The Chief AI Officer: Scientist, Architect, Coach — Rania Khalaf, WSO2
频道: AI Engineer
视频: https://www.youtube.com/watch?v=9cJrbj23fOA
原文语言: en
统计: 共 13 轮 · Rania 13
[0:01] Rania
[music] Hi everyone. How are you? All right. I'm so excited to see all of you here still awake and ready. Um, so today I wanted to uh share my experiences on the chief AI officer role. It's very new. I get a lot of questions about it. I've been in this role for nearly two years and some variant of it for about the last five. And um I wanted to so to get started I just would like to get an idea about the folks around here. Um how many are in companies that build and sell software products? All right, great. Um, and um, how many folks are uh, have someone leading AI that is not the CEO or the CTO? All right, very cool. So, we'll we'll jump right in. This helps. Um, so like I was mentioning, the stroll is new, but it's becoming more and more popular. And what I found is that it means drastically different things based on two criteria. One is the company, the type of company you're working for and two is the level of AI maturity of that company. There's also another dimension which really is depending on the skill set and the background of the person in the role and I've seen that also vary a lot and in some cases the role was created for a particular person that brings a particular skill set right um in other cases there was a very specific need and people hired in for it right so we'll see because it's so hard to hire for this role specifically we'll also see that um there's a lot of open-mindedness in the companies that are recruiting for it for exactly what the person might do based on the person they find and how well that fits and if they can complement it. So I've also seen it sometimes breaking into two. Um but the really interesting things is you see like even these IBM studies one year apart uh so it was like 11% in 2024 26% in 25 and now 76%.
（音乐）大家好，都还好吗？好嘞。看到各位到这会儿还醒着、还这么有精神，我特别开心。今天我想跟大家分享一下我做首席 AI 官（CAIO）这个角色的经历。这个角色非常新，我老被人问起。我在这个位置上快两年了，而它的各种变体，我前前后后做了差不多五年。开始之前，我想先了解一下在座各位的情况。有多少人所在的公司是做软件产品、卖软件产品的？好，很好。那有多少人的公司里，牵头负责 AI 的那个人既不是 CEO 也不是 CTO？好，很不错。那我们就直接开始，这下我心里有数了。就像我刚才说的，这个角色很新，但越来越普遍。而我发现，它的含义会因为两个条件而天差地别：一是公司，也就是你所在的是哪一类公司；二是这家公司的 AI 成熟度。其实还有一个维度，取决于坐在这个位置上的人自己的技能和背景，这一点我看到的差别也非常大。有些情况下，这个角色是为某个特定的人设的，因为他带来了一套特定的本事，对吧；另一些情况下，是公司有个非常明确的需求，然后招人进来干这件事，对吧。所以我们会看到，正因为这个角色实在太难招，招人的公司对这个人到底要做什么其实相当开放——看他们找到的是什么样的人、契合得怎么样、公司能不能把其余的部分补上。所以我也见过有时候这个角色被拆成两个。但真正有意思的是，你看，就连 IBM 的这些调研，前后只隔一年——2024 年大概是 11%，25 年是 26%，现在是 76%。
[2:24] Rania
Um, okay. So, the tricky thing about this role is like it's so great. Okay, I have an AI officer, but there's AI in everything, right? It's kind of like saying digital or I don't know, electricity. It it's in everything and it can be so many things and it can be really overwhelming, right? So, you have to have a lot of discipline to do it well and to still have joy and not to get overwhelmed and overloaded. Um, so how can we do that? So in my experience, it's been about breaking it into three focus areas. Um I use the word scientist, architect, and coach. I'll explain a little more. So scientist is more um you're exploring, you're experimenting, you're building. Architect, you're also building, but you're also doing a lot more strategy. If the company is selling software product, you're really thinking about creating new software, you're impacting the product strategy. um if the company doesn't build software product, you're creating you're adding AI in ways that affect not only the bottom line and like cost takeout and productivity but also the top line in terms of um new revenue streams or getting to new markets and so on. Um I'm putting also there is one piece that is uh quite important here.
