Most AI and transformation initiatives don't stall because the model isn't good enough. They stall because nobody has a clear picture of how the work actually gets done.

Discovery still relies on workshops, interviews, and outdated documentation. Edge cases, workarounds, and the tribal knowledge behind key decisions get missed early. They show up later as rework, delays, and pilots that never reach production.

At Linc AI, we help organizations scale their AI transformation by capturing how work really happens across people, systems, and decisions. Teams record workflows through screen recordings, video uploads, meeting transcripts, and AI-led follow-up interviews. Linc turns that into implementation-ready SOPs and process maps, shows process variations and gaps across departments, and points to where AI and automation will have the most impact.

I spend most of my time with transformation leaders, operators, and implementation teams, learning why process discovery breaks down in practice and what it takes to deploy AI effectively at scale.

I'm always glad to compare notes on how teams handle process discovery, documentation, and workflow improvement.