From Sales to AI Agents: The GTM Shift

Speaker A: Hello, world.

Speaker B: This is Better Tech, a podcast where we chat with some of the most successful leaders about the latest industry developments. So join us as we explore the world reliance on tech.

Speaker C: Hello and welcome to Better Tech. Uh, we're really excited Today to welcome A.J. cook from Mercato. Welcome, A.J.

Speaker A: hi there. How you doing?

Speaker C: Great, great. It's great to have you on the show. Um, what we're talking about today is, uh, incredible impact of AI in the sal and go to market space. And so, um, you've been an expert on this for quite some time at a company that's, uh, shown its expertise. Um, tell us a little bit about what you're doing today and a little bit about Workado.

Speaker A: I mean, it feels like the big bang has only really happened in the last couple of years. So it's debatable whether there are any true, longtime veteran experts in, uh, using AI in this way. But, yeah, I've, I've been, been really excited to be part of Workato's team here for about four and a half years. And we have very much been on the forefront of, let's call it, the convergence of integration and automation, really becoming one capability in enterprises. And a natural extension of that is incorporating machine learning, libraries, AI that's available through APIs and now Genai and large language models in a number of ways.

Speaker C: Interesting. Um, just for people who don't maybe know Workato, can you talk a little bit about where, uh, that company came from and the types of customers you work with today?

Speaker A: Yeah, of course. I mean, as long as there's been, uh, IT companies have been trying to figure out how to stitch together all of their large data sources and their big enterprise systems of record. And one of the first companies to do that all the way back in the 1980s was called Tibco. Actually, it started out being called Technotron and then became something called Tibco later. And one of the technical founders of that business was Vijay Tella, who founded Workado. So we've really been steeped in, in whatever the front of this wave is. You know, how are Companies through the 80s, the 90s, the thousands, the 2000s, attempting to continue stitching together all of their systems, data and processes. How can we make that easier and how can we make it more relevant to what the current generation of technology is? So Tibco was doing this with, you know, metal boxes that had to be wired together like on the New York Stock Exchange. Right. And, uh, as we got into the 2000s and the advent of cloud and SaaS two things that were really important happened. Uh, number one, the number of apps that any given company had skyrocketed because we went into the era of, you know, SaaS and every company, uh, every line of business in the company could buy the best point solution for one specific thing, even an app for just one use case that worked really well. So there was a ton of kind of, um, you know, fracturing and heterogeneity across a company's processes. One process like you and I getting hired as new employees all of a sudden could include a dozen different SaaS applications. So that happened. The other thing that happened as a result of this kind of Cambrian SaaS explosion was that there was a ton of demand for, uh, applications and software that were easier to use, right? More user friendly, better design, more click and drag. And so Workato is founded to address both of those trends, right, that we had the integration and the automation expertise already that was in our DNA. But we saw that there were so many more cloud based applications that had to be stitched together and we had this demand from the business because of, you know, apps like Slack that, you know, taught everybody that enterprise software didn't have to be hideous. And there was a demand for a less technical builder to be able to start coming in and doing the stitching together of all of these systems and data. So that's really what Workato was founded to address. And we're the only truly cloud native platform that can do this at an enterprise scale.

Speaker C: Um, and so this is really interesting, right, because I just want to vibe with what you're talking about as a customer, on the customer side, working with like very large financials. Uh, yeah, there was a huge explosion of very customized, highly customized SaaS applications and really nobody knows how they work under the hood or work together. There's a lot of undocumented process in the seams. Right. Um, and so I think people are really hungry to get the tools into the hands of business users who really know the process, uh, rather than trying to kind of pretzel yourself around to document it. It's incorrectly documented, incorrectly coded up. Um, and I'm assuming. But I'd love to hear more. You're in a kind special role.

Speaker A: That's right. Yeah. I started out at Workado in our OEM and embedded business, which is on its own sort of. It started out as a skunk works because we started deploying our technology in a different way for customers. And now my role this year is to build the global agentic and AI organization at Workato. So the capability to take what we do really well here, what's in Workato's DNA and now extend that to helping our customers incorporate large language models and gen into their business. Helping them to start building custom um, AI agents and agentic automation in their business. It just requires, you know, a completely different set, uh, at least at first, of product capabilities of understanding a different market, a different magic quadrant, so to speak. Um, the competitors are very different. So you know, our job is to act as a kind of a global speedboat team to help our entire business get this messaging and this positioning into the market more efficiently this year.

