Kimberly Tan’s Post

Transcript

Nobody wants to do data entry like nobody wants to sit in the back and read 100 faxes and try to input that into a system. If you're able to build an intelligent AI agent specifically for that industry that is tailored to exactly how they do their business, it's almost a no brainer to do it. Can really, you wrote an article with a pretty fun title, RITORAO. Let's jump into that. But first, what is RPA? RPA stands for robotic Process Automation, and it's a way of basically automating very manual tasks within an organization. So things like data entry or invoice processing that basically every business has to do, but it's nobody's core competency. It's just one of the like dirty, messy internal things within an organization. Everyone has to do. So historically it's been done very manually, like you would just hire a data analyst or you were hire a back office operations person. And there was this like I would say innovation in the last 20 years where people were like, is it possible to automate these tasks? And so the historical way people have done it is through robotic process automation where you basically build like a little software bot that mimics the actual clicks that somebody would be doing. It's very deterministic, meaning like they're literally clicking the different. Like boxes that I would be clicking as a human. But you know, like organizations are messy and the work we actually have to do is not perfectly delineated by a very specific like process. So oftentimes it's something veers a little bit off course, like maybe someone misspelled a name or maybe a website changed where the sign in box physically is on a page. Then historically that would break the RMA process. And as you can imagine, there's like an infinite number of small little things that could happen like that. So our prayers often very good. We're doing like 80% of the task, but then like 20% of the time that it fails, it's still a manual person who has to come in. So it's just not reliable enough to actually do the full task. And so you're still left with having the back office people that were the first generation of, you know, doing these sorts of tasks there. So I just think like with a ILM's now, because they're able to process such unstructured data and they're able to intelligently collect context and then figure out what the best course of action is the next generation of actually automating these back office tasks should be. Intelligent AI agents instead, what can intelligent automation or what you refer to as these MLM's in action? What can they do that RP couldn't? Let's use the example of a company that we are actually invested in called Tenor. Tenor does referral management for healthcare practices. So if I'm a primary physician and I need to refer a patient to a specialist, historically the way that that would be done is I would literally write something out on a piece of paper. I would fax it to the specialist, the specialist front desk person would take the facts, look at it, look at all the information on it, and then input it into my own database, check, you know, like the insurance policies, check prior history, et cetera, and then decide whether to accept the patient or not. And that was a very manual task that there's just a little bit too much complexity in the way that it's done for RP able to be able to handle. So it would have to be some sort of administrative person like human who was going to do it and with now like intelligent automation. Tenors come up with a very sleek solution that is basically able to automate that whole process, and it's much more self-serve. Yeah, Because the way that RPA would historically work is you would have to hire like an implementation consultant or something, and they would sit next to whoever was doing the task, and they would basically just watch, like, what are the clicks that you are doing, right? And then program those clicks. But someone like a tenor, for example, you're not going to have somebody sitting there watching what the front office admin is doing. Rather, they've created a really sleek UI. Where it looks very much like a drag and drop, different process flows. Yeah. And they're able to create their own automation process, which to them feels very intuitive so they can set it up themselves, but actually has a ton of complexity under the hood that's being handled. I mean, one natural question that comes up, I think for many people, especially as they think about things like hallucinations, is where is the technology in this arc? Are we able to really achieve this idea of intelligent automation today? Are there barriers? Like, where do we sit in that trajectory? The way that we've seen it work best is when there's one. Very specific automation flow, at least to start that a company can just nail, meaning it's often industry specific. So you can integrate into all the core systems there you can understand the context for that industry and it's one very repeated but very manual flow. So for example, like data entry, it's I get on a phone call, I hear the update on where an order is all the information from that order, it can be parsed through that call and input it into my main system. That probably happens like thousands of times a day for the largest organizations, all manually done. And that is one very specific flow. And that's just to start. And then once you get there, you can build deeper into other flows. But I think that is a much more successful path where you can actually understand the constraints and build around them. Make sure that like the agent performs correctly versus tackling, let's say, like everything within healthcare, everything within legal and logistics to start. Normally I ask the question, why now? But I feel like, you know, listeners know that AI. Coming to your MLM's are maybe the term that a lot of people use, but is there a deeper why now or specific technological advances within the sphere of looms that you can point to that actually make this possible? Yeah. I think one thing that we're really excited about is, you know, people use the term AI and they're like, oh, everything's going to change now because of AI, but like, what does that mean? You know, there's a lot of very distinct technological breakthroughs that make different applications possible and specific to intelligent automation. I think one of the things that makes it much more. Possible than before is a lot of the fundamental research coming out of the large labs. So for example, recently Anthropic announced computer use, which is basically a browser agent that is able to intelligently understand what is happening on the browser level of any sort of desktop and be able to take actions accordingly. So you know, we talked about how historically our PA basically understood at a pixel level, hey, I should click this thing and then I should click that, but with something like computer use or I think open AI. Something called operator that they're going to release soon, agents are going to be able to browse the Internet and browse the web in a much more sophisticated way, which is going to open up a lot of possibilities for what intelligent agents can do before. So we think a lot of these intelligent automation startups, they're not going to be doing fundamental research on their own. You know they're still tech that needs to be done to make a browser agent fully work at scale. But what's really exciting for us is that the large labs are clearly working on this and clearly understand the opportunity and so as that tech gets better we think there's going to be a. The whole world of startups who are able to leverage it for all the different industries out there that the large lives themselves are not going to tackle. And as you think about the opportunity, you framed it in your article as kind of two different paths that people might take. So one of them was the horizontal AI enabler and the other was a vertical automation solution. So tell us about that, the two different paths that you see if people want to build in this