RPA Due Diligence vs AI-Assisted Due Diligence

This article is written for acquirers of Lower-Middle-Market (LMM) software companies—including Private Equity buyers, holding companies, and strategic acquirers.

Traditionally, RPA (Robotic Process Automation) has been seen as a way to automate the manual, click-by-click parts of due diligence. Unlike API-based or code-based integrations that connect systems through structured data exchange or custom logic, RPA works by imitating a human user’s interactions with software interfaces.

However, what once seemed like a promising technique that could become a huge time-efficiency lever for sophisticated due diligence teams, has begun to fade into "one of many" tools in the toolbox... and even, perhaps, one of increasingly rare use. Here's why.

What Diligence Teams Try Before RPA

When information needs require a diligence team to ingest and normalize certain rote information that is crucial to the deal but repetitive to gather, RPA could very well be an option. However, it plays second fiddle to any of the following:

• Asking the target to supply the information in the required format. A motivated seller is often "eager to please", and an information gathering/synthesis effort by the acquirer, can often be dramatically simplified by carefully adding to an ongoing information request that the seller works to respond to.
• Third party tooling. Lets use the example of an acquirer that wants to do certain financial statement analysis, and routinely acquires companies who use Quickbooks for accounting. It's entirely possible (and likely) that there is third party tooling that could gather/synthesize the desired information from the target's system-of-record, without the brittleness and configuration overhead of building out an RPA-led workflow.
• API tooling. In any era of Model Context Protocol, the most sophisticated diligence teams that might have considered RPA for certain tasks in the past, are looking instead/first to Model Context Profile to see if they can efficiently craft queries/searches/reports that meet their information needs (with consent/support of the seller of course).

For these reasons, RPA is becoming less popular with even the most sophisticated diligence teams.

Where RPA Still Fits In

RPA still does have a place in due diligence, though, in certain isolated/specific situations. The most common ones are:

• An acquirer whose targets are in "legacy software" ecosystems where third party tooling and API access are unavailable, unreliable, or impractical. In cases where navigating a web-based UI or installed application UI are more practical for information gathering than those approaches, RPA is still a viable and interesting solution.
• An acquirer doing a geographic rollup of otherwise similar companies. For example, an acquirer rolling up PEOs (Professional Employer Organizations) fitting specific criteria -- for example, ones that are known to rely on the same backend system. In those cases, the uniformity of the targets, may make it worthwhile to automate certain parts of the diligence process via RPA.
• A team that already has RPA infrastructure and expertise in place, making it cost-effective to extend existing bots for new but similar diligence tasks. In such cases, the marginal cost of adapting RPA may be lower than implementing new AI or integration tooling.
• Highly regulated environments where every step of the process must be logged, audited, and replayable exactly as performed. RPA’s deterministic nature can make it attractive where compliance or repeatability outweigh flexibility.

The Advent of AI-Assisted Diligence

AI-assisted diligence represents a shift from brittle task automation to adaptive intelligence. Instead of mimicking clicks or scraping screens, AI models can interpret unstructured data, summarize findings, and respond dynamically to new document types or business models. These systems rely on reusable prompt packs and fine-tuned models that can be applied across a wide range of deals, reducing setup time and improving insight quality.

• Reusable intelligence. Prompt packs and model configurations can be shared and refined across deals, turning experience from one engagement into a performance boost for the next.
• Contextual flexibility. AI can adapt to variations in document structure, terminology, and business context without requiring major reconfiguration.
• Analyst augmentation. Rather than replacing human judgment, AI helps diligence teams focus on interpreting findings and making strategic decisions rather than processing data.

Together, these capabilities make AI-assisted diligence not just faster, but more scalable and insight-driven than RPA-based approaches.