嗯，好。这个角色棘手的地方在于——听上去挺好，行，我有个 AI 官了，可 AI 什么里头都有啊，对吧？这有点像说“数字化”，或者，我也不知道，“电”。它无处不在，可以是太多太多东西，真的会让人不堪重负，对吧？所以你得非常自律，才能把它做好，同时还保有乐趣，不至于被压垮、被塞爆。那怎么做到呢？就我的经验来说，是把它拆成三个重点领域。我用的词是科学家、架构师和教练，下面稍微展开讲讲。科学家更偏向——你在探索、在做实验、在动手构建。架构师呢，你也在构建，但做的战略层面的事要多得多。如果公司是卖软件产品的，你其实是在琢磨怎么做出新的软件，你在影响产品战略。如果公司不做软件产品，那你是在把 AI 加进去，加的方式不光影响利润（bottom line），比如削减成本、提升生产力，还影响营收（top line），比如开辟新的收入来源、打进新的市场等等。另外我这里还放了一块，这一块相当重要。
[3:42] Rania
So like I'm in a board-facing role, right? So I have to report out to the to our board in both this company and the previous one I was in. And so for that there's also part of the job that's like responsibility for the what we're doing in AI and how that is helping the company and our customers. And finally for coach the coach one of surprised me. Um I had spent 20 years in IBM research um running about a third of the global research AI organization and when I took my first job in this role I forgot that you have to really spend a lot of time um evangelizing educating sharing overcommunicating about what you're doing about what how AI can help about ways people can adopt it not only to your employees but also to your customers and now in this role I also spend a lot of time as like trusted advisor to many of our customers. So people in my role at other customers or CTOs that are starting to adopt AI, right? They they want to learn about okay what's happening in other places. So I do a lot of calls with customers and I'm never going to be like here buy my thing, right? Um that happens later downstream if they're interested. My discussions are always what problems are you having? This is how we think about the problem.
比如我这个角色是面向董事会的，对吧？所以我得向我们的董事会汇报，现在这家公司是这样，我之前待的那家也是这样。所以这份工作里还有一部分，是要对我们在 AI 上做的事负责，对它怎么帮到公司和客户负责。最后说教练。教练这一块有点出乎我的意料。我在 IBM Research 待了 20 年，管着全球研究院 AI 组织大约三分之一的摊子，可我第一次接这类角色的时候，却忘了一件事：你真的得花大量时间去布道、去教育、去分享、去反复沟通到过头的程度——讲你在做什么，讲 AI 能怎么帮上忙，讲大家可以用哪些方式把它用起来，不光对员工讲，也要对客户讲。到了现在这个角色，我还花很多时间给我们的不少客户当可信赖的顾问。也就是别的客户那边跟我同样角色的人，或者刚开始采用 AI 的 CTO，对吧？他们想了解：好，别的地方都在发生什么。所以我跟客户通很多电话，而且我绝不会上来就说“来，买我的东西”，对吧？那是后面下游的事，他们有兴趣的话自然会走到那一步。我聊的永远是：你们遇到了什么问题？我们是这样看这个问题的。
[5:01] Rania
And I have no slides, but that's okay. Does it come back? Well, while you fix it, I'll say a few more words. Right. So, the conversation I have with them is more of a a trusted advisor. Like, I understand what problems they're having. I see if our technology fits. If our stuff doesn't fit, I might direct them to another thing that works much better for them and where they are. And sometimes they'll come back or they'll just want to have a discussion again. Um, we have many products uh that are also not AI first. So, you know, we I might talk to some customers that don't use any of our AI capability, right? But they want to think about what they could do in the future. Thank you. And then each of these focus areas has a range and I think this range really depends on the kind of company and the skill set that you're bringing to the role. So for the scientists like even at one set you don't have to have a PhD you're just exploring heavily. I do believe in this AI world that's moving so so quickly, um, you have to always have some of your team experimenting and understanding what's new and trying it out and seeing how it fits, right? I don't think there's a way around that because the tech is moving so fast. Um, but on the other end is like the creator inventor side of the house for the architects. I've also seen I was surprised by this because I discovered it when I got an email um saying something like uh AI education for chief AI officers and I'm like wait what like how do you get this job if you don't know about AI and then I realized that there are some companies that they have someone in the role that really understands the business and they're really a steward so they have a budget and they're trying to understand you know people propose projects and they're trying to understand which projects will bring enough value and they prioritize and they allocate budget and they project manage them and they report out right so