Speaker C: Okay, that's interesting. So let's talk a little bit about just to give people, um, kind of what Workato generally does and then what this new thing is that you're doing. So generally speaking it's an integration platform that's automating workflows for big complicated companies, right?

Speaker A: That's right. You'd find us in those Gartner magic quadrants that are titled ipaas Integration platform as a service or boat Business Orchestration and automation technology. That's exactly right.

Speaker B: Mhm.

Speaker C: And let's just briefly talk about some of the non functional requirements there because I think that is so critical to an agentic build out. Uh, so some of the things that Workado is able to do, I sort of vaguely remember and you have to, you're the expert, but so you've got like pretty broad coverage of integration. You're experts of like different data sources. Is that. And like, tell me a little bit about the data sourcing, the security, some of the things that you don't see and touch every day but are sort of, that are really essential to, to a huge platform like this.

Speaker A: Yeah, I think the industry term for this is all of the unsexy parts of making stuff work.

Speaker C: That's the technical term. Yes.

Speaker A: Right. I am, I am technologist if nothing else. Yeah. Um, also fluent in French from a, from a business perspective. So um, what we're talking about is this prerequisite, this foundational layer that as you say largely is. It's invisible. You know, if everything's working, you should never think about how everything is connected. And that is where Workato started. That's our, that's our sort of DNA as a business. So you know, we think about data as a monolith. It's just this thing, this unit of information in our business. But the reality is that data might only be accessible in some places through an API, in some places through um, you know JDBC drivers or through SQL queries. Um, some of it might be on prem, some of it might be in the cloud. The, even the APIs, you know, there's. Not all APIs are created equal. Some are very accessible and some are very. And they're, they're complex. So you know, what Workado's fundamental job is, is to make a lot of that complexity and that heterogeneity go away. So we can give you a place where you have the same drag and drop experience. All you have to do is log into any given database or app or, or user account and once you've established those connections which Workato manages for you now, it is much faster to go in a secure way and stitch those systems and those data sources together. So you can then understand an end to end process and automate it.

Speaker C: Yeah, so let me give you, uh, or let's work together on a quick example. Right. So let's say I'm running a call center. I want to include data that's like, from my old transcripts, unstructured data source somewhere. And I want some new fresh data that's coming in, maybe some sentiment analysis that's being measured during the call. And I want both those things to come into some kind of dashboard for my customer, um, service agent. Right. Uh, so with Workato, I can just kind of drag and drop those components that have been preset for me. Um, and so it just works. That's the baseline functionality. Is that right? Yeah.

Speaker A: The call center example is really apt, Jocelyn, because if we think about the stuff that I need as a human call center agent to be able to have that real time enriched interaction with the other human that might be chatting with me or talking to me on the phone. Um, some of that data probably lives in customer360. Right. Everything that I know about that customer's location, our shipping and return policies in their state or their country, their order history. Right. Some of it might live in a CRM, some of it might live in the customer support portal where they filed a ticket, and some of it might be unstructured documents, um, like the picture that the UPS driver snapped of the delivery on their porch. And so, you know, yeah, the first task is how can I get this different, you know, sourcing and formatting of data from different places and get all of that in real time. That's part of Workato's responsibility. The other responsibility is that sort of orchestration that you spoke to. So I'm implying that I need to see all of that information in a Specific place, whether that's a dashboard or it's triggered by certain events taking place. Um, and so Workado handles the connectivity and also the moving around of the data according to certain workflows or certain processes. And that has to happen very fast. Uh, and you know, the other thing that goes along with that back to the unsexy theme is that it has to work. Right.

Speaker C: I'm so glad you said that. I was just thinking in the back of my mind, like, there's one, you

Speaker A: have one job, there's like, there's infrastructure, scalability, uptime, like how reliable is this stuff that I'm doing to stitch everything together? And the most interesting last piece is that you mentioned the idea of like sentiment analysis, right. Which implies that we're going to take some amount of data at some specific step in the product, uh, process and we're going to ask some machine learning model, does this sound good or bad? Does this person sound happy or angry? Right. And, uh, Workato also gives you the connectivity to either go to a model that might be sitting on prem that you've built custom, or it could be something like the, you know, the many different sentiment analysis APIs that are out there. Right. We've made that easier. Like even now when we work with Gen AI and LLMs, we're working with the vast majority of those models through an API, so we make it really easy to kind of log in, put them at a certain step in a process, say exactly when we want to ask them a certain question, and then we keep going through the rest of that workflow.

