Useful AI looks unglamorous
Applied well, AI in the enterprise looks unglamorous: summarizing a contract before a reviewer reads it, drafting a first-pass response to a routine support ticket, extracting structured fields from an invoice or a claim form. These are high-volume, well-defined tasks where a human stays in the loop to check the output — exactly the profile where large language models are strong today and where the cost of an occasional error is low and recoverable.
Where initiatives stall
Where we see AI initiatives stall is when they’re aimed at judgment-heavy, low-volume decisions — final underwriting calls, clinical diagnoses, irreversible financial transactions — before the organization has built the monitoring, evaluation, and escalation infrastructure that judgment-heavy AI actually requires. The model isn’t the hard part; the guardrails are.
Treat every AI workflow as a product
Our approach is to treat every AI workflow as a product with its own accuracy targets, human-in-the-loop checkpoints, and rollback plan — the same discipline we’d apply to any other system that touches regulated or financially significant decisions. That discipline is what turns a promising demo into something operations will actually rely on.