space. So the first is the horizontal in AI enabler. And that's something that we think any company who's doing any sort of automation, intelligent automation. Is going to have to do like one very common example, which I've touched upon a little bit already is data extraction. Like almost every intelligent automation path starts with some messy unstructured data that you need to pull key outputs from. And today a lot of people are just building that manually, but we've started to see companies emerge that are purely doing the that path, which is taking unstructured data and pulling out the key pieces so turn it into structured data. And we think that could be one really interesting opportunity. So anyone who is. Either building their own automation in house, can you leverage that as a key component or if you're building like a full end to end solution, maybe you input that as one of your components as well. One thing that I'm personally really excited about is the vertical automation path. I think to make an intelligent AI agent very successful, it often is helpful in the beginning to have it be in a very constrained domain, For example, in logistics or in healthcare and legal, like it is a a domain that they can understand all the context for. They have all the necessary inputs. Integrations, et cetera, and they're able to automate one specific flow. So what we're really excited about there is like, let's take an industry that does have a lot of manual work that needs to be done like a very large back office. If you think about like what are the things there that actually have to be automated that maybe RP A wasn't able to tackle before because it would just wasn't like a large enough individual customer, like it wasn't one of the Fortune 500 customers. So that's one thing. It's like what sort of industries fit that criteria and then thinking about like. What is an actual automatable flow to start with? And ones that get us really excited are flows that are actually revenue generating where the customer that you would sell to was previously constrained on the amount of business that they could handle because of this flow. So that could be taking customer orders by voice. That was maybe not possible before that. Now you could do or it could be like a referral management like I said before, where you just couldn't process that amount of data quickly enough. But now you can, I mean, when you think about the market. As well, you're just talking about how effectively you're targeting what was previously done by labor. What does that say about the opportunity and the scale of it? It's just so much larger. Like there's so many markets where, you know, you look at the market from just like Bureau of Labor Statistics data and you're like, this is an enormous market. And then you look at who the software incumbents are and you're like, they just don't match up to the size of the opportunity. And that was historically because like, as I said before, software could not handle it like the long tail of edge cases of what these companies were actually doing. Or they just didn't have large software budgets, but all these companies have large labor budgets and they do have a lot of opportunity that obviously they do want to wrangle on technology can empower. And we think with intelligent automation, this is one of the most exciting times to actually be going after some of these legacy markets, seeing whether or not you can actually serve them through AI agents in a way that maybe traditional workflow software couldn't. So I think it's actually like a false comparison to look at the historical software and companies and say. Oh, this is the cap on what a company could become. I think there's just so much untapped opportunity that technology just wasn't able to penetrate before then. Now, you know, with intelligent AI agents, with voice agents, et cetera, you can now tackle. Yeah, I think you're absolutely right that we were. There was all this untapped potential because the technology only went so far. But now that we're here, how do you see the next 5-10 years evolving? Because there is kind of like a a shift that people have to do intellectually as well as they're thinking about their software budget to labor budget and as they almost have to. Regear their brain to say, oh, we actually can do this automation, which we previously couldn't. So yeah, how do you see that trajectory? I definitely think is going to be an evolution. And I think it'll depend on the technology spectrum, like how technology savvy or out at the forefront that industry is. But for a lot of these older industries that we're talking about, like the larger ones that are a little bit more on Prem, a little bit more based like in the physical world, I think it will take take time, which is why I think doing the vertical. End to end automation solution is so exciting because you can actually build something that is very tailored for their specific workflow where it's almost a number brainer to use it like everybody. Nobody wants to do data entry, like nobody wants to sit in the back and read 100 faxes and try to input that into a system. And that's no no companies core competency either. So if you're able to build an intelligent AI agent specifically for that industry that is tailored to exactly how they do their business, it's almost a no brainer. Do it. And then the folks who are doing that before can now focus on much higher value, either customer facing tasks or much more complex tasks. And then overtime, let's say in the next 5 to 10 years, you know, the technology wave will continue to get adopted by more and more companies, people, but will become more knowledgeable about what these agents can and cannot do, more comfortable with the technology. And then because you've integrated yourself with that customer base, with their core systems, you'll have the opportunity to take on more and more like human labor. Or core tasks that they're traditional systems record could do. So it's a really exciting time, I think to wedge in now because there's a clear opportunity to build something that is ROI generating and just an obvious boost to the company's top line. But you'll still get an early enough that you will have the right to win in the future as these companies get more and more mature on the adoption curve. Totally. And so obviously, we're kind of early in this arc as you mentioned, but there's a lot of interesting, exciting things to come. What would you like to see builders focus on? What kind of builders would you like to hear from as well? I would be really excited about people who are thinking about what was not possible before. You know, we've talked a lot about like what RPA does today and the types of customers that's able to target today. But when you think about like the world of work that could be intelligently automated away and the amount of time and savings both employees and companies can get, it's just like an order of magnitude larger than what what is currently possible. And so I'd be really excited about people who are thinking about the bucket of. Types of tasks that were automatable that RPA historically could not handle and types of industries that it currently was not able to tackle and really thinking about like what are those? First flows or first automations within those industries that are possible and really thinking about what are the clean UI or UX paradigms that you could bring to bear for their solutions. I love that. I love hearing that, you know, you're not just interested in hearing from builders in finance or healthcare, but some of these really niche markets. I think that's a, a paradigm shift. Yeah. And if let's say, 10 years from now, no one has to do manual data entry again or no one has to, you know, get yelled at on the other side of the line for an angry like person. And customer service, I think that'll be a win for everybody. Yeah. And then all these folks can then focus on like much more creative, productive tasks that probably make them happier to finally sends out the fax machine. Yeah.