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The Agile Brand with Greg Kihlström®: Expert Mode Marketing Technology, AI, & CX
#605: AI agents working across the marketing workflow with Kevin Li, Optimizely
00:26:18
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To realize the future America needs. We understand what's needed from us to face each threat head on. We've earned our place in the fight for our nation's future. We are Marines. We were made for this.
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The Agile Brand.
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Welcome to season six of the Agile Brand where we discuss marketing, technology and customer experience, trends, insights and ideas with enterprise and technology platform leaders. We focus on the people, processes, data and platforms that make brands successful, scalable, customer focused and sustainable. This is what makes an agile brand. I'm your host Greg Kilstrom, advising Fortune 1000 brands on martech, marketing operations and CX, best selling author and speaker. The Agile Brand podcast is brought to you by Tech Systems, an industry leader in full stack technology services, talent services and real world application. For more information go to teksystems.com now let's get on to the show.
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What if your AI assistant could do more than just generate content? Imagine an AI agent that works across your entire marketing workflow, enhancing both creativity and productivity. Are you ready to transform your marketing team? I'm here at Opticon24 in San Antonio, Texas and getting the opportunity to see a lot of inspiring ideas from some of the world's leading brands and hearing all about optimizely's platform and how it enables one to one personalization, streamlined content operations and incorporates the latest generative AI features. Today we're discussing how AI agents are revolutionizing the marketing workflow with Kevin Lee, SVP of Product Strategy and Operations at Optimizely. We're going to talk about the concept of AI agents, their role in enhancing creativity and productivity, and why marketers should be excited about them. Kevin, welcome to the show.
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D
Thanks Greg for having me. Good to see you here again at Opticon.
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I know returning champion here.
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D
Absolutely. We'll get you a plaque or something if you come back next year.
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B
Nice, nice. So before we dive in here and certainly lots to talk about, why don't you tell us a little bit about your role, which I think is a little bit of a new role for you and a little bit about your background.
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D
Yeah, so I lead up product strategy and operations here at optimizely. A little bit of background. I joined a company five years ago now. Can't believe it's been fast with all that. Through an acquisition and it took over product strategy, which covers M and A, corporate development, acquisitions, but also our portfolio strategy. So we obviously grew as a company from 100 million to 400 million. That was a public press release. And so looking after the sort of portfolio strategy and on the operations side, I also have product analytics documentation, competitive intelligence as well. And yes, I appreciate that. I recently got promoted roughly the same role, just a slightly bigger title, I suppose.
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Congrats.
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D
Thank you. Thank you.
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Well, yeah, let's dive in here again, lots to see and hear in San Antonio here. I wanted to focus this conversation on AI agents and I've talked about them a little bit on the show, had somebody from Microsoft talk about some of it, talked with a few others. But I wanted to focus on really specifically the role of AI agents in marketing. Optimizely recently announced the launch of AI agents embedded within the OPAL platform. First of all, maybe for those a little less familiar, what exactly is opal? And then can you talk about, you know, what's an AI agent in this context of marketing?
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D
Yeah, so Opal was essentially our AI co pilot that we launched last year at Opticon. I think you were here covering that piece of it. So Opal is meant to be the interface that marketers and users essentially access a lot of the generative AI features across the board. So last year we had half a dozen, ten plus features or so. This year we've doubled, tripled that, et cetera. And then the AI agents importantly will be accessed through opal. So think about OPAL as the interface, if you will. That's why we sort of gave it a little bit of a personified identity. And then behind that is where the capabilities such as agents retrieval, augmented generation, some of the more sophisticated capabilities will actually sort of come in through Opal itself for users and then on the agents piece of it. I think this is a super exciting moment for sort of technology. It's changing super quickly. Last year we were talking about like, oh, let's embed generative AI into the features itself. And this year we're now talking about agents. So the biggest difference is really the level of autonomous execution that can actually happen. So before, if you think about without AI agents, what happens is software vendors like us, we sort of purpose built use cases. So if a user is doing something like I'm sure you've tried to hold, like, oh, generate a content, write a headline, generate an image, et cetera, the task is very specific and there's not much sort of autonomous execution because it's sort of input output, right? You tell it to do something, you get something in return. Agents is a little bit different in that you typically are describing things in terms of the outcomes that you actually want to accomplish and the agents are executing something sort of on your behalf. So There's a workflow, automation piece of it. There's a little bit of more intelligence and borrowing sort of a metaphor from autonomous driving, there's sort of the six levels of autonomous driving. So what we're also seeing in sort of marketing and Martech in general is the degree of autonomous execution kind of going up as well. So agents is basically a big step forward to say, can I have one agent do something or maybe a cluster or group of agents do something or an army of agents go do something. So that's the path that we're basically on.
