WEBVTT

NOTE Sentence-level transcript of https://www.youtube.com/watch?v=9cJrbj23fOA

NOTE One cue per sentence. Cue ids are the line anchors on /transcripts/9cJrbj23fOA.html. A cue ends where the next begins, or 2 s after its last word.

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[music]

s2
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Hi everyone.

s3
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How are you?

s4
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All right.

s5
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I'm so excited to see all of you here still awake and ready.

s6
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Um, so today I wanted to uh share my experiences on the chief AI officer role.

s7
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It's very new.

s8
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I get a lot of questions about it.

s9
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I've been in this role for nearly two years and some variant of it for about the last five.

s10
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And um I wanted to so to get started I just would like to get an idea about the folks around here.

s11
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Um how many are in companies that build and sell software products?

s12
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All right, great.

s13
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Um, and um, how many folks are uh, have someone leading AI that is not the CEO or the CTO?

s14
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All right, very cool.

s15
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So, we'll we'll jump right in.

s16
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This helps.

s17
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Um, so like I was mentioning, the stroll is new, but it's becoming more and more popular.

s18
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And what I found is that it means drastically different things based on two criteria.

s19
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One is the company, the type of company you're working for and two is the level of AI maturity of that company.

s20
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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

s21
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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

s22
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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.

s23
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So I've also seen it sometimes breaking into two.

s24
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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%.

s25
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Um, okay.

s26
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So, the tricky thing about this role is like it's so great.

s27
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Okay, I have an AI officer, but there's AI in everything, right?

s28
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It's kind of like saying digital or I don't know, electricity.

s29
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It it's in everything and it can be so many things and it can be really overwhelming, right?

s30
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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.

s31
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Um, so how can we do that?

s32
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So in my experience, it's been about breaking it into three focus areas.

s33
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Um I use the word scientist, architect, and coach.

s34
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I'll explain a little more.

s35
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So scientist is more um you're exploring, you're experimenting, you're building.

s36
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Architect, you're also building, but you're also doing a lot more strategy.

s37
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If the company is selling software product, you're really thinking about creating new software, you're impacting the product strategy.

s38
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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

s39
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um new revenue streams or getting to new markets and so on.

s40
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Um I'm putting also there is one piece that is uh quite important here.

s41
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So like I'm in a board-facing role, right?

s42
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So I have to report out to the to our board in both this company and the previous one I was in.

s43
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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.

s44
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And finally for coach the coach one of surprised me.

s45
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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

s46
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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

s47
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and now in this role I also spend a lot of time as like trusted advisor to many of our customers.

s48
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So people in my role at other customers or CTOs that are starting to adopt AI, right?

s49
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They they want to learn about okay what's happening in other places.

s50
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So I do a lot of calls with customers and I'm never going to be like here buy my thing, right?

s51
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Um that happens later downstream if they're interested.

s52
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My discussions are always what problems are you having?

s53
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This is how we think about the problem.

s54
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And I have no slides, but that's okay.

s55
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Does it come back?

s56
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Well, while you fix it, I'll say a few more words.

s57
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Right.

s58
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So, the conversation I have with them is more of a a trusted advisor.

s59
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Like, I understand what problems they're having.

s60
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I see if our technology fits.

s61
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If our stuff doesn't fit, I might direct them to another thing that works much better for them and where they are.

s62
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And sometimes they'll come back or they'll just want to have a discussion again.

s63
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Um, we have many products uh that are also not AI first.

s64
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So, you know, we I might talk to some customers that don't use any of our AI capability, right?

s65
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But they want to think about what they could do in the future.

s66
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Thank you.

s67
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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.

s68
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So for the scientists like even at one set you don't have to have a PhD you're just exploring heavily.

s69
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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?

s70
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I don't think there's a way around that because the tech is moving so fast.

s71
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Um, but on the other end is like the creator inventor side of the house for the architects.

s72
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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

s73
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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

s74
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will bring enough value and they prioritize and they allocate budget and they project manage them and they report out right so for that you don't need deep AI knowledge but you need to understand the business really well and be a very strong operator

s75
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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

s76
00:07:11.599 --> 00:07:23.759
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.

s77
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You're coaching your uh the different teams, the the employees about how to use things.

s78
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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.

s79
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Um and then, uh as the company has more maturity, you're doing more coaching outward, right?

s80
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So, working with customers, working with the community and and things like that.

s81
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So uh I also uh worked with Claude and Gemini to help me like analyze resumes of people in the role, right?

s82
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And also job postings of people in the role and match them to these slider directions and see like what it thinks.

s83
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And these are some of the areas it found.

s84
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So this validates that like really it's the person.

s85
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So um the row is the type of persona and their skill set and then the columns are the sliders, right?

s86
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Um so I'm going to give some examples from my own journey because this is area is so fluid and new.

s87
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So I think that's the the most useful I hope thing to do.

s88
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Uh so after IBM research I joined a biotech uh unicorn in the Cambridge area that was working in agriculture.