Speaker C: Great. You correctly jumped to the turning, uh, point of this conversation because I think once you do get into incorporating the outcome of an AI, uh, an LLM, or the outcome of a traditional machine learning model, um, you're getting into new territory that looks a little bit more agentic. So now that we've kind of established the baseline, this is like the bread and butter, which is actually very difficult to accomplish. Congratulations. Workado does a good job of getting all that, uh, into one place, that low code environment. It all works. But now, like, you don't really have me, the person setting up this workflow. Maybe we want an agent to do it automatically, um, or something like that. Is that kind of where you're going and what your team's focused on?

Speaker A: I mean, you're touching on a couple of interesting things here. First of all, no one on the planet agrees about what an agent is or what is it so true that

Speaker C: is such a hot ticket right now in the AI world. I don't know why everyone's all jammed up on that. Um, what do you think it is?

Speaker A: I mean like everyone wants to be agentic because it sounds cooler. That's one part of it. Um, and also people tend to want to define really popular terms in their own favor. Right? Like I could say the definition of somebody that's handsome is a 5 foot 7 bald guy because obviously that's, that plays to my strengths. Right. So I think that we're seeing much the same right now. Like if somebody doesn't really fulfill the criteria of what would be considered agentic, uh, or an agent, then it's in their best interest to try and influence the criteria. Just like I love that every day.

Speaker C: You know, I'm just a, uh, I've been in this business a long time and I'm, I do find myself asking the question sometimes like uh, isn't that just code? Or like isn't that, isn't that just instructions really? Or isn't that really, you know. So I, I do feel like you're right. Uh, this is maybe a non issue, but there is a bit of a debate on agents there.

Speaker A: It's a non issue if you don't have a stake in which direction that user decides to go to solve their problem. So that's a luxury that Work Auto has that the majority of platforms don't. If I am a uh, startup that just got out of YC three months ago and all I do is, you know, build AI agents, then I'm terrified that you might decide that the use case we're talking about is really just workflow automation. But at Work Auto in our world those are just two different buttons that I'm going to click in the exact same UI in the same platform. And so we talk to our customers every day about the importance of having the freedom to choose the right tool for the job. And so you know there's this, it's kind of an eye chart that we've created but we help people to understand wherever you are in the business, it, sales, finance, customer support, um, you know, there are some use cases within it that are just, you know, black and white workflow automation, what we call our SAP. And there are some use cases that are a great candidate for still being a uh, black and white pre programmed recipe. But we can incorporate an LLM into a step in that process because LLMs are really good at helping us process data in some new ways. And then there's what we would consider to be more of a true agentic uh, workflow. Right. Which is basically us giving a lot more of the logic itself, how we're going to go through the process, how we're going to make decisions, when to do what. We're giving more of that to the AI. Right. We're delegating more of the process design, the decision making, the reasoning to the large language model. And so there's a spectrum. And again the luxury we have at workato is that, you know, we can help you to use automation across the whole spectrum and then use LLMs when they're, when they're a good fit at the right level. And you know, if you don't have that flexibility, if you're not agnostic in that way, you're going to end up um, you know, trying to make a lot more things nails because you're a hammer. That's the reality.

Speaker C: Yeah, I like that. You know, I'm dating myself a little bit but you know, after like the Java Linux Oracle revolution came into enterprise, everyone was super excited to adopt a new technology. And the next question was like, well, what are we building? What are these applications going to be? What is the workflow? And I feel like we're uh, at the doorstep of that same consideration with uh, AI. Right. People are excited about this technology. Every CEO has funded projects to do AI. And then the next question is, well, what actions, what activities are we AI ing? And what you're saying is workato is already there with the recipe today.

Speaker A: Yeah, and I think that the, maybe the controversial thing to say is that, you know, you should probably choose the most deterministic tool that you can to solve a problem. Which means oftentimes gen AI is not the answer, an agent is not the answer. We're so far away, uh, in industry from saying that we should fully delegate decision making, interpretation, reasoning to a large language model when it's, you know, deciding what your prison sentence is going to be or whether you should get a home loan approved. Right. Uh, and yet we're talking as if we can completely eliminate entire swaths of the knowledge worker economy in favor of these models. So I think that that is an example of us getting way ahead of ourselves and hyping the utility of this technology. Right. There's, there's a ton of this business value that all of a sudden CIOs and CEOs want to apply. And that's great, but they may still often just be talking about workflow, automation, black and white, pre programmed logic, if this, then that, and when you do that you end up with a Lot less risk in the business.

