Most transformation and automation projects do not fail because the model is not good enough.

They fail because nobody has a clear picture of how the work is actually being done.

Discovery usually depends on workshops, interviews, and outdated documentation. That means edge cases, workarounds, and decision logic get missed early, then show up later as rework, delays, and failed implementations.

At Linc AI, we’re building a process intelligence platform that helps teams capture real workflows through screen recordings, video uploads, meeting transcripts, and AI-led follow-up interviews. From that, teams can generate implementation-ready artifacts like SOPs and process maps, analyze gaps across departments, and get a clearer view of where improvement opportunities actually are.

I spend a lot of time speaking with transformation leaders, implementation teams, and operators about why process discovery breaks in practice and what it takes to make AI deployments work in the real world.

I’m always interested in hearing how teams are currently handling process discovery, documentation, and workflow improvement.