s89
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So they were using crisper for corn, soy and wheat to make plants that make more food and use less water.

s90
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But to do those experiments took 10 years and a ton of land to validate like if your gene edit would work.

s91
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So you needed methods to help the scientists make better hypothesis of what edits to make.

s92
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Right?

s93
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So that was the way in the door for the AI and data part.

s94
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Um and then when I got there they were like oh you are like a computer person.

s95
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They are more like they had amazing geneticists and amazing act people but not a lot of tech people from like engineering.

s96
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So they're like, "We're also going to give you it and please hire a CISO."

s97
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And we had no data engineers.

s98
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So like that mandate grew very quickly, very far beyond the let's build AI together.

s99
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Um but that company sold bags of seeds, right?

s100
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So we're in IBM, I was building platform here.

s101
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I was like we we end up uh to be a fully AWS shop.

s102
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I bought data bricks, right?

s103
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Right.

s104
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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

s105
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uh was interesting uh but uh not my joy and passion.

s106
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So that was a very different experience and a very different skill set of what to do there.

s107
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But also the AI there was also fundamentally uh changing what the how the company operates and its ability to have great success.

s108
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Right?

s109
00:10:14.079 --> 00:10:16.480
So if you go back to the sliders and so on.

s110
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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.

s111
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Um and these uh sliders below are also showing like where I'm at in each of the dimensions.

s112
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Right?

s113
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So we have a small research team there that uh we've done some very interesting work.

s114
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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.

s115
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We have a very techforward employee base.

s116
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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

s117
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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

s118
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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?

s119
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How can we gather the data was a total mess, right?

s120
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How can we clean and integrate the data um and how do we like inject these things into the processes that they have.

s121
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In some case there was more invention for the gene discovery part but in other places it was just about basic operations.

s122
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So for example uh the scientists had a hypothesis that if you 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

s123
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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

s124
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that you just need basic computer vision and we use a very simple algorithm and it worked really well right and that helped.

s125
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Um and then in the other case for gene discovery we did some very interesting with birds and and things like this.

s126
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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

s127
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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?

s128
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So uh I get asked a lot what I measure.

s129
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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.

s130
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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

s131
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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

s132
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everybody but I have an AI lead in every single product and every product team has folks 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

s133
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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

s134
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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?

s135
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So that's a spectrum and different teams are in different parts of this spectrum.

s136
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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?

s137
00:14:29.199 --> 00:14:31.600
Um and then geo visibility.

s138
00:14:31.600 --> 00:14:34.959
So this is generative engine optimization, right?

s139
00:14:34.959 --> 00:14:36.800
Can LLM find us?

s140
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Can they know what we do?

s141
00:14:38.480 --> 00:14:39.760
Someone searches?

s142
00:14:39.760 --> 00:14:41.360
There's a lot going on there.

s143
00:14:41.360 --> 00:14:43.120
We're still working uh on that.

s144
00:14:43.120 --> 00:14:47.199
I think with a lot of other people it's not so straightforward.

s145
00:14:48.320 --> 00:14:51.040
Um and that's where also someone it was interesting.

s146
00:14:51.040 --> 00:14:52.959
Someone was telling me that just yesterday, right?

s147
00:14:52.959 --> 00:14:55.040
They're like the web has become for agents.

s148
00:14:55.040 --> 00:15:01.279
Like it's unreadable for humans anymore because everything is being written assuming an LLM is reading it and it's so frustrating.

s149
00:15:01.279 --> 00:15:08.480
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?

s150
00:15:08.480 --> 00:15:18.720
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

s151
00:15:18.720 --> 00:15:23.440
um and then I we wanted all our products to be agent and LLM consumable.

s152
00:15:23.440 --> 00:15:25.680
So the docs need to be LM consumable.

s153
00:15:25.680 --> 00:15:34.399
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.

s154
00:15:34.399 --> 00:15:40.880
So everything needs to have an MCP server and skills and CLI etc. Um agent proof pricing.

s155
00:15:40.880 --> 00:15:48.320
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.

s156
00:15:48.320 --> 00:15:53.120
So we are our pricing is all consumption based and then dog footing our stack.

s157
00:15:53.120 --> 00:15:55.440
I heard another interesting thing today about that.

s158
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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."

s159
00:16:05.440 --> 00:16:13.600
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.

s160
00:16:13.600 --> 00:16:16.639
And then the third dimension is the market and ecosystem.

s161
00:16:16.639 --> 00:16:23.600
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?

s162
00:16:23.600 --> 00:16:26.399
Do you uh have publications?

s163
00:16:26.399 --> 00:16:30.399
Are you showing up in the press because you're doing something interesting for real?

s164
00:16:30.399 --> 00:16:31.040
Right?

s165
00:16:31.040 --> 00:16:36.880
Uh analyst recognition, customer adoption and use cases is I think very very important, right?

s166
00:16:36.880 --> 00:16:42.240
Like I stay up a lot thinking about what hurts for my customers and how we can make it better.