我的幻灯片没了，不过没关系。它还会回来吗？好吧，你们修着，我再多说几句。对，我跟他们的对话更像是一个可信赖的顾问。就是说，我弄明白他们遇到了什么问题，看看我们的技术合不合适。如果我们的东西不合适，我可能会把他们引到另一个对他们、对他们当下所处阶段管用得多的东西上去。有时候他们之后会再回来，或者只是想再聊一次。我们还有很多产品并不是 AI 优先的。所以，我可能会跟一些完全没用我们任何 AI 能力的客户聊，对吧？但他们想琢磨琢磨将来能做点什么。谢谢。接下来，这几个重点领域每一个都有一个区间，我觉得这个区间落在哪儿，确实取决于公司的类型，以及你带到这个角色里来的技能。拿科学家来说，就算在其中一端，你也不需要有 PhD，你只是在大量地探索。我确实相信，在这个变化快得不得了的 AI 世界里，你团队里必须始终有人在做实验、在搞懂新东西、上手试一试、看它合不合用，对吧？我觉得这事绕不过去，因为技术跑得太快了。而另一端呢，就是创造者、发明者那一头。再说架构师。我还见过——这事让我挺意外的，因为我是收到一封邮件才发现的，邮件里写的大概是“面向首席 AI 官的 AI 教育”，我当时就想：等等，什么？不懂 AI 是怎么拿到这份工作的？后来我才明白，有些公司里坐在这个位置上的人是真的懂业务，他们其实是个管家：手里有一笔预算，他们要搞清楚——你知道，大家会提项目上来，他们要搞清楚哪些项目能带来足够的价值，然后排优先级、分预算、做项目管理、再往上汇报，对吧？所以
[6:56] Rania
for that you don't need deep AI knowledge but you need to understand the business really well and be a very strong operator right on the other side is more like what I'm doing which is like you're shaping the whole strategy of the company you're reinventing how we do things in terms of our productivity but also in terms of the products and the strategy in the go to market uh and finally for coaching it also depends on the kind of company if you're in a company that's not really selling an AI product, you're spending a lot of time coaching inward. You're coaching your uh the different teams, the the employees about how to use things. You want them to discover for themselves things like hallucination because you can explain until you're blue in the face, but people don't believe it until they experience it. Um and then, uh as the company has more maturity, you're doing more coaching outward, right? So, working with customers, working with the community and and things like that. So uh I also uh worked with Claude and Gemini to help me like analyze resumes of people in the role, right? And also job postings of people in the role and match them to these slider directions and see like what it thinks. And these are some of the areas it found. So this validates that like really it's the person. So um the row is the type of persona and their skill set and then the columns are the sliders, right?
干那个你不需要很深的 AI 知识，但你得把业务吃得很透，还得是个很强的运营型管理者，对吧？另一头呢，就更像我在做的事：你在塑造公司的整个战略，你在重新发明我们做事的方式——既包括我们的生产力，也包括产品、战略和 go-to-market（市场打法）。最后说教练，这也取决于公司的类型。如果你所在的公司其实并不卖 AI 产品，那你会花大量时间向内做教练：教各个团队、教员工怎么用这些东西。你要让他们自己去发现比如幻觉这类问题，因为你可以讲到口干舌燥，但人不亲身经历一回是不会信的。然后，随着公司的成熟度提高，你向外做的教练会更多，对吧？也就是跟客户打交道、跟社区打交道，诸如此类。另外，我还找了 Claude 和 Gemini 帮我分析在这个角色上的人的简历，对吧？还有这个角色的招聘启事，把它们对应到这几个滑块的方向上，看看它怎么判断。这些就是它找出来的一些类别。这也印证了，真的是因人而异。所以，行是人物类型和他们的技能，列是那几个滑块，对吧？
[8:21] Rania
Um so I'm going to give some examples from my own journey because this is area is so fluid and new. So I think that's the the most useful I hope thing to do. Uh so after IBM research I joined a biotech uh unicorn in the Cambridge area that was working in agriculture. So they were using crisper for corn, soy and wheat to make plants that make more food and use less water. But to do those experiments took 10 years and a ton of land to validate like if your gene edit would work. So you needed methods to help the scientists make better hypothesis of what edits to make. Right? So that was the way in the door for the AI and data part. Um and then when I got there they were like oh you are like a computer person. They are more like they had amazing geneticists and amazing act people but not a lot of tech people from like engineering. So they're like, "We're also going to give you it and please hire a CISO." And we had no data engineers. So like that mandate grew very quickly, very far beyond the let's build AI together. Um but that company sold bags of seeds, right? So we're in IBM, I was building platform here. I was like we we end up uh to be a fully AWS shop. I bought data bricks, right?