Speaker C: I like what you're saying about being thoughtful and, um, intentional about where you're using AI and where you're not using it based on some principles, one that you've just touched upon is I think, cost. Right. Um, I've seen that a lot of sort of like that, that consideration coming last when it should maybe come first. Uh, what are some other considerations that you and your customers are, um, pausing to consider when they're thinking, hey, is this workflow? Is it machine learning, Is it AI?

Speaker A: Well, I mean, look, my stake in all of this is that we don't have to tell our customers that they, they have to make a bunch of different investments. I guess one of the other overarching cautions that I would give any CIO or any technologist right now is to be careful about making investments that, you know, Jeff Bezos would call like a trap door instead of a revolving door. You know, if you get completely locked into a specific company's foundation models, right, to build your entire architecture and your tooling and your whole stack on just say OpenAI, what do you do if next month Microsoft, uh, comes out with an industry model that's 10 times more relevant for your business, or if an open source model comes out that's free, you know, and now you've completely adopted a locked in, siloed tech stack for one foundation model. So because the industry is moving so rapidly right now, and, and because we're still learning at such a fast rate about what the best use cases, the best target architectures are, I just think that there's a lot of risk and therefore there should be a lot of caution in making an investment that paints you into a corner.

Speaker C: How are some of your customers thinking about data security, data safety sensitive data as it's flowing into these, uh, new, uh, agentic and AI workflows?

Speaker A: Yeah, that's a great question. I mean, again, this is kind of table stakes for us as an enterprise platform. So, you know, what I'd say is there's an urge in the name of innovation and keeping up to start to maybe brush some fundamental blocking and tackling of enterprise governance and observability and data security under the rug. Uh, and we've seen a couple of examples of not going through the proper deployment process or not thinking about where your privacy, your compliance posture is as a business coming out to buy enterprises like in a really public venue. Right. And so I'm not going to get into the specific news stories, but like we're seeing this on A recurring basis now because people, you know, did the ready shoot AIM, uh, with their AI experiments. And so what I would say is you don't want to forsake the flexibility that we talked about, the optionality of being able to swap components in and out of your architecture. And you don't want to forsake, um, you know, fundamentals like logging and observability, having a fairly fine grained sort of role based access control and entitlement regime. Because you don't, you don't want to allow either humans or AI agents that have some autonomy to have access to the wrong data and take the wrong actions. Like we've already solved alignment and trust for humans for the most part. Like we're really good at not letting human users touch systems and data that they're not supposed to. And it should go without saying that we would put the same controls in place to safeguard, you know, AI and large language models having access to those things.

Speaker C: Well, I would say our intent is certainly correct, uh, but uh, a lot of organizations are really over privileged today and that is one big difference I think between um, deterministic and non deterministic systems. You just don't understand, understand who has access downstream to the output of an LLM in the same way that you might to even the output of a machine learning model that's at least versioned.

Speaker A: Yeah, that's true. And while the machine learning models generally speaking tend to be built and maintained internally, you know, I mean Capital One is one of uh, the foremost organizations on the planet at innovating with like machine learning and risk models. Right, but those aren't really, you know, off the shelf solutions that they went and bought from industry. Right. Their ip. Um, it's not the case yet with gen AI and I think that's a big part of the fear for the average CIO CEO board member today is that they're thinking, look, I'm going to entrust access to my data and my processes to a model that's coming from a vendor and it's in the cloud. Right. And I can completely appreciate why there would be a lot of kind of fear.

Speaker C: Uh, there's a lot of heartburn about that to begin with. Um, well, you know, one of the things you mentioned a couple times is the cio. Um, I just wanted to shift directions a little bit because we do want to talk about go to market and the impact of uh, AI on the sales motion. Um, so who, who are you mostly selling to? Who's your icp?

Speaker A: It's a really interesting question. You know, I mean Workado really thrives at uh, working with senior IT leadership and the cio because decisions about how we want to move the entire business forward with respect to automation or providing more access to tools for building these workflows, those generally happen in a somewhat centralized way. Right. This isn't like um, Trello or Atlassian where I might have just one team start using it and then it bubbles up from the bottoms up in the organization. Now that said, a big part of the value that we provide today at Workato is that we have line of business or um, solution specific agents that are pre built as part of our platform. So over here I can be building a completely bespoke agent using all of the framework and the tools from scratch for something that might be really specific to my company. And then over here, uh, you know, every company has an IT department, a sales department, an HR department, a service, a finance. And so we're already building those pre built sort of line of business agents. That means that now, and this wasn't always true at Workato, my team and I can go directly to a chief revenue officer or chief people officer and we can talk about really acute business and operating pains that those folks have in their part of the sort of back office and solve those specific business problems and those specific use cases with a product off the shelf from Workato. So I guess you would say that that expands our ICP a little bit because now our first conversation could in principle be with those, those line owners or those other members of the elt, not just the cio.

