Every leadership team I talk to knows AI matters. Fewer know where to start. Having scaled revenue orgs from less than $5M to $160M+, here is what separates companies that see real ROI from those still “exploring”:
1. Start in revenue operations
AI pilots stall when they are handed to a lab team disconnected from P&L pressure. Put your first use case in the function that touches revenue directly — forecasting accuracy, pipeline scoring, or customer churn signals. You will get faster feedback loops, and a business case leadership cares about.
2. Fix your data before you buy a platform
I have watched companies spend six figures on AI tools running on broken CRM hygiene. If your Salesforce or HubSpot data is inconsistent, AI will just make bad decisions faster. Spend 60 days cleaning and structuring data before you evaluate vendors — it is unglamorous, but it is the actual unlock.
3. Tie every use case to a KPI you already report on
Don’t launch AI initiatives that need a new dashboard to justify themselves. If you already track close rates, CAC, or EBITDA margin, pick an AI use case that moves one of those numbers within a quarter. Executive buy-in follows proof, not promises.
AI transformation is not a big-bang initiative — it is a series of small, measurable bets that compound. Start narrow, prove value fast, then scale what works.