Why AI Agents Will Do for the Enterprise What RPA Never Could
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Today, on the A I daily brief, i'll look at how agents might fill the potential once seen in rpa. The AI daily brief is a daily podcast in video about the most important news of discussions in A I to join the conversation. Follow the discard link in our show notes.

Hello, friends, happy weekend. We have a long reads episode for you here today. In my conversations with enterprises, one of the things that they are watching most closely is where automation solutions in the form of agents are really going to actually be performance in a way that allows them to replace entire categories of tasks.

This is the road that many organizations and enterprises is have been down before in the form of something called robotic process automation, or R P A. R P A, in practice, has had a very spot record of success in the piece that we are reading today. Or rather that I am turning over to A, I to read cheerly ten of injuries and horrow explores how agents are replacing R, P, A and living up to the original promise.

So let's read the S. A. And then i'll come back and do a little bit of discussion .

as A I turns labor in the software. The opportunity to product tize external professional services, for example, in legal or accounting, become a hot topic. However, we believe there is also substantial opportunity and productive zing internal work within organizations.

These respons ibi E S often fall under the umbrella m of Operations and can range from full time data try in front test grows to routine Operational tasks embedded in every other role. This work generates fewer media headlines, but IT is the internal stitching that holds companies together. These obstacles involve critical, but often repetitive in monday in tasks.

Companies have historically attempted to automate these tasks by using robotic process automation R, P, A. But with generated A I, we believe true automation through agents is now possible. We've already seen early examples of agents working in production, such as decades as automated customer support.

And with companies like anthropic launching capabilities like computer used to enable models to meaningfully interact existing software, there is a clear emerging infrastructure stack for founders to build vertical zed intelligent automation applications. These examples preview a world in which A I agents are able to fulfill the original promise of R, P. A, turning what used to be Operations had count into intelligent automation, and freeing workers to focus on more strategic work.

The original promise of R, P, A and the impact of ai Operations work is sprawling and diverse, including tasks like data entry, document extraction, information transfer, system migrations and web scraping. These tasks essential, but they often lacked the A, P, S or direct integrations required for traditional software to manage them efficiently. Tones of work is still done over phone calls, spread sheets, fax lines and paper forms.

And over the last decade, R P. A became a buzz d for automating this type of work. Companies like U. I path, which was founded in two thousand and five, promise to enable the fully automated enterprise and empower workers through automation.

But despite its IPO in twenty twenty one and its current valuation, these last generation R, P, A companies couldn't fulfill the promise. True automation, the technology at the time just wasn't advanced enough. As a result, instead of true automation, these companies observed how their customers navigated a process, then built bots that mix the exact keystrokes and clicks that a human would make.

While these bots often provided meaningful business value when they functioned correctly, they stumbled if the process was not riddled and clearly defined or when IT underwent changes. In addition, implementing these boats s required expensive consultants, which meant rpa was only available to companies large enough to afford this heavy hand approach with l EMS. However, we believe the original vision of R P A is now possible.

Instead of hard coding, each determinists to step in a process, AI agents will instead be prompted with an end goal, for example, book an appointment for the customer transfer data from this document into this database, and then be empowered with the right tooling and context to take those actions on behalf of the company. They'll be adaptable to various data inputs and capable of handling changes in business processes. And because of this flexibility, they will be far easier to implement and maintain the traditional R P A systems, the future of A I ops and where the opportunity lies.

We are excited about this opportunity and intelligent automation for two main reasons. The potential market is enormous. For all the work that current software can handle. There are orders of magnitude more work that I cannot work that is being done via pendent paper, spread sheet, phone calls and facts.

Intelligent automation can address the current labor costs associated with this work, comprising over eight million Operations information clerk roles according to the bureau's labor statistics, as well as the spend associated with outsourcing this work, representing a meaningful portion of the two hundred fifty billion dollars business process. Outsourcing startups largely have a Greenfield opportunity in the space. There is often no existing software product for these workflows, given their spoke nature, the people were the product.

As a result, these rules never develop systems of record in the way other roles did. For example, sales force for sales, workday for H, R, meaning there is no software in comment to add A I into their existing product. Sweet, this market is wide open for startups. Specifically, we view the market opportunity as focused on two main areas, horizon onto A I enablers that execute a specific function for a broad range of industries and vertical automation solutions that build end to end workflows. Taylor to specific industries.

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partner horizontal L A, I enablers. Today, every intelligent automation company is building a similar set of capabilities in internal tooling. This creates a perfect opportunity for startups to simplify the process by focusing on one specific foundational component.