Speaker C: Yeah, that's interesting because uh, it's interesting you mentioned like the HR world because those are the people who are going to have to uh, change resources around. Maybe some people don't have the same job that they had before as agents get adopted. Uh, is that part of the reason you're looking at that group of people with a specific kind of return on investment discussion?

Speaker A: Uh, that's a really interesting question. I wouldn't say that necessarily. Our inspiration for starting to build what we call these Agent X apps at Work Auto was more that we saw a lot of analysis paralysis in the market. So you had these two sort of competing forces. On the one hand you had your kind of CEO and your board breathing down the neck of the CIO and your employees pressuring the cio. Let us use AI. What's our AI strategy? We've got to do something. And then on the other side you had really what I think of as sort of this multi Tiered risk that's associated to making the leap into using gen AI because there's this market risk. What if everything changes next week and I look like an idiot for making an investment? There's model risk. What if genius causes my company to get fined for a billion dollars because we, you know, we, we violate the Equal Housing act or fcra? And then there's um, there's just execution risk, like, hey, where should we start? And so that fear of not understanding what that golden path was, what are the best use cases, what are the places where we're guaranteed to get business value, that was a lot of why we decided to create these pre built Agent Dex apps here.

Speaker C: I like that I wasn't thinking at that level, but I, um, see this all the time, this kind of tension in the executive, uh, suite of who's kind of got the pen, who's got the problems, who gets fired if it doesn't work. And so I do think they need tools to get started and get out of paralysis. I like that. I also was thinking, I thought you were going to answer in a different way, which is these, um, what do you call Agent X template?

Speaker A: Agent X apps.

Speaker C: Apps. These Agent X apps. You know, I'm just picturing in my mind really rolling something like this out. I think this is my spicy take. I think there's a myth that there's subject matter experts who know how all the processes work. Those people aren't really there so much anymore. Right. And so if you were to roll this out, I think now you would need more templated approach or more application, uh, API approach, because not everybody knows how. You know, once we were done with the call center call, where that data goes and who audits it, like there's mysteries, there's mysteries out there. And so I think I see Nvidia doing this as well, creating these packages that uh, can be installed and get you maybe 80% there. Is that part of the intent is to help people implement just on the ground, at the department level?

Speaker A: Yes, absolutely. Um, look, there's an entire cottage industry about this in the enterprise called process mining. Right. Like to your point, right. We have these complex processes and we all wish that there was one person who knew exactly how all those pieces fit together and then we could just keep that person from retiring and we could keep the lights on. But oftentimes, you know, already retired.

Speaker C: They left us already.

Speaker A: Yeah, in many cases that's absolutely right. And so, you know, just illustrating that with something like, um, bpm. Right. May not be enough and One person may not know how the whole thing works, so workado certainly helps there. But it's also that, yeah, to your point, this is about really trying to hotwire time to value because there's a lot of pressure right now, and you touched on this earlier on, those leaders who are starting to do the kind of POCs and the pilots and the experiments of Gen AI, and they're, you know, at one point, 12 to 18 months ago, there was practically unlimited money to go throw at, uh, being able to tell everybody that was a stakeholder, hey, look, we're spending money on it, we're doing something about AI right now.

Speaker C: Mhm.

Speaker A: But we're now getting through that cycle where, you know, we're getting into the next year's budget planning in the next month or two and people are going to be asking, what was the ROI on all these experiments? What actually made it into production, what was real? And that's another big part of the inspiration for these Agent X apps like Agent X Sales, Agent X it, Agent X Support, is that we want to give a much easier path to that ROI to our users so that they can all see business value from what they're doing here, uh, instead of it remaining in the abstract.

Speaker C: Yeah, that's interesting. I think in terms of measuring success, that's something that, um, you know, working at a platform level does give you a better understanding of the roi, the satisfaction of the people using it. Um, so I like that you called that out. Um, I had one other question. You know, I'm just hearing this from so many customers. Right. We're jammed up. We had big ideas, we bought a lot of like, uh, capacity. It's just waiting around for us to figure out what we want to do and operationalize it. I know you can't share customer names always, but do you want to share some stories, uh, about where you may have already unblocked some of your customers?

