What works, what doesn't, and what we're shipping for clients. A new piece most days.
AI-generated demand forecasts look precise — until you realize two reps are using the same pipeline stage to mean completely different things. The fix isn't a better model; it's standardizing stage exit criteria before the data ever gets there.
Read →AI can move fast through vendor onboarding — until it hits W-9s and banking details. Here's why that step breaks automation and how to fix the data handoff before your pilot does.
Read →AI-powered support routing degrades silently as your product evolves and your tag taxonomy falls behind. Before you automate escalations, here's how to audit what's actually breaking.
Read →AI matching tools can cut hours off your month-end close — until they hit intercompany reconciliation and quietly fall apart. Here's why it happens and the data-hygiene fix that stops it before it starts.
Read →AI contract review tools are only as good as the contracts they can actually see. If your negotiated redlines are living in email threads and shared drives, you're routing incomplete documents to legal and calling it a reviewed deal.
Read →Teams rushing to automate sales-to-CS handoffs with AI keep blaming the model when the output is garbage. The real problem is upstream: the CRM fields that feed the summary were never filled in consistently to begin with.
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