接下来我举一些我自己这一路走来的例子，因为这个领域太不固定、太新了。所以我觉得这是——但愿是——最有用的做法。离开 IBM Research 之后，我加入了剑桥（Cambridge，美国麻省）一带一家做农业的生物科技独角兽。他们用 CRISPR 改造玉米、大豆和小麦，培育出产粮更多、用水更少的作物。但做这些实验，要花 10 年时间和大片大片的土地，才能验证你的基因编辑到底管不管用。所以就需要一些方法，帮科学家对该做哪些编辑提出更好的假设，对吧？这就是 AI 和数据这一块进门的切入口。然后我到了那儿，他们说：哦，你是个搞计算机的。他们更多是——他们有非常出色的遗传学家和非常出色的农业人才，但工程出身的技术人员不多。于是他们说：“IT 我们也一并交给你，另外麻烦你招一个 CISO。”而且我们一个数据工程师都没有。所以这份职责很快就膨胀开来，远远超出了“我们一起来做 AI 吧”。不过那家公司卖的是一袋一袋的种子，对吧？所以，在 IBM 我是做平台的，到了这儿，我的做法是——我们最后成了一家全盘用 AWS 的公司，我买了 Databricks，对吧？
[9:37] Rania
Right. And then I spent my engineers time building new algorithms for gene discovery and um moving from uh box to Microsoft and you know things like that the IT side which uh was interesting uh but uh not my joy and passion. So that was a very different experience and a very different skill set of what to do there. But also the AI there was also fundamentally uh changing what the how the company operates and its ability to have great success. Right? So if you go back to the sliders and so on. So I would say like at WS2 which is the job I have now I'm at about like 20% scientist I want to say 60% architect 20% coach right. Um and these uh sliders below are also showing like where I'm at in each of the dimensions. Right? So we have a small research team there that uh we've done some very interesting work. We've really worked to change the strategy of the company that's very focused now on the agentic enterprise fabric and I spent a lot of time coaching mainly outward. We have a very techforward employee base. are about 75% technical people uh on in the whole company and people are super super curious right it's the kind of place where if you're not curious you probably won't stay for very long right um so then I don't have to do a lot of inward coaching I do a lot of outward coaching um in my previous role the one I was just mentioning right it was very different so uh there the point was not to we didn't need to invent new algorithm or make new uh foundation models right the idea was how can How can we infuse some of the AI techniques that are well known? How can we gather the data was a total mess, right? How can we clean and integrate the data um and how do we like inject these things into the processes that they have. In some case there was more invention for the gene discovery part but in other places it was just about basic operations. So for example uh the scientists had a hypothesis that if you
对。然后我让工程师把时间花在给基因发现做新算法上，还有，嗯，从 Box 迁到 Microsoft，你知道，诸如此类的事，也就是 IT 那一摊——挺有意思的，但不是我的乐趣和热情所在。所以那是一段很不一样的经历，在那儿该做什么，需要的也是一套很不一样的技能。不过那里的 AI 同样在从根本上改变公司的运作方式，以及它取得巨大成功的能力，对吧？那如果回到那几个滑块上来。我会说，在 WSO2，也就是我现在这份工作，我大概是 20% 科学家，我想说 60% 架构师，20% 教练，对吧。下面这些滑块显示的也是我在每个维度上所处的位置，对吧？我们在那儿有个小型研究团队，做了一些很有意思的工作。我们着实下了功夫去改变公司的战略，现在非常聚焦在 agentic enterprise fabric 上；我还花很多时间做教练，主要是向外的。我们的员工群体非常拥抱技术，整个公司大约 75% 是技术人员，而且大家好奇心特别特别强，对吧？这是那种你要是没有好奇心、多半待不了多久的地方，对吧？所以我不用做太多向内的教练，向外的教练做得很多。而我上一份工作，就是我刚才讲的那个，对吧，情况就很不一样。在那儿，重点不是——我们不需要发明新算法，或者做新的基础模型，对吧？思路是：我们怎么把一些已经广为人知的 AI 技术融进去？我们怎么把数据收拢起来——当时数据完全是一团糟，对吧？我们怎么清洗、整合数据，又怎么把这些东西注入到他们已有的流程里。有些地方发明的成分多一些，比如基因发现那部分，但在别的地方，就只是些很基础的日常运作。举个例子，科学家们有个假设，说如果你
[11:45] Rania