Speaker A: Yeah. As far as, you know, AI specific use cases and some of those early wins. Absolutely. And we just, we announced the public availability of all of these solutions just last week or two weeks ago. So, you know, we've been working in a lot of kind of stealth mode, closed beta, working with customers for the first half of the year. Um, we do already have several enterprise customers and public companies that are in production with these use cases though. And so a couple of the ones that come to mind for me, uh, one is, let's call them like a, uh, pretty well known publicly traded cybersecurity company and they are out there already. Fully automating their cpq uh configure purchasing quote process using an agent that they built with our platform. So the headline there is that they had have hundreds of sellers in their business across many different markets. Very complex uh, price book because they have so many different SKUs and software products and add ons and widgets they can sell and for them it took 45 minutes to an hour for a seller to be able to go into Salesforce CPQ and build the first draft of that order form that quote. It's very complex and they're able to do that now chatting in natural language with this AI agent that, that we built with them for CPQ and they're able to build the first draft of that quote in three to five minutes. Right. And so you know a lot of that logic and the interpretation is getting handled by the AI, by the large language model and the human user only has to say here's who I'm trying to sell to, here's what I'm trying to sell. And then the agent actually can provide back and forth and coaching to help them get to the best packaging, the best discounting, etc.

Speaker C: I think it's a great example because it's a closed system, it's complicated, it's tedious but there's an internal correctness, there's an answer that can be had. So I love that example, I'm going to steal that. Um, we do want to talk a little bit about how AI because you have had a long. I didn't ask you enough about your career. Tell uh, me a little bit about how you ended up on the go to market side because you mentioned you were had a technical background.

Speaker A: I think I was joking when I said I was a technologist. Yeah, no, my first career was being a home remodeler. So um, you know I came to technology and sales because I loved the fast pace of startup and I loved getting closer to the coal face and talking to customers, you know, every day. That was the part of being a home remodeler I liked the most. So that's how I got into the sales world. And I've just found that I um, really enjoy complex, ambiguous technical sales. Um, so you know, you wouldn't find me necessarily out there selling a market marketing technology platform.

Speaker C: I was going to say doing home remodeling and repairs is like uh, exactly analogous to what workato is orchestrating and automating because there's so many bit pieces that can get you, they all have to work at the End of the day.

Speaker A: Yeah, yeah, absolutely. Well, it's certainly analogous to my experience with earlier stage startups, which is part of how I ended up in the role I'm in now at Workato.

Speaker C: Interesting. How do you think AI is going to change the role? Well, first of all, just for um, listeners, what is go to market? Like what is the bound? What is it? What isn't it?

Speaker A: Well, I, I think that it's a very popular term these days, but I would say that go to market probably encompasses the, let's call it like the first 2/3 of the revenue bow tie. So you know, the life cycle of uh, of a company out there becoming your customer. It starts with marketing and then leads into what we would call demand generation, which is where we try to start capturing someone's interest by giving them information, maybe getting them to take a meeting. Then it goes through the formal sales process, which is where you're now talking to a human that's trying to close the deal with you. And then I would say what we call go to market probably sort of culminates in how we implement and support that customer post sale.

Speaker C: Nice.

Speaker A: Right. Some people are really just salespeople and they say GTM because they want to sound less threatening on LinkedIn. But um, it's a little more all encompassing than just sales, I think.

Speaker C: Yeah, I like that, that's helpful. Um, how do you think AI is going to change that? Uh, all those, you know, you just mentioned some key pillars of activity for big and small companies. Um, what are you seeing? What are you doing, uh, internally to adopt AI and improve your processes? What are you seeing out there that's going to get impacted in that set of activities you just described?

Speaker A: Yeah, I mean, look, this is a trillion dollar question right now for us in industry. Right. Um, I guess and I have these conversations with customers a lot. I would say top line, you know, we should expect that this will be like other general purpose technologies. What I mean is that if you compare this to, you know, the industrial revolution or inventing internal combustion engines or electricity or the Internet, everybody said that the jobs were all going to go away. We haven't seen that happen yet. Right. What we tend to see is that the net number of jobs keeps growing, but people that were doing this end up doing that instead of.

Speaker C: Mhm.

