The AI Readiness Test
Eight Questions to Ask Before You Implement AI Across Your Revenue Lifecycle
Before the next AI tool gets evaluated, procured, or piloted, there's a shorter and cheaper exercise worth running first:
- Testing whether the process it is meant to automate is actually ready to be automated.
With one question per stage of the revenue lifecycle. Answered honestly, this takes an afternoon. But skipped, it costs a lot more than an afternoon way later.
The Eight Questions
1. Lead Acquisition — Is there a documented ICP and qualification criteria specific enough that two different people would identify the same leads as qualified?
2. Sales & Evaluation — Does the evaluation process have defined stage gates and success criteria, agreed with the buyer, or does progress depend on a sales rep's read of the room?
3. Onboarding — Are activation milestones defined and validated against actual retention data, or is "activated" a feeling the customer success team has developed over time?
4. Delivery — Can the delivery methodology be executed consistently by someone who wasn't involved in the last ten engagements, or does quality depend on which specific person is assigned?
5. Customer Success — Has the health scoring signal been validated against real churn and expansion outcomes, or does it rely on metrics that seemed reasonable when the dashboard was built?
6. Invoicing & Payments — Is the billing data model clean and are the business rules for proration, dunning, and edge cases explicitly documented, or does the process depend on manual reconciliation catching what the system doesn't?
7. License & Contract Management — Is there a single, structured system of record for every active license and contract, or is the real record a spreadsheet someone updates when they remember to?
8. Renewal & Expansion — Is there a defined cadence of touchpoints and health checks throughout the engagement, or does renewal outreach only start once the contract date is already close?
Reading the Results
A "no" or a hedge on any of these isn't a reason to avoid AI at that stage. It's a precise map of where an AI deployment will amplify what's already there instead of improving it. That's valuable information. It tells you exactly where to invest in process hardening first, and exactly which AI investments will pay off immediately because the foundation underneath them is already sound.
Most companies find they're ready at two or three stages and exposed at the rest. That's normal. It's also the actual starting point for a serious AI roadmap. One sequenced around readiness, not around whichever tool got the most attention at the last industry conference.
Why This Matters Before the First Conversation
A CEO who's run this test before talking to a vendor, a consultant, or an internal team walks into that conversation already knowing which stage of the business needs process work and which stage is genuinely ready for automation. That's a materially different conversation than "we want to add AI somewhere". It's specific, it's prioritized, and it's grounded in an honest look at the business rather than a vendor's roadmap for what their tool happens to do.
Taking This Further
Eight questions surface where the gaps are. They don't quantify what those gaps are costing, or what fixing them first is worth relative to the AI investment being considered. That's the next-level diagnostic. A structured Revenue Engine Risk Assessment that scores risk across all eight stages and returns a concrete picture of where the business is losing the most, and in what order to fix it.
The eight questions cost nothing but honesty. What they reveal is usually worth more than the AI budget being considered to answer them.