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Nice, nice. Yeah, because that's kind of the limitation when you're just like prompt to prompt is things get very. You can move forward. But that automation is key. Since its introduction, the OPAL platform has seen a 500% increase in adoption. So we've all seen rapid adoption numbers out there or whatever. But even with the rapid adoption of AI in general, that's pretty impressive. What do you think has fueled that growth and are there areas within that growth that are particularly notable?
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D
Yeah, I think for us, number one, it's great to see. As part of my job, I run product analytics. So the adoption numbers we see on a day to day basis, it's interesting. So you know, a year or two ago when we sort of first launched some of this generative AI stuff, it's very much a. I don't know if you remember that time, it was a wild west. So people are just sort of like throwing things out there and they're like, does anyone want to use this is even a thing they want to do now when we see this adoption, I think an important point is also we're seeing sort of sustained adoption. So people are trying it, but they're also sort of sticking with it. So we're sort of seeing behavior change that actually sort of tends to stick rather than like, you know, you try something and like I'm not really going to use it the next time. That's an important sort of part about the growth that's pretty notable. I think. The other part of it is for us as optimizely, we sort of have three different parts of the pillars sort of powering this stuff. So the first part is we actually have our customers data. Well, they put their data in our system, so that's their. We obviously we have to get their permission to use it. So they have to explicitly say, hey, this is okay. The second part is we have the workflow itself. They already do work in our platform. Right. So there's no SWIVEL chair of like copy and paste something or going somewhere else, et cetera. It's already sort of part and parcel for their day to day tasks. And then the third thing is as a sort of digital optimization, digital experience provider, we also have the experience, we know what the sort of eventual outcome sort of looks like. So those three things, the data, the workflow and then the end resulting experience, piece of it all in one place is kind of how we think about why marketers are like, well, honestly, it's just easy. We've lowered the barrier to adoption. You don't have to sign up for another account and then get legal approvals on whether or not you can use it or not and move your data to a bunch of different places, et cetera. Right. So if you think about your, you are starting your AI stack from scratch, that's a lot of pain and effort. Whereas if you're already on optimizely, you go, oh, actually I can get all of it already where I work today.
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Yeah, Because I mean otherwise you're like copying and pasting out of one thing into another. And as you would imagine, it's very disconnected. Right.
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Yeah. I think as someone you write a lot. And so if you were to sort of take a very simple metaphor as well. Spell checking is a form of AI, but how painful would it be for you if you don't get the red squiggly lines to tell you that you misspelled something and you literally have to copy and paste a paragraph to some other browser to spell check for you correct it and then you have to paste it back in. You just wouldn't use it.
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Yeah.
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Right. So it's the embedded in the workflow. It's right there as you type, you're like, oh, I typo, let me fix something that's AI sort of right there and there and has access to your data because it's looking at what you're typing. So that's a good metaphor to kind of really think about adoption in general. So it has to be where the marketer is already working today.
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Yeah, and rightfully so. There's been a lot of focus on content creation and generating text images, all of those kinds of things. I think the workflow stuff, some of that comes from. I do a lot of work in process and marketing ops myself, but I think the workflow and productivity angle is really powerful when combined with those as well. So why is introducing automation into the workflow and delivery components just as important as some of those creative aspects?