Speaker A: Right. Like we, we kind of got rid of a lot of the CPAs, but all of a sudden we have way more data scientists. You know, we see these transitions happen all the time. So I'm not as worried about mass Unemployment, just to take that off the table in the near term. Um, but what I do think is going to happen is what economists like to call dislocation, which makes it sound very sterile and medical. But that means the people that were used to doing a certain job are going to have a real rug pull and they're going to have to figure out how to go reskill. They're going to have to figure out how to stay relevant. That's not new in our economy and in our society. Um, you know, I just turned 40 and when I talk to people that are AI native and they're a generation younger in the workplace, I already feel some days like an old dog that needs to learn a lot of new tricks to keep up. So I would say that acutely in the go to market world, you know, we should expect that AI isn't coming for your job per se, but it's coming for your tasks. So if a lot of your day is made up of very rote, repetitive admin stuff, you know, that's the part of your job that we should probably expect is going to become a candidate for AI and automation and what we call these agents over time, for most people, that should be good news, right? Unless the majority of your day and the majority of your current business value comes from the repetitive rote admin tasks. So if you're the kind of person that just pushes paper around, you are on the chopping block, right? And so I think that there's a constituency for that in marketing, in sales. You know, what you want to be sure of is that as we go forward, you're really thinking about, um, and again, this is a great Jeff Bezos kind of excerpt. He talks about what we know will be true in 10 years, right? And he built Amazon's strategy, their North Star, on things he knew wouldn't change. Like people would want more selection, people would want lower prices, and in 10 years those things would both be true, um, whether quantum mechanics gets figured out or not. And I think that that's true for Sal and go to Market as well. So my admonition to anybody who's worried about this right now is that, you know, the stuff that AI and agents won't really be able to usurp over the next, say, 10 years are going to be truly critical thinking and being able to create a unique business value for a customer. Um, they might be able to help you prepare for a meeting or create talking points. But I'm very reticent of fully delegating my ability to think from first principles to the AI. And I think that people should be careful about doing that. And the other thing that AI and agents will not be able to usurp or automate away is the creation of a connection with people. So what we call building rapport or building true value and true connection with customers, going in person and being able to go to a whiteboard with a customer, work through their problem together. I just think it's very unlikely that those parts of your job, those parts of your value are a candidate for automating away. So you hear sales leaders talk about revenue generating activities, RGAs, you want to spend as much of your time as possible actually selling something to the customer face to face and not doing all the admin. So that's kind of my um, my long answer about.

Speaker C: I like that. In terms of increasing your RGA time. Um, what. I'm just curious because, uh, for your team, for yourself, what is the most tedious drudgery that you would like to AI IFI right now?

Speaker A: Well, I mean, fortunately I work at Workato, so we're getting a lot of the cool toys and the cool stuff to help us with admin. And since my first day at Workato, I'd say we're a company that's very good at drinking our own champagne. So I already had my laptop in hand, I was already set up with every app I needed at Workato and all of that was done by Workado recipes by the time my first day of employment started. Right. So we've made a lot of that investment already here in removing friction for people and trying to reduce a lot of that, that kind of admin and you know, non.

Speaker C: Interesting. Yeah. I just think that I look at our like, uh, when I look at enterprise sales now and go to market, um, you know, I'm sort of in awe of their capabilities. They have a lot of things going on and it just seems like they have a lot of documentation requirements that I would chafe under if I was in that role. So.

Speaker A: Well, yeah, you're, you're in good company then because you're like every other enterprise AE on the planet. Look, a lot of our Salesforce updates and our CRM updates, that busy work does get done by AI.

Speaker C: I'm glad it's not my role because I uh. They have a lot of persistence. Right. Like you sort of think of salespeople as being performers who are focused on the rga, but they definitely have a lot of homework to do at night that I'm glad I don't have.

Speaker A: Well, and that's, you know, again, it's coming for your tasks, right? So a lot of our CRM updates are made by AI. We don't have to go log into Salesforce and do that field by field anymore because we're recording our meetings. AI can listen to our meetings. It can help us to transcribe those notes. Um, and then another thing that I think is really interesting, especially in the enterprise, is, you know, this idea of having to take a bunch of time to manually pull together, uh, let's call it like a value pyramid, like understanding and developing a unique point of view about incorporation before you meet with them. That's the stuff that should go away, right?

Speaker C: So it should get a lot faster and easier.