measure the size of the embryo of the corn embryo that is an indicator of how likely the size was an indicator of how likely you are for the next step to be successful in your gene editing but it was too costly for humans to do it right so they oh please can we use AI and um I did my masters in computer vision so I looked at that I was like well yeah sure we can use AI but you just need like blob detection you don't need any machine learning that you just need basic computer vision and we use a very simple algorithm and it worked really well right and that helped. Um and then in the other case for gene discovery we did some very interesting with birds and and things like this. Uh so this is a bit like I was trying to put it a bit on a grid of how do we think about it and really in my view with my experience having been like at IBM and at the biotech startup and now at WSO2 those are the dimensions I think about right like does the company sell software is the is the population mainly technical um and where are you in your AI journey? So uh I get asked a lot what I measure. Um, someone asked me to measure tokens because to see how much AI we do and I said no. Um, because right it's so easily hackable like I'm not like okay it's easy to measure it but I don't want to. Um so they asked me what do you measure instead and I said okay these are the things I look at AI fluency of the workforce like is it only a few people that know AI in a center of excellence and anytime anyone has to do anything you have to get those people um are there builders across the functions right are there people who know how to do enough to get their job done and to really augment it that are you know in marketing in different engineering teams etc or again are you holding all of those so now I have a small central AI team that kind of works with everybody but I have an AI lead in every single product and every product team has folks
测量胚、也就是玉米胚的大小，这个大小就是一个指标，能看出你基因编辑的下一步有多大可能成功，可是让人来量成本太高了，对吧。于是他们说：「拜托，能不能用 AI 啊？」嗯，我硕士读的是计算机视觉，所以我看了一下，心想：行啊，当然能用 AI，但你其实只需要 blob detection（斑点检测，一种很基础的图像算法）就够了，根本用不着什么机器学习，基础的计算机视觉就行。我们用了一个非常简单的算法，效果特别好，对吧，这就帮上忙了。嗯，另一头，基因发现那边，我们用 birds 之类的东西做了些很有意思的事。呃，所以这张图算是我试着把这件事放到一张网格上，看我们该怎么去想它。说真的，以我自己的经历——在 IBM 待过，在那家生物科技创业公司待过，现在在 WSO2——我看的就是这几个维度，对吧：这家公司卖不卖软件？员工是不是以技术人员为主？嗯，还有，你们在 AI 这条路上走到哪儿了？呃，经常有人问我衡量什么。嗯，有人让我去统计 token，好看看我们用了多少 AI，我说不行。嗯，因为，对吧，这东西太容易被刷了，就是说——行吧，量起来是容易，但我不想量。嗯，于是他们问我：那你衡量什么？我说，好，我看的是这些。员工队伍的 AI 熟练度（AI fluency）：是不是只有卓越中心（center of excellence）里那么几个人懂 AI，谁要做点什么都得去找这几个人？嗯，各个职能里有没有 builder（亲手把东西做出来的人），对吧，有没有那种懂得够多、能把自己的活干完、还能真正用 AI 给工作加码的人，分布在市场部、各个工程团队等等地方？还是说，这些能力全都攥在少数人手里？所以现在我有一个很小的中央 AI 团队，算是跟所有人都配合，但每一个产品我都设了一位 AI 负责人，而且每个产品团队里都有人
[13:47] Rania
working on the AI capabilities in it right um and then we have some products that are AI first right so everybody there is working on AI level of adoption I think matters like are you just doing a simple task cleaning an email code completion or are you changing an entire workflow for a better outcome or like we have a couple of our teams that have put agentic employees right so you have these agents that you can instantiate and they work alongside inside the team, right? So that's a spectrum and different teams are in different parts of this spectrum. Uh tool availability like have you as a company do you have you subscribe to some tools and are you reimbursing or paying the cost with team or enterprise accounts? Um and then geo visibility. So this is generative engine optimization, right? Can LLM find us? Can they know what we do? Someone searches? There's a lot going on there. We're still working uh on that. I think with a lot of other people it's not so straightforward. Um and that's where also someone it was interesting. Someone was telling me that just yesterday, right? They're like the web has become for agents. Like it's unreadable for humans anymore because everything is being written assuming an LLM is reading it and it's so frustrating. He's like so I'm just going to have my LLMs read and summarize for me and I'm only going to talk to humans from now on, right?
