AI Readiness Assessment
How ready is your business for AI? Take the BANKDENS AI readiness assessment to get an AI maturity score across six dimensions, see what is holding you back, and find where AI could create the most value.
No contact information required to see your results.
What an AI readiness assessment measures
Six dimensions · 28 questionsDirection & Priorities
Whether leadership has decided what AI is for and who owns moving it forward.
Leadership alignment on what AI is for, which outcomes it serves, and who is accountable for moving it forward. Without this, AI work stays a collection of individual experiments.
Opportunities & Value
How clearly you have identified where AI helps and what it is worth.
A named, sized list of places AI can create value in your business — and evidence that those places are worth the effort before anyone builds anything.
Data & Systems
Whether your information is organized, accurate, and reachable.
Whether the information AI would rely on is organized, accurate, and reachable across the systems you already run. Most stalled AI programs stall here.
Tools & Execution
How reliably a good idea becomes something people can actually use.
How dependably a promising idea becomes something people can use day to day: build capacity, integration, and the discipline to ship rather than demo.
People & Adoption
How confidently your teams use AI and change how they work.
Whether teams have the confidence, training, and permission to change how they work — the difference between a tool that is licensed and a tool that is used.
Guardrails & Scale
Whether use is safe, reviewed, measured, and repeatable beyond one team.
Review, security, measurement, and repeatability. What lets one team's success safely become the way the whole business operates.
Every question asks about something you can observe in your business. There is no technical vocabulary to decode, and there are no right answers — only a clearer picture of where you stand. The same six dimensions frame our applied AI strategy work.
The AI readiness checklist
If you want the short version before answering anything, this is what the assessment is checking for. Organizations that can honestly tick most of these are ready to scale AI; the ones that cannot usually have a foundation gap worth fixing first.
- Leadership has agreed what AI is for and named an owner
- AI opportunities are written down, sized, and prioritized by value
- Core business data is accurate, organized, and reachable by the systems that need it
- There is a dependable path from a working idea to something in production
- Teams are trained and expected to use the tools they have been given
- Usage is reviewed for accuracy, privacy, and security before it scales
- Results are measured against the outcome AI was meant to improve
- What works in one team can be repeated in another without starting over
How your AI readiness score is calculated
Each answer contributes to one of the six dimensions. Dimension scores are combined into a weighted overall score out of 100 — direction and data carry the most weight, because they are the constraints that most often decide whether AI work reaches production. That score places you in one of five maturity stages:
Exploring · 0+
Interest exists, but AI is still mostly unfamiliar or individual-led.
Experimenting · 34+
People are trying AI, but the effort is fragmented.
Building · 52+
Priorities are emerging and useful solutions are being implemented.
Scaling · 72+
AI is becoming repeatable, integrated, and appropriately governed.
AI-Enabled · 87+
AI is embedded across the business and continuously improved.
Recommendations are conditioned on your actual answers, so a Building score with a data gap reads differently from a Building score with an adoption gap. If the result points at execution capacity, our AI prototyping and MVP work is the usual next step.
AI readiness assessment: common questions
Related reading: why AI is harder for smaller businesses and what "ready" actually looks like.
Where the results lead
The assessment is the front door to our Applied AI strategy and implementation practice. When a use case is clear enough to test, it moves into AI prototyping and MVP builds. If you're still weighing what to build first, start with POC vs. prototype vs. MVP.
Not sure the assessment is the right starting point?
Tell us what you're weighing and we'll point you at the shortest path to a useful answer — assessment or not.