Speaker A: That's exactly right. You look at what, you know, the, the public tools are like deep, um, research. Right. And, and you can create very sales specific versions of this which we have here that allow me to prepare for my first meeting with Acme Corporation with, you know, a really strong summary and briefing and curation of all of the relevant executive interviews, the key insights from their most recent earnings reports, relevant news stories. And so the manual effort, the legwork to collate all of that information should be off the table. Uh, that's a perfect example of something that Gen AI can help us to streamline so that I can spend more time digesting the important talking points. The Cliff Notes, developing my point of view.

Speaker C: As an example, we're going to switch gears to our rapid, uh, fire questions. Uh, these are fun for us, but also great, uh, for social media. Um, before I do that though, is there anything in particular you wanted to make sure to talk about that I didn't ask about?

Speaker A: I think we hit on all of the big stuff. Jocelyn. Thank you.

Speaker C: Great. Uh, okay, so what's one quality that you think separates good leaders from great leaders?

Speaker A: Self awareness.

Speaker C: Okay, um, what's one industry that, and we talked about a little bit. What's one industry you think AI will completely transform?

Speaker A: I don't know any industry that's safe. So I think that the mental model has to be anything that is repetitive and doesn't really require creativity or interpretation is a candidate for getting automated. Um, like I said, it's not coming for your job so much as it's coming for your tasks. So I think the m most at risk industries might be things like, um, you know, the. I'd hate to be a paralegal right now. Right. Uh, I would not want to be a CPA in the current industry because when you think about Those types of jobs, they are very, very high on that scale of administrative or like, uh, logical functional work.

Speaker C: Um, what's the best advice you've ever received as a leader?

Speaker A: Uh, I was told this pretty early on actually it was to take the S off my chest. So, you know, there's this urge to be Superman and to try and take too much onto yourself, not to delegate and trust work to people enough. You confuse your own individual ability to execute for your team's ability to execute. And then it causes you to forget what your real job is as a leader, which is to make them better at their job.

Speaker C: Mhm. Is, uh, there a book or movie that you would recommend to our audience right now?

Speaker A: Uh, we would need like a separate podcast for me to go through all of these. Um, I think that it's a great time to read books that call a lot of attention to people and tell them to kind of snap out of it and wake up. So I guess that genre for me has books like, um, Only the Paranoid Survive by Andy Grove. One of my, my favorite books, Amp it up by Frank Slootman. I think it's a great time to have a little bit of that wake up call, but I've got several categories of books I could recommend. Big Reader.

Speaker C: Oh, good, good. Um, is there a tech leader, past or present that you've mentioned? Jeff Bezos a couple times. So that may be the answer there. Tech leaders that you look up to or think about in your daily activities?

Speaker A: Yeah, I mean my, my past, like Mount Rushmore type business leader. I would probably include Andy Grove there. I think John D. Rockefeller is a really, really interesting guy if you have the chance. Ohio and his story. Um, current business leaders. And I do bias towards tech, I would say like Aaron Levy, Jeff Bezos, absolutely. Um, Frank Slootman just as an operator, as somebody that I find really, really interesting.

Speaker C: That's a good call out actually. Um, so if you had to summarize your leadership philosophy into like one motto or catchphrase, what would it be?

Speaker A: Backup is not on the way.

Speaker C: Less novel. You mean there's no one coming to save us? Us?

Speaker A: Yeah, I, I, I really believe personally and I try to impress this upon my team that, you know, you, you can ask for help, you shouldn't be afraid to ask for help, but you should always put the oxygen mask on yourself first. So I think that the first person people look to to solve a problem always should be themselves. Can I figure this out from first principles? Can I go find this answer? Can I get this done without having to take somebody else's time. Right. And I think that sometimes people are guilty of being. I call them baby birds, you know, because baby birds can only be fed by the mom, kind of, uh, like, you know, regurgitating directly into their mouths. Um, you don't want to be that person in a business setting or on a team. You don't want to be that person to your manager. So I think that my overarching kind of leadership philosophy is to. To really preach people to have, um, a bias for action and a really high level of agency and autonomy. If you do that, almost everything else works better.

Speaker C: All right, well, um, aj, thanks for coming and sharing a little bit with us about Workato's new activities and your new role. Um, we really appreciate it.

Speaker A: Thanks, Jocelyn. Great talking to you today.

Speaker B: We look forward to bringing you the latest industry news in our next episode. In the meantime, check out our other episodes@techcell.com podcast and be sure to subscribe to our YouTube channel so that you never miss an episode.