Historically, vertical SaaS has centered on workflows and data, simplifying complex processes within a specific industry. The data stored within large vertical SaaS companies ultimately leads to and integrates with nearly all of the core workflows within an organization. Before the AI boom, investing in new vertical SaaS platforms typically meant one of three things:
- Applying a similar playbook to smaller markets without existing solutions
- Targeting new customer segments within large industries that already have a “winner” - typically downmarket customer bases or more specialized subgroups of an industry
- Attempting to displace the current entrenched legacy systems solely through a stronger UI or specific workflow
The unique challenge—and the compelling appeal—of vertical SaaS is that once a platform is the central hub of truth and data for the organizations it serves, it becomes incredibly difficult to replace. Systems like Epic's electronic health record (EHRs), ServiceTitan's verticalized CRM, Toast's POS solution, and Blackbaud's fundraising management tools embed themselves deeply within an organization. Switching off any of these is incredibly burdensome since organizations run their entire businesses from the data.
Given the reliance on those platforms, more niche solutions often struggle to ascend in the market as they simply act as a method to feed data into the core operating system. This limits the ability to charge larger ACVs over time and the ability to build a competitive moat.
The data dominance of these platforms relies on two core inputs. The first is through integrations or product offerings that feed structured data into the vertical SaaS platform. The second is through manually inputting previously unstructured data into the platform. Advancements in AI and more broadly, the ingestion of unstructured data, allow that second portion to become automated. For verticals in which legacy platforms rely heavily on manual data input, it represents a real challenge to their supremacy.
Consider the healthcare industry, where traditionally, clinicians or their scribes have had to manually enter all patient data and visit summaries into EHRs. These systems have been repositories of vast amounts of data, while point solutions could only address peripheral issues. If an EHR lacked scheduling capabilities, a new platform could add that feature, but the point solution would not have access to the existing patient data. It would feed any data back into the EHR. Solutions like AI scribing tools that sit in clinician meetings and ingest all of the audio data from those meetings have access to a much larger and core portion of data. Without relying on the EHR, they can build deeper workflows and increase the amount of clinician engagement with their platform. Over time, access to patient visit data allows them to build deeper workflows than other point solutions and ultimately replace the EHR as the main point of contact for data.
The ability to access and utilize unstructured data means that AI-enabled solutions can gradually eliminate the need for direct interaction with legacy platforms and, as they build deeper workflows over time, replace them. As software development becomes more efficient, the barriers erected by workflow automation and simple data management weaken. Platforms that have dominated by controlling data access for decades are finally starting to see their influence decline.
The next generation of major vertical software solutions will likely leverage AI to process vast quantities of unstructured data in unprecedented ways. These innovations will unlock efficiencies that transform industries and deliver value to customers in ways that legacy vendors will be slow to match. If you’re building a vertical software platform and the above sounds like your plan, please reach out to duncan@ansa.co.
