The gap between knowing you need AI and knowing what to do about it is where most mid-market companies get stuck. Leaders of $5M-$20M organizations don't lack ambition; they lack a map. The vendors selling them tools have every incentive to make the problem sound like a software purchase. It isn't.
Start with outcomes, not algorithms. Before evaluating a single tool, name the three business results that would matter most this year: faster quote-to-cash, fewer manual handoffs, higher win rates. AI is only useful in service of a number you already care about.
Audit your data reality. The most common reason pilots stall is that the data needed to power them lives in five disconnected systems. A 30-day data readiness review saves months of downstream frustration and tells you exactly what to fix first.
Run one pilot, not ten. The temptation is to prove AI everywhere at once. Resist it. Choose a single high-volume, low-variability process, instrument it, and measure the before-and-after. A visible win builds the internal permission you'll need for everything that follows.
The companies pulling ahead aren't the ones with the biggest budgets. They're the ones who turned vague pressure into a concrete, sequenced plan and executed it.