As AI turns labor into software, the opportunity to productize external professional services (e.g., in legal or accounting) has become a hot topic. However, we believe there is also substantial opportunity in productizing internal work within organizations. These responsibilities often fall under the umbrella term of “operations” and can range from full-time data entry and front desk roles, 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 ops roles involve critical, but often repetitive and mundane tasks. Companies have historically attempted to automate these tasks by using Robotic Process Automation (RPA), but with generative AI, we believe true automation through agents is now possible. We’ve already seen early examples of agents working in production, such as Decagon’s automated customer support. And with companies like Anthropic launching capabilities like computer use to enable models to meaningfully interact with existing software, there is a clear emerging infrastructure stack for founders to build verticalized intelligent automation applications.
These examples preview a world in which AI agents are able to fulfill the original promise of RPA, turning what used to be operations headcount into intelligent automation and freeing workers to focus on more strategic work.
The Original Promise of RPA 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 are essential, but they often lack the APIs or direct integrations required for traditional software to manage them efficiently. Despite the shift toward software eating the world, tons of work is still done over phone calls, spreadsheets, fax lines, and paper forms.
Over the last decade, RPA became a buzzword for automating this type of work. Companies like UiPath, which was founded in 2005, promised to enable the “fully automated enterprise” and empower “workers through automation.” But despite its IPO in 2021 and its current valuation, these last-generation RPA companies couldn’t fulfill the promise of 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 mimicked 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 rigid and clearly defined, or when it underwent changes. In addition, implementing these bots required expensive consultants, which meant RPA was only available to companies large enough to afford this heavy-handed approach.
With LLMs, however, we believe the original vision of RPA is now possible. Instead of hard-coding each deterministic step in a process, AI agents will instead be prompted with an end goal (e.g., 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 than traditional RPA systems.
The Future of AI Ops and Where the Opportunity Lies
We’re excited about this opportunity in 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 it cannot: work that is being done via pen and paper, spreadsheets, phone calls and fax. Intelligent automation can address the current labor costs associated with this work – comprising over 8 million operations / information clerk roles according to the Bureau of Labor Statistics – as well as the spend associated with outsourcing this work, representing a meaningful portion of the $250 billion business process outsourcing (BPO) market.
Startups largely have a greenfield opportunity in this space. There is often no existing software product for these workflows given their bespoke nature: the people were the product. As a result, these roles never developed “systems of record” in the way other roles did (e.g., Salesforce for sales, Workday for HR), meaning there is no software incumbent to “add AI” into their existing product suite. This market is wide open for startups.
Specifically, we view the market opportunity as focused on two main areas: horizontal AI enablers that execute a specific function for a broad range of industries, and vertical automation solutions that build end-to-end workflows tailored to specific industries.
Horizontal AI enablers
Today, every intelligent automation company is building a similar set of capabilities and 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 parse unstructured data and output contextualized, structured data. Many companies have built this out internally, and companies like Reducto and Extend are working to be the horizontal enabler to solve this 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.
End-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 narrower context and 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 startups use LLMs to automate one flow as a strategic wedge to build deeper for specific industry needs.
- In healthcare, for example, Tennr has automated the referral management flow. Referrals are the lifeblood of any growing healthcare practice, but accepting a referral used to require a lot of manual labor (e.g., receiving a fax, having the front desk pull the information from the fax, and manually inputting that patient information into their system). Tennr has built intelligent automation to solve this information transfer problem – using LLMs to extract unstructured data from PDFs and faxes, run validations on the information, and then write that information back into the system of record (EHR) automatically. This dramatically reduces the time it takes to accept a referral, 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 Happyrobot can automatically check on load status and updates via AI-powered voice assistants, and companies like Vooma are able to ingest 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 rip-and-replace problems of going after legacy systems. They also start by automating revenue-generating workflows making themselves top priorities for their customers. And because these automations start at 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.
Conclusion
We are incredibly excited by the future of intelligent automation. LLMs have given startups the opportunity to fulfill the initial promise of RPA. 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 horizontal enabling layer and in the verticalized end-to-end solution for customers in different industries.
If you’re building an intelligent automation company, we’d love to chat.