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Yeah, because I think at the end of the day, if you think about a marketer's workflow, they don't just want to create. So when we kind of think about the outcomes that they want to do, creation is sort of one aspect of it. It's sort of like one pillar. Right. But they also want the AI to not just say, hey, help me write something or create an image. They also say, say, help me do something. So help me remind me if a task is due. Create for me an entire campaign out of something. Go execute, go do something for me. They also want AI to think about analyzing some data, tell me, surface some anomaly for me, summarize a bunch of data and extract the key points so I don't have to go write a bunch of SQL queries to figure out what's actually going on. And then the last one being like, you know, help me make a decision for execution, you know, for personalization, like, decide for me what's going on on the fly. So historically, we've always thought about, and rightfully so, sort of the first use case was always content generation. So to create. But if you think about the, well, help me create, help me do, help me analyze and help me execute. It's multifaceted in terms of what it is. So we're definitely going way beyond just creation into the sort of four aspects. And I'm sure there's even more aspects of that, of the overall workflow.
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Yeah. So within that as well, there's a lot of opportunities. There's also some concerns that brand managers, marketers, creatives, all of the above, want to make sure that this stuff that's automated and created is also compliant. Brand compliance and, and consistency. I think that's another argument of why you don't copy and paste stuff out of a million different apps into each other and stuff. I think that can hurt that as well. But how do you look at brand compliance? And when we're talking about these AI agents, there is a little bit of, there's some autonomy there. So how do they stay brand compliant?
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So that's actually really interesting because AI agents can actually help brand compliance. So when, when people kind of think about autonomous, it doesn't mean, it doesn't mean complete freedom. So I want to really clarify. Right, so autonomous doesn't mean complete freedom. Autonomous means they can do something without supervision. That's actually a very big delineation.
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That's huge.
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It's like if they repeatedly do something to your expectation without supervision, that's still autonomous. Whereas I think what people are fearful of is going rogue.
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Yeah, yeah, right. So the Terminator scenario.
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Exactly. So. But I think that delineation is really interesting because AI agents, I think to the contrary, can actually help with brand compliance. Because what is brand compliance? It's someone looking at something to decide whether or not it passes the test.
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Right.
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And so if you're able to train an AI agent to do that, that's actually going to help brand compliance. It may actually reduce sort of human based errors to do some of these things. And obviously brand compliance is legal compliance, compliance in general. Right. What must you say? Do you have to write disclaimers, do you have to write accessibility things, et cetera. But each of those things over time are going to be sort of perfected by agents. And so that delineation between autonomous and going rogue are two separate concepts. Autonomous doesn't mean necessarily bad.
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B
You touched a little bit on, you know, helping marketers make better decisions, getting better results. But I'd like to go back to that a little bit as well. You know, how do you look at AI agents supporting both, you know, strategic support as well as practical supports, especially in, you know, we're not just talking about a single channel, we're talking about omnichannel, we're talking about personalization, all of those kinds of things. So like, how do they, you know, how do they support in the complex marketing world?
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Yeah, I think that's then one of the advantages of sort of us providing AI agents as part of Optimizely1. So the core principles of OPAL is it works across Optimizely1. So it doesn't matter where you are. I'm sure you've seen our sort of architecture of plan, create, globalized store, et cetera, personalized experiment, analyze. So our intent is it doesn't matter where you are within that overall marketing lifecycle, marketing workflow, you can use opal. And the key thing is it's not sort of just a use case driven within the specific task. So if you're in planning, like, well, what if you wanted to generate some variations for experimentation? You can do that if you're in publishing. But what if you want to actually sort of look at analytics from previous results to see, hey, is this even a good idea to publish or should we make some tweaks to it, et cetera. So the ability to actually traverse across the activities, that's sort of a core strategic pillar. The second thing that's really strategic for us is the ability, like I mentioned earlier, for customers to choose to use their specific data. That's already in our system to help them go do something. So a good example being if you're in our content marketing platform and we see that you've started a campaign every single year around a certain time, maybe it's Black Friday, maybe it's Christmas, maybe it's back to school, et cetera, the AI should be smart enough to say, hey, I've seen you do something. Let me proactively suggest something. So making use of your data sort of in a right way, and then the last one is actually sort of traversing. It shouldn't just be creating. Can you help me decide? Can you help me analyze? Can you help me go do something and execute? So those are the three core things and that's really kind of our guiding principles, if you will, for somebody, obviously on safety, governance, compliance, et cetera.