在做这个产品里的 AI 能力，对吧。嗯，然后我们还有一些产品是 AI 优先的，对吧，那边所有人都在做 AI。采用的深度我觉得也很重要：你只是拿它做个简单任务——理顺一封邮件、代码补全——还是在为了更好的结果改掉一整条工作流？又或者，像我们有几个团队已经用上了 agent 员工（agentic employees），对吧，就是那种你可以实例化出来的 agent，它们就在团队内部和大家并肩干活，对吧？所以这是一条光谱，不同团队处在这条光谱的不同位置。呃，工具的可得性：作为一家公司，你们有没有订阅一些工具？是给员工报销，还是用团队版、企业版账号由公司出钱？嗯，然后是 GEO 可见度。这指的是 generative engine optimization（生成式引擎优化），对吧？LLM 能不能找到我们？它们知不知道我们是做什么的？有人去搜的时候呢？这里面事情很多。我们还在，呃，做这件事。我想很多人也一样，这事没那么简单直接。嗯，说到这儿还有件挺有意思的事。就在昨天有人跟我说，对吧？他说，现在的网页已经变成给 agent 看的了。人已经读不下去了，因为所有东西都是假定读的是 LLM 来写的，特别让人抓狂。他说：所以我干脆让我的 LLM 去读、再给我做摘要，从今往后我只跟真人说话，对吧？
[15:08] Rania
Um for software products uh in the beginning like when we when I started in this role it was about like what should we build what should we extend and where should we partner um and then I we wanted all our products to be agent and LLM consumable. So the docs need to be LM consumable. Uh last year we added first class support for agents LLMs and tools to all our products and this year we're making sure every product can be used by agents. So everything needs to have an MCP server and skills and CLI etc. Um agent proof pricing. So we've all heard this whole SAS is dead and if you price per se and whatever what happens when the agents come and blow it out of the water the consumption. So we are our pricing is all consumption based and then dog footing our stack. I heard another interesting thing today about that. Somebody told me like, "Oh, I'm at so and so company which I won't name and they make us use our product and it would be great except nobody takes any feedback, right, to make the product better." So hopefully we close the feedback loop and not only make people use our stuff, but they use it if they like it and if it helps them. And then the third dimension is the market and ecosystem. So AI thought leadership is not about like articles you can pay for, but do you have earned media, which is are you invited for talks? Do you uh have publications? Are you showing up in the press because you're doing something interesting for real? Right? Uh analyst recognition, customer adoption and use cases is I think very very important, right? Like I stay up a lot thinking about what hurts for my customers and how we can make it better. Uh we are a fully open source company. So everything we do is 100% open source. It's not open core. And um so for us also that's a really important signal right community participation and adoption and stars and forks and then finally AR from AI products right some of our AI products
嗯，软件产品这块，呃，一开始，就是我刚接这个角色的时候，要想的是：我们该自己造什么、该在哪些地方做扩展、又该在哪些地方找合作伙伴。嗯，接着，我们希望所有产品都能被 agent 和 LLM 直接消费（读得懂、调得动）。所以文档得是 LLM 能直接消费的。呃，去年我们给所有产品都加上了对 agent、LLM 和工具的一等支持，今年我们要确保每个产品都能被 agent 用起来。所以样样都得有 MCP server、有 skill、有 CLI 等等。嗯，还有抗 agent 冲击的定价（agent-proof pricing）。「SaaS 已死」那一整套说法大家都听过了：你要是按席位定价之类的，等 agent 一来、把用量冲到爆表，会怎么样？所以我们的定价全部按用量计费。然后是 dogfooding，自己用自己的技术栈。关于这个，我今天又听到一件有意思的事。有人跟我说：「哦，我在某某公司——名字我就不说了——他们要求我们用自家产品，这本来挺好的，问题是没有人收任何反馈，对吧，去把产品做得更好。」所以但愿我们能把反馈闭环合上，不只是让大家用我们的东西，而是他们因为喜欢、因为真有帮助才用。然后第三个维度是市场和生态。AI 思想领导力看的不是那种花钱就能买到的文章，而是你有没有 earned media（靠实力挣来的曝光）：有没有人请你去演讲？呃，有没有发表成果？你上媒体是不是因为你真的在做有意思的事？对吧？呃，分析师的认可；客户采用和用例，我觉得这一条非常非常重要，对吧？我经常熬夜琢磨客户到底痛在哪儿、我们怎么能让它好一点。呃，我们是一家完全开源的公司。我们做的所有东西都是 100% 开源，不是 open core（开放核心）那种。嗯，所以对我们来说这也是一个特别重要的信号，对吧：社区的参与度和采用度，star 数和 fork 数。最后是来自 AI 产品的 ARR（年度经常性收入），对吧，我们有些 AI 产品
[17:02] Rania