s167
00:16:42.240 --> 00:16:45.120
Uh we are a fully open source company.

s168
00:16:45.120 --> 00:16:47.920
So everything we do is 100% open source.

s169
00:16:47.920 --> 00:16:49.519
It's not open core.

s170
00:16:49.519 --> 00:17:09.439
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 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

s171
00:17:09.439 --> 00:17:24.000
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?

s172
00:17:24.000 --> 00:17:25.679
So for me, this is what I'm measuring now.

s173
00:17:25.679 --> 00:17:30.640
Once these things like reach maturity, I will change this list of things, right?

s174
00:17:30.640 --> 00:17:34.960
Um but think a lot about it because you really get what you measure.

s175
00:17:34.960 --> 00:17:39.280
You start measuring something, everyone's going to optimize for that.

s176
00:17:39.280 --> 00:17:42.400
Um so just a few words about WSO2.

s177
00:17:42.400 --> 00:17:45.280
So um we're about 20 years old.

s178
00:17:45.280 --> 00:17:48.160
We're about 150 million in ARR.

s179
00:17:48.160 --> 00:17:57.039
Uh our software powers uh a lot of capability in over 90 countries in six continents.

s180
00:17:57.039 --> 00:18:08.320
Uh our main products are our history has been in API platforms, integration platforms, uh identity and access management and uh internal developer platform.

s181
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We have a product for that.

s182
00:18:10.240 --> 00:18:18.160
And in the last couple of years since I came, we've added agent identity and AI gateway to govern your uh AI interactions.

s183
00:18:18.160 --> 00:18:22.320
We've added an agent builder to our integration platform.

s184
00:18:22.480 --> 00:18:28.480
So that's what we do and everything we do is open source and this is our whole portfolio today.

s185
00:18:28.480 --> 00:18:38.480
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

s186
00:18:38.480 --> 00:18:39.840
what AIs need, right?

s187
00:18:39.840 --> 00:18:45.039
So LLM AI APIs are APIs at the end of the day, but they need to be managed differently.

s188
00:18:45.039 --> 00:18:47.600
So we put an AI gateway in the API platform.

s189
00:18:47.600 --> 00:18:53.120
The identity platform, we already know how to do very robust identity and access managements for humans.

s190
00:18:53.120 --> 00:18:54.880
So now let's do it for agents too.

s191
00:18:54.880 --> 00:18:56.320
We added agent identity.

s192
00:18:56.320 --> 00:19:10.559
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

s193
00:19:10.559 --> 00:19:13.919
the whole life cycle of these agents.

s194
00:19:14.160 --> 00:19:16.720
So I'll give a couple examples on the scientist side too.

s195
00:19:16.720 --> 00:19:22.320
We have a small research group like I mentioned I'm very proud we have these uh three publications this year.

s196
00:19:22.320 --> 00:19:25.360
Two of them are with undergrads in Sri Lanka.

s197
00:19:25.360 --> 00:19:30.960
Uh that is their first publication ever and one of them won a best paper award.

s198
00:19:30.960 --> 00:19:40.559
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.

s199
00:19:40.559 --> 00:19:44.480
So how can we extend that with these capabilities like I mentioned before?

s200
00:19:44.480 --> 00:19:49.360
So those are the things that we've added and agent manager is the the newest we've done.

s201
00:19:49.360 --> 00:19:51.200
We've just released it.

s202
00:19:51.200 --> 00:19:59.679
Um uh in the interest of time I won't step but please feel free to explore the GitHub repo and the website.

s203
00:20:00.080 --> 00:20:11.039
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

s204
00:20:11.039 --> 00:20:20.880
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

s205
00:20:20.880 --> 00:20:32.240
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,

s206
00:20:32.240 --> 00:20:32.960
right?

s207
00:20:32.960 --> 00:20:37.280
Or um in other places that was surprising in companies that are not software.

s208
00:20:37.280 --> 00:20:44.240
I've also seen cases where the head of HR became the head of AI.

s209
00:20:45.360 --> 00:20:47.120
I don't know how I feel about that.

s210
00:20:47.120 --> 00:20:48.159
But yeah, I know.

s211
00:20:48.159 --> 00:20:52.400
I'm like I Okay, so it's just interesting things are happening.

s212
00:20:52.400 --> 00:20:54.240
Everyone's trying to figure out what to do, right?

s213
00:20:54.240 --> 00:21:07.439
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

s214
00:21:07.439 --> 00:21:07.520
[laughter]

s215
00:21:07.520 --> 00:21:19.840
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

s216
00:21:19.840 --> 00:21:30.080
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.

s217
00:21:30.080 --> 00:21:45.600
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

s218
00:21:45.600 --> 00:22:00.720
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.

s219
00:22:00.720 --> 00:22:01.120
All right.

s220
00:22:01.120 --> 00:22:01.840
Thank you very much.

s221
00:22:01.840 --> 00:22:03.770
If you have any questions, I'm around

s222
00:22:03.770 --> 00:22:05.770
[applause]