s media event on Wednesday, Patzak discussed some of the findings and insights from the AWS Reimagine: Turning AI into Value report released in September.
The conversation touches on AI governance, AI agents and deriving ROI from agentic systems.
Patzak is a veteran German technologist whose career has focused on agile and organizational redesign and AI technology.
What have been some of the most prominent concerns that AWS customers have brought up with you about AI?
Matthias Patzak: I’d say the most common concerns are about governance and using AI in a responsible way. But it differs. In some organizations, cost is a greater concern. Others are more concerned about adoption. And a large part, maybe my number two topic after governance, is that they don't see any value. All these topics are very interesting conversations. Mostly, I have been focusing on innovation and driving value because if you solve the value issue, then you can turn the conversation from a cost conversation to a return on AI. ROI conversations are getting easier for the customers.
As for governance, do you speak to organizations in different countries? How do they try to mitigate that?
Patzak: Yes, so I do work internationally. I mostly engage with customers who face the same challenges I solved as a customer, because the value our team provides is based on experience, not book knowledge. So, I work with automotive companies. I work with banks. It’s always in regard to using AI in software development and creating agents and business processes. Organizations with a vague understanding of governance and vague rules, mostly defined in vague policies, are also not very good at establishing good governance for their agents.
Surprisingly, the organizations that are most advanced in adopting and scaling AI are in the financial services industry, because they have a lot of governance written down, so they can provide these guidance documents as context to an agent, which is not really a best practice. But the agents need to know which rules they have to follow. So, it’s important to create governance as an infrastructure, as a container, where you can deploy the agent. Then put governance in the platform and set boundaries for the agents. So, manufacturing, automotive, and financial services companies are the most advanced in creating proper governance for their customers. These are also the organizations, especially in Germany, with their automotive companies, that have high cost pressure right now, with a lot of competition from China. They are curious and ambitious about deploying AI for redesigning business processes. They do not just use AI to provide chatbots for employees; they are really starting to think about AI governance as an important business process and how they can improve it.
I wanted to talk to you about that idea of redesigned decisions. I think that was part of the Reimagine: Turning AI into Value report. What does that look like? What does it mean to redesign decisions, or to let AI navigate the decisions that you're redesigning or working on?
Patzak: I see several different ways. Subject matter experts use AI tooling to digitize poorly documented processes and applications, mostly eliminating Excel sheets. This is the first part, but this is very local optimization. But I've seen with some customers that have a subject-matter expert in a leasing department or an automotive factory plant who creates an agent, which is then used by 200 colleagues. It’s truly bottom-up digitization, which impressed me because it wasn't possible before. And then when I asked these two people in two different organizations in the research why they hadn’t done this before, the answer was that there was no budget. Asking for budget is super complicated. It takes a lot of time. Another is organizations taking a business process and not trying to optimize it locally but really optimizing the current business process with agentic AI. And then I see another group. For example, there was a CIO of a large German bank. He took 50 of his most capable people out of the office for a week, put them in a hotel, and said, ‘Hey, what is the most complicated business process we have?’ They said it's mortgages. If a private person asks for a mortgage, it's a complicated process. Usually, you want an answer instantly. Do you get a mortgage? What is the interest rate? But sometimes it can take days or even weeks, because it's a complicated process. And he said, ‘Reimagine this business process from scratch.’
When you say AI native, how do you define that? Because that term gets thrown around a lot.
Patzak: A lot. Interesting question. So, it starts with as few humans as possible involved in this business process, maximizing automation.
Regarding the data, it seems you're saying we have different agents. The data is attached to that agent. But is there a time when they work together, or how do you make sure that the orchestration of those agents runs smoothly?
Patzak: The current best practice, and it’s an evolving space, is that you have an orchestration agent. What really doesn't work is assuming you create an agent and that the agent won't make any errors. So, you need a second agent and a third agent in a different role. Agents in specific roles need deep knowledge, but their context windows are limited. They need an orchestration agent to oversee the overall process, but it is currently implemented as individual software components that organizations and developers must set up. We probably will see products soon.
Shaun Sutner, lead news editor of AI Business and TechTarget AI, edited this interview for clarity and conciseness.
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