are very new so there isn't a lot of AR but some of these other things can give signal to it as we get off the ground um and also I think um revenue is not the only number that matters like tokens is not the only number that matters so this is and I think what you measure measure should change over time based on what you're doing and what your company is doing, right? So for me, this is what I'm measuring now. Once these things like reach maturity, I will change this list of things, right? Um but think a lot about it because you really get what you measure. You start measuring something, everyone's going to optimize for that. Um so just a few words about WSO2. So um we're about 20 years old. We're about 150 million in ARR. Uh our software powers uh a lot of capability in over 90 countries in six continents. Uh our main products are our history has been in API platforms, integration platforms, uh identity and access management and uh internal developer platform. We have a product for that. And in the last couple of years since I came, we've added agent identity and AI gateway to govern your uh AI interactions. We've added an agent builder to our integration platform. So that's what we do and everything we do is open source and this is our whole portfolio today. So you'll see on the bottom the four platforms this is what existed like uh some years back and the way we started was saying we're going to extend out into what AIs need, right? So LLM AI APIs are APIs at the end of the day, but they need to be managed differently. So we put an AI gateway in the API platform. The identity platform, we already know how to do very robust identity and access managements for humans. So now let's do it for agents too. We added agent identity. This was all last year and this year we've released an agent platform um that lets you use those things but also lets you uh put it all together and manage the whole life cycle of these
还非常新，所以 ARR 不多，不过在我们刚起步的阶段，前面那些指标可以先给出信号。嗯，另外我觉得，嗯，营收不是唯一重要的数字，就像 token 也不是唯一重要的数字。所以这就是——而且我觉得，你衡量的东西应该随时间变化，取决于你在做什么、你的公司在做什么，对吧？所以对我来说，这是我现在在衡量的。等这些东西成熟了，我就会换掉这张清单，对吧？嗯，不过这件事要多想想，因为你量什么就真的会得到什么。你一开始量某个东西，所有人都会冲着它去优化。嗯，简单说几句 WSO2。嗯，我们公司大约 20 岁了，ARR 大约 1.5 亿。呃，我们的软件在六大洲 90 多个国家，呃，支撑着大量的业务能力。呃，我们的主要产品——我们历来做的是 API 平台、集成平台，呃，身份与访问管理，还有，呃，内部开发者平台，这块我们也有一个产品。我来了之后这两年，我们加了 agent identity（agent 身份）和 AI gateway（AI 网关），用来治理你的，呃，AI 交互。我们还给集成平台加了一个 agent builder（agent 构建器）。这就是我们做的事，我们做的一切都是开源的，这是我们今天的完整产品组合。你们会看到底下这四个平台，这是，呃，几年前就已经有的。我们起步的思路是：要往 AI 需要的方向延伸出去，对吧？LLM、AI 的 API 说到底也是 API，但管理方式得不一样。所以我们在 API 平台里放了一个 AI gateway。身份平台这边，我们本来就知道怎么给人做非常扎实的身份与访问管理，那现在也给 agent 做吧。于是我们加了 agent identity。这些都是去年的事，今年我们发布了一个 agent 平台，嗯，既能让你用上这些东西，也能让你，呃，把它们整合到一起，全生命周期地管理这些
[19:11] Rania
agents. So I'll give a couple examples on the scientist side too. We have a small research group like I mentioned I'm very proud we have these uh three publications this year. Two of them are with undergrads in Sri Lanka. Uh that is their first publication ever and one of them won a best paper award. Um on the architect side, right, the idea was AI is like great shiny object but we want to infuse it in the whole digital fabric. So how can we extend that with these capabilities like I mentioned before? So those are the things that we've added and agent manager is the the newest we've done. We've just released it. Um uh in the interest of time I won't step but please feel free to explore the GitHub repo and the website. Um so just to close in a few more seconds the idea is okay there's no silver bullet depends on the company you have to work very closely with others I suggest if you try something like this you have very strong backing and relationship with the CEO because you are going to have to work very closely with a lot of other parts of the company and I've seen also some places where if it's a very AI forward company the chief officer like the chief product officer officer and the chief AI officer are one person, right? Or um in other places that was surprising in companies that are not software. I've also seen cases where the head of HR became the head of AI.
