That's precisely what we found in our research as well! We outlined it in our observations too (excerpt below):

The most successful deployment strategies we’ve seen started with:

simple and specific use cases with clear value drivers, that were low risk yet medium impact;

weren’t majorly disruptive to existing workflows;

preferably automating a task that the human user dislikes (or was outsourced);

the output of the workflow can be easily/quickly verified by the human for accuracy or suitability; and

demonstrated clear ROI quickly

Given the current levels of technological development, AI Agents work best when narrowly applied to very specific tasks and operating under a specific context. For instance, we’ve seen this in healthcare with revenue cycle management processes (claim and denial management) that health systems were already outsourcing to third-party providers.

The land-and-expand strategy for AI agents is very different to traditional SaaS. Given enterprises are increasingly under pressure from the C-Suite to incorporate AI into their work, there are plenty of opportunities for startups to “land” but it’s much harder to “expand” – and not only that, it’s taking much longer to expand even when they want to expand, because it’s a use case by use case rollout.

Much like the iconic Volkswagen ad, sometimes it’s better to “Think Small” and build trust first, rather than attempt too many use cases (and excessively complex use cases) right off the bat.

> Much like the iconic Volkswagen ad, sometimes it’s better to “Think Small” and build trust firsy

Poor choice of example for building trust - Volkswagen: - lie big on emissions/fuel efficiency performance.