RIP to RPA: How AI Makes Operations Work

0:00 nobody wants to do data entry like nobody wants to sit in the back and read a 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 rip to RPA so let's jump into that but first what is RPA RPA

0:29 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 that everyone has to do so historically it's been done very manually like you would just hire a data analy or you hire a back office operations person and there was this

0:58 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

1:24 to do is not perfectly delineated by a very specific like process so often times if something veers a little bit off course like maybe someone misspelled a name or maybe a website changed where the signin box physically is on a page then historically that would break the RPA process and as you can imagine there's like an infinite number of small little things that could happen like that so RPA is often very good for doing like 80% of the task but then like 20%

1:52 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 AI and llms now because they're able to process such unstructured data and they're able to intelligently collect context and then

2:14 figure out what the best course of action is the next generation of actually automating these back office tasks should be like intelligent AI agents instead what can intelligent automation or what you refer to as these llms in action what can they do that RPA couldn't let's use the example of a company that we are actually invested in called tenor tener does referral management for healthcare practices so if I am a primary physician and I need to refer a patient to a

2:43 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 Specialists 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 data datase check you know like the insurance policies check prior history Etc and then decide whether to accept the patient or not and that was a

3:06 very manual task that there's just a little bit too much complexity in the way that it's done for RPA to be able to handle so it would had to be some sort of administrative person like human who was going to do it and with now like intelligent automation tenor has come up with a very Sleek solution that is basically able to automate that whole process and it's much more self served yeah because the way that RPA would

3:28 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

3:52 drag and drop different process flows and they're able to create their own automation process which to them feels very inuitive 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

4:15 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 okay 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

4:43 call I hear the update on where in order is all the information from that order it can be parsed through that call and inputed into my main system that probably happens like thousands of times a day for the large 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

5:08 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 is coming it's here llms 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 llms that

5:33 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

5:55 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 RPA basically understood at a pixel level hey I should click this thing then I should click that but with something like computer use or I think open AI has

6:23 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 sophis phisticated 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 there's still Tech that needs to be done to make a browser agent

6:43 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 whole world of startups who are able to leverage it for all the different Industries out there that the large la VES are not going to tackle and as you think about the opportunity you framed it in your article as kind of two

7:05 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 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

7:27 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 to turn it into structured data and we think that could be one really interesting opportunity so anyone who is either building their own

7:57 automation inhouse can leverage that as a key component or if you're building like a full endtoend 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 like in logistics or in healthcare or in legal like it is a a

8:21 domain that they can understand all the context for they have all the necessary inputs Integrations Etc 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 RPA wasn't able to tackle

8:45 before because it it was 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

9:10 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 size as well you're just talking about how effectively your targeting what was previously done by labor what does that say about the opportunity and the scale

9:33 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 longtail of edge cases of what these companies

9:56 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 wringle and 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

10:20 workfl software couldn't so I think it's actually like a false comparison to look at the Historical software Inc compant 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 Etc you can now tackle yeah I think you're absolutely right that we were there was all this untapped potential

10:43 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 reggear 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

11:04 definitely think it's going to be an evolution and I think it'll depend you know on the technology Spectrum like how technology Savvy or how 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 it will take take time which is why I think doing the vertical

11:28 end and automation solution is so exciting because you can actually build something that is very tailored for their specific workflow where it's almost a no-brainer to use it like everybody nobody wants to do data entry like nobody wants to sit in the back and read aund faxes and try to input that into a system and that's no no company's core competency either so if you're able to build an intelligent AI agent specifically for that industry that is

11:55 tailored to exactly how they do their business it's almost a no brainer to do it and then the folks who were doing that before can now focus on much higher value either customer facing tasks or much more complex tasks and then over time 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 will become more knowledgeable about what these agents can and cannot do more comfortable with

12:19 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 labor or core tasks that their 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

12:46 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 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 I

13:08 mean like 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 were thinking about the bucket of

13:31 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 those Solutions I love that I love hearing that you know you're not just interested in hearing from builders in finance or

13:58 Healthcare but 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 in customer service I think that'll be a win for everybody and then all these folks can then focus on like much more creative productive tasks that probably

14:19 make them happier too finally Sunset the fax machine yeah