Insights and strategies for operational businesses looking to make AI work in the real world — not just in demos.
AI-driven layoffs are colliding with data showing AI isn't working in large enterprises. Read across McKinsey, HBR, and Block's Jack Dorsey and a sharper picture emerges: most companies are stockpiling tools when they should be building a company brain that gets smarter every time the business operates.
AI is widely accessible, but adoption at the individual level is still lagging. We think it's a curiosity gap — and that a personal, low-friction AI conversation is the bridge that can get people over it.
AI can rank your most at-risk service agreements by combining visit history, billing records, service notes, and external signals into a single churn-risk score. The output is a prioritized save list with specific evidence for each account. Four files, twenty-five accounts, twenty minutes.
Margin leakage is hard to diagnose because the number shows up in one place and the cause lives somewhere else. The cost report shows bid margin versus actual margin, but the explanation is spread across estimates, change order logs, project notes, and decisions made while the job was moving.
Knowing which accounts are ready to expand has always been a bit of a fallacy. The signals are real, just spread across CRM fields, rep notes, call transcripts, and outside news. With AI, that begins to change in a way that wasn't really achievable before.