AI Readiness Assessment
Twenty questions for the executives who have to make AI pay off. Get a scored readiness profile across four dimensions and a short list of what to fix first.
What it measures
Most AI programs stall for reasons that have little to do with the model. This assessment looks at the conditions around it: who owns it, what data it can reach, how work is run and how risk is managed.
Goals, ownership, funding and how value is measured.
Access, quality, integration and the platforms teams can build on.
Documented, standard processes and a real path from pilot to production.
Policy, review, data controls, human oversight and vendors.
We need a clean, company-wide data foundation before we can start with AI.
You need clean, reachable data for the first workflow you pick. Fix that slice, prove value, then widen. Waiting for perfect data is the most common way programs never start.
Your readiness profile
Your position on the climb
Scores are calculated only from your answers. They are a structured self-assessment, not a benchmark against other organizations.
What this means
Prioritized from your lowest-scoring dimensions and answers. Each one is a concrete step you can assign this quarter.
Plan your climb
Pick the moves your team could make in the next two quarters. Each one lifts that answer to level 4: written down, owned and working. Watch the score, the radar and your camp move.
In our experience, the lowest dimension sets the pace for the whole program. Two moves on your weakest dimension usually beat five spread across the strongest.
“Email me my report” opens a draft to Leron at Baxter Mountain with your scores, so the full report can be sent back to you. Your details are optional and only go into that draft, on your device.
Field guide
What we see again and again when operations teams move from AI interest to agents running real work.
A strong strategy cannot carry a process nobody has written down. A great data platform cannot carry an agent that no one is allowed to approve. Programs move at the pace of their lowest score, which is why the next move almost always sits there.
Rule of thumbSpend the next quarter lifting your lowest dimension, not polishing your highest.
Good first agents start from a painful, high-volume workflow with clear rules and a measurable baseline: invoice matching, vendor onboarding, case triage. The model choice comes later. A pilot without a baseline cannot prove anything.
Rule of thumbMeasure hours per case and error rate for two weeks before the pilot starts.
Decide up front where a person approves, reviews a sample or handles the exception. That design is what earns trust from finance, risk and the team doing the work. It also makes the agent easier to audit and improve.
Rule of thumbAutomate up to about 80% of a task. Keep people on decisions that carry financial, legal or customer risk.
We build custom assessments, skills and internal apps as part of our engagements. Tell us what you need.