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Yeah, yeah. Well, one quick follow up to that. I mean, I think one of the bigger gaps that I see out there with marketers is the time to do the analysis and get the insights. So what you're touching on here of it being easy to switch back and forth to get the insights so that you can make better decisions instead of, I think when it's a lot of manual work, you just get stuck in this like, okay, we got to launch the next campaign. We don't have time to look at what happened last year, last month, whatever. So that kind of speed to insights and creation seems like, you know, the all in one aspect of this really is really powerful.
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Yeah. And I think that's the practical side, which is, you know, why we're also seeing the 500% increase in adoption. It's the how do we bring AI and the benefits of agents and all of that to the fingertips of the marketer as they're doing their job. Not make marketers go learn something else completely separate and then figure out how it all ties together and then worry about, hey, someone from legal not going to be not okay with this, et cetera. So that's really the thing. It's like bring the tools to the humans so they can get more work done.
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Yeah, yeah. So before we wrap up here, I wanted to touch on just two other things quickly. You know, lots of, lots of big announcements this week, but Optimizely recently announced the acquisition of netspring. I'm going to have Vijay, the founder and CEO, on the show as well, but wanted to get your take in your purview as product strategy and all. How does this fit into the Optimizely strategy and how do you think it's going to be able to benefit what brands are able to achieve.
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Yeah, for us. And this is a super exciting announcement for us. I think it'll pay off huge strategic dividends for us going into future. So there were two things that sort of we looked at when we were thinking about this space. So the first one is the exponential growth of data warehouses in general. So we've been talking about the single source of truth for data for a decade. Yeah, it's been a while.
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It's been a while. Right.
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But I think now enterprises sort of have finally gotten their act together. They have enterprise data architecture, they have sort of unified data dictionaries. Like the maturity of data management and governance is night and day versus five years ago, 10 years ago. And you also see sort of the growth of data warehouses in general. Snowflake, Bigquery, Amazon, Microsoft, et cetera. Like everybody who somebody in tech has a warehouse offering and enterprise is starting to put sort of data there. The second thing is what data they're actually putting in there. It's actually the most sort of consequential data that a company actually has. It's revenue data. If you're E Commerce, it's where you track your return rate. If you're a financial services firm, it's where you actually how many people started a credit card application? How many people got rejected? How many people got approved? How much credit utilization are they using? All of that data sits there. And guess what? They don't ever want that data to ever leave their own data warehouse. And so actually very similar to kind of what I just talked about with AI agents, which is let's bring AI agents to the marketer. Netspring really means give us the means to connect what we're doing on the optimization side. So take experimentation for instance, and connect it to those specific business metrics that exist inside of a Cosmos data warehouse. So how do we tie it into that? And so when we kind of think about that historically, when you do optimization, maybe it's a simple a B test. Just as an example, you try A, you try B. The metric you're tying to is oftentimes the observable metric. So for E commerce, a very simple example being did you actually add to cart? Because that's what the experiment can see. But does the CFO care about add to cart? Yes. I'm not going to be like, no, they don't. But what do they actually really care about is return adjusted revenue. How many people bought something and kept it and didn't actually return. So if you sort of follow E Commerce returns is super expensive. Tons of ways, tons of fraud, like lots of issues. Right. So if you're able to run an experiment and say, oh, we actually drove up return adjusted revenue, that metric is inside of the warehouse. So how do we connect it? So that's essentially our sort of first horizon on this. And then longer term we see sort of this architectural shift. You know, last year we sort of announced the launch of SaaS CMS. We said, hey, people want to go CMS from platform as service to software service. On the analytics side of it, we're also seeing big trend of saying people want to start thinking about moving from hosted analytics on the vendor side to putting in an analytics and visualization layer on the warehouse directly because all that data is already there. Why make two to three copies of the same data? Why have data move back and forth? Why take the risk of sensitive data leaving your warehouse? So that's essentially what we're thinking about sort of for the longer term on that second horizon.