agent。科学家这一面我也举几个例子。就像我前面提到的，我们有一个很小的研究组，我特别自豪，今年我们有这，呃，三篇论文发表。其中两篇是和斯里兰卡的本科生一起做的。呃，那是他们人生中发表的第一篇论文，其中一篇还拿了最佳论文奖。嗯，架构师这一面，对吧，思路是：AI 是个很亮眼的新玩意儿，但我们想把它融进整张数字化底座（digital fabric）里。那我们怎么用我前面提到的这些能力去扩展它？所以这些就是我们加进来的东西，Agent Manager 是我们最新做的，刚刚发布。嗯，呃，时间关系我就不一一过了，欢迎大家去看看 GitHub repo 和官网。嗯，最后再用几秒钟收个尾：核心意思是，没有银弹，要看公司的情况，你得和其他人配合得非常紧密。我的建议是，如果你要尝试这样的角色，一定要有 CEO 强有力的支持、和 CEO 的关系要够牢，因为你势必要和公司里很多其他部门紧密合作。我也见过一些地方，如果是一家在 AI 上走得很靠前的公司，首席产品官和首席 AI 官就是同一个人，对吧？或者，嗯，还有些地方让我挺意外的——在不是做软件的公司里，我还见过 HR 负责人成了 AI 负责人的。
[20:45] Rania
I don't know how I feel about that. But yeah, I know. I'm like I Okay, so it's just interesting things are happening. Everyone's trying to figure out what to do, right? because they said oh agents are work workforce our workforce so we manage them HR will manage them I appreciate that I like people feel like me in this room because that rumor I heard it it [laughter] was not the reaction um but uh so I'll just leave you with this to close um when I think about even for me like to take this role I was trying to think okay um you know what I'm somebody had given me this advice like think about what are you good at on what you love, what the world needs and what you can be paid for and try to find something at the intersection. So since this role is very fluid still try to find if you're interested in it and you're looking you have a match somewhere try to shape it in a way that it you are at the center and it makes you happy and the things that you don't like see if you can like have someone work on them differently or maybe a different company is a better fit that has a better alignment with your interests with your love and your needs and what you're good at. All right. Thank you very much. If you have any questions, I'm around [applause]
这事我也说不清自己是什么感受。不过，对，我懂。我就——好吧，总之有意思的事情正在发生。大家都在摸索该怎么办，对吧？因为他们说：哦，agent 就是劳动力，是我们的员工队伍，所以归我们管，归 HR 管。我很欣慰，这间屋子里大家的感受跟我一样，因为我当初听到这个说法的时候，（笑）现场可不是这种反应。嗯，不过，呃，最后我就留这一点给大家收尾。嗯，我在想，哪怕是我自己，当初要不要接这个角色的时候，我也在琢磨，嗯，你知道，有人给过我这样一条建议：想想你擅长什么、你热爱什么、这个世界需要什么、什么事能让你拿到报酬，然后试着在这几样的交集里找点事做。既然这个角色现在还很没定型，那如果你对它感兴趣、正在找机会、在某个地方又刚好对得上，就试着把它塑造成你处在正中心、又能让你开心的样子；至于你不喜欢的那些部分，看看能不能让别人换种方式来做，或者也许换一家公司更合适——它和你的兴趣、你的热爱、你的需要、你的长处更对得上。好了，非常感谢大家。有问题的话，我就在附近。（掌声）