For example, almost every intelligent automation company has to pass unstructured data and output contextualized structured data. Many companies have built this out internally, and companies like reduct, to an extent, are working to be the horizontal, enabling to solve the specific need. We think there are many other core building blocks needed for complex intelligent automation, including, but not limited to, building web data crawlers, structuring data from unstructured sources or writing data back to legacy systems.

And to end vertical automation, we've previously written about our excitement for investing in vertical software, software that sells to one particular industry. We think this is a particularly good fit for intelligent automation since Operational agents will need to have the narrowing context in deep integrations to achieve the accuracy and consistency customers expect. Every industry has back office Operations that could be automated, and we ve already seen startup s use LLM to automate one flow as a strategic wedge to build deeper for specific industry needs in health care, for example, tenner has automated the referral management flow.

Referrals are the lifeblood of any growing health care practice, but accepting a referral use to require a lot of manual labor, for example, receiving a facts, having the front desk pull the information from the facts, and manually inputting that patient information into their system. Tr has built intelligent automation to solve this information transfer problem, using LLM to extract on structure data from P, D, F and faxes, run validation on the information, and then right that information back into the system of record E, H, R automatically. This dramatically reduces the time IT takes to accept a, which allows customers to secure new business more quickly.

In logistics, trucking brokers spend an enormous amount of time processing inbound orders and tracking loads. Now using intelligent automation, companies like happy robot can automatically check on load status and updates via A I powered voice assistance, and companies like ua are able to invest unstructured email data to automate Price quoting and order entry into the trucking management system tms. These companies often focus on automating a very narrow but very common and important workflow in their respective industries, often involving data and information transfer.

They do not seek to be the system of record at least initially, and can thus bypass the difficult rippin replace problems of going after legacy systems. They also start by automating revenue generating workflows, making themselves top priorities for their customers. Because these automation started the beginning of a workflow, these startups earn the right to the upfront data and downstream workflows.

We believe this approach is a winning formula for intelligent automation startups, and we're eager to partner with those going after this opportunity across different industries. We are incredibly excited by the future of intelligent automation. LLM s have given startups the opportunity to fulfill the initial promise of R P A. By automating tasks traditionally handled by labor, they can now tap into markets and opportunities that were previously too small or too difficult to pursue. We believe a number of large companies will be built here, both in the horizon onal enabling layer and in the vertical zed end to end solution for customers in different industries.

All right, back to real non A I N L W here. A couple things that are really interesting about this paper to me. The first of all is just the opportunity in general.

One of the things we discuss all the time with artificial intelligence is the idea that we're not looking to replace humans entirely. We're looking to take off their plates task that are extremely repetitive and mundane. Now of course, if we are being honest with ourselves, inevitably there will be some categories of jobs that do so many of those tasks.

They will be disrupted. The bio large, the goal again, is to disrupt people. It's to make work that is repetitive and mundane, but necessary, automatic and automated. The fact that there is so much further exploration of agents who are focusing in a vertical way on that side of tasks, I think, is really promising.

Now speaking of vertical, one of the really interesting gaps in the A I space is the space between those who are trying to create general agents, which include both the frontier labs as well as some starts like multiple, and those who are thinking instead about extremely specific vertical agenticity applications. Now the risk for the vertical companies is, of course, that the frontier labs figure out something and their general alist agents are just more performance even than the vertical agents are. However, I think that from where i'm sitting, the most likely scenario for how agents actually come to market is going to be in these highly vertical zed applications.

Now mostly when people talk about vertical applications, they're talking about an industry vertical. I think there will also be functional verticals, specific types of tasks that get done across different industries where agents have the liability to thrive and specialize. Over the next couple years, the pilots that have been focused on assisted work and co pilots inside the enterprise are going to increasingly give way to pilots that focused on agenticity.

But importantly, despite what mark bending off of sales force says, that doesn't mean that the assistant era of A I is somehow over. What I believe is that every single business process that we have today is going to be a ii fied in some way. I think that a lot of processes are, yes, going to be automated and taking care of by agents.

But I also think a lot of processes are going to be done by AI assisted superhumans. And what's more, when IT comes to the really valuable new opportunities, the things that companies can do that simply weren't possible before. I think there is going to be a ton of human innovation in orchestration. In other words, the big task for companies over the next couple years is not thinking about who they can replace with robots, is about how they get the robots in the human sitting together to be more powerful than either one could be alone. For now, though, that's going to do for the today's a brief appreciate your listings as always and until next time, peace.