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Yeah, yeah, love that. Well, yeah, looking forward to seeing the trajectory there. And then last thing, the Google announcement. Google Gemini announcement. Do you mind talking a little bit about that?
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D
Yeah, for sure. So we've been a customer of Google for the longest time. So our experimentation platform sits on BigQuery. Specifically. We had a PR announcement about that. We've also been working closely with Google across the board. So when they announced the sunset of Google Optimize, which is a AB testing solution, we were one of three partner selecteds where they migrated a bunch of customers over. We have sort of good integrations there as well. And then obviously then when we looked at, you know, we're not in the business of building our own LLMs. Right. So when we go look at the marketplace, it's going to be leveraging sort of someone else with that itself. So when we looked at sort of our overall tech, our overall architecture and also the existing relation with Google, it's kind of a no brainer to sort of double down on specifically that relationship. So they've always done a lot with, you know, Vertex Gemini launching sort of new and new versions there. So that's essentially sort of what's also powering our AI agents underneath the hood.
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Great, great. So one last, well, two last questions for you here real quick. So just following back on AI agents, certainly lots of exciting things today and announcements this week and things like that, but where do you see this going? What should marketers keep an eye out maybe in the months to come?
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Yeah, I think the Key thing is we're still in the accelerating phase of innovation, so things are still speeding up and not slowing down. We're sort of nowhere near sort of this comfortable thing where people are like, oh yeah, we're not in the CRM space, for example. And it's like, yeah, we might want to track sales pipeline. This is still like we're in the early days. And so I think the important thing is really for marketing leaders to give it a try. That's how you'll learn about this. That's how you start to see and reap the benefits. Going back to that 500% adoption stat. That's what we're seeing too. It's those customers are trying and then they're starting to see the subtle sort of bottoms up behavior change to say, oh, I can actually get something, be more productive, do more with less, switch to less tabs, just very concretely do less swivel chair type of things. Things. I think that that's sort of the key thing because that's the only way. I mean, large organizations, a lot of times it's, it's change management is hard, right? There's countless books on change management. So it is about really getting into this sort of AI mindset. And, and that's in part why, you know, here as well we have the marketer's playbook for AI to try to get them to kind of think about it. And at various Opticons we have a masterclass on AI. It's like, hey, here's how you got to think about prompt engineering. So really being more educated on this space, I think that's the key thing.
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Yeah, great. Well, Kevin, it's always great to talk with you. Last question here. I like to ask all my guests, what do you do to stay agile in your role and how do you find a way to do it consistently?
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I think for me it's really about staying curious. If you think about the. The netspring acquisition is sort of a good example. If you sort of look at that space, there's really only four or five very early stage vendors who even do warehouse native analytics in particular. And so that's not a widely covered sort of category itself, but staying curious, thinking about, you know, connecting the dots together, et cetera. I also have a decently large network of people who are like, hey, what do you think about this? What do you think about that? Probably spend too much time on LinkedIn.
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Same here.
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People posting things and being like, oh, that's actually super interesting. Keeping on the startup community et cetera I think that's great. And then I think in my role, so we just get so many almost like inbound M and A queries. It also actually really helps because a lot of times it's like, oh, that's interesting. Let's at least take initial call and we get to ask questions and how do they see their existing category, are there any systemic changes, et cetera? It's just about keeping the pace and the frequency of information flow, really.
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Yeah, yeah, I love it. Well, again, I'd like to thank Kevin Lee, SVP of Product Strategy and Operations at Optimizely, for joining us today at Opticon24 in San Antonio. To learn more about Kevin Optimizely, please
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follow the links in the show Notes thanks again for listening to the Agile Brand brought to you by Tech Systems. If you enjoyed the show, please take a minute to subscribe and leave us a rating so that others can find the show more easily. You can access more episodes of the show at www.greggkilstrom.com. that's G-R E G K-I H L S T R O M.com While you're there, check out my series of best selling Agile brand guides covering a wide variety of marketing technology topics. Or you can search for Greg Kilstrom on Amazon. The Agile Brand is produced by Missing Link, a Latina owned, strategy driven, creatively fueled production co op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. Until next time, stay Agile.
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