Is your organization ready for AI? See exactly where you stand.
Five foundations decide whether AI investment pays — strategy, data, systems, team and governance. See where you stand on each, with your score, profile and recommendations on screen the moment you finish.
Your level, five-dimension profile and recommendations appear on screen the moment you finish — no email, and the branded PDF report is yours to keep.
01Strategy & Use Cases
02Data Foundation
03Systems & Infrastructure
04Team & Skills
05Governance & Risk
Scored in this browser — nothing is sent unless you choose to.
Written by DigiWagon’s delivery practiceScored with the published DigiWagon AI Readiness FrameworkISO/IEC 27001 and ISO 9001 certifiedWork we’ve delivered
Who this is for
Founders and CEOs weighing a first serious AI investment
CTOs and technology leads sizing the gap between ambition and foundations
Operations and data leaders asked to make AI actually work
The flow
How it works
01Answer quick multiple-choice questions about how your organization actually works today — options, not essays.
02Get your readiness level and a five-dimension profile, scored against the published rubric.
03Keep the branded PDF report — and send your results to us if you want an expert readout.
Every result is one level and five dimension scores — an example profile.
The framework
Five dimensions, scored in the open
Every answer carries zero to three points by the maturity it describes. Three questions per dimension give a dimension score out of nine; the five dimensions together map to one of four readiness levels. The rubric is published because a score you can't inspect isn't worth acting on.
0–3 per answer→3 questions per dimension→dimension score out of 9→5 dimensions→your result out of 45
No gate. Full results on screen — email is never required to see them.
Nothing stored. Answers are scored in your browser and never leave this page unless you choose to send them.
No invented benchmarks. Every number derives from the published rubric and your own answers.
All five dimensions weigh equally in v1 — deliberately, until real-world results earn differentiated weights. Framework v1 · September 2026.
Whether AI has a defined job in your business: named use cases, a way of ranking them, and leadership that owns the outcome.
Why this matters
Most stalled AI initiatives fail here, not in the technology — a pilot built without a prioritized use case and an accountable sponsor demonstrates nothing and changes nothing.
How clearly has your organization defined what it wants AI to achieve?
How do you decide which AI opportunity to pursue first?
Who sponsors AI inside the organization?
02
Data Foundation
Where the data an AI system would learn from lives, how much you trust it, and how much usable history it carries.
Why this matters
Models are downstream of data. Siloed, untrusted or shallow data doesn't stop an AI project — it quietly caps what the project can ever be worth.
Where does the data an AI system would need live today?
How much do you trust the accuracy of that data?
How much history does your data carry?
03
Systems & Infrastructure
Whether your current systems can feed an AI service and act on its output — integration, cloud posture, and how changes reach production.
Why this matters
An accurate model that can't read your systems or write back into your workflow is a report, not a capability. Integration is where AI value is realized or lost.
How easily can your current systems connect to new services?
Where does your software run?
How do changes reach production today?
04
Team & Skills
The hands available for AI work: in-house capability, day-to-day fluency with AI tools, and whether anyone has the capacity to own an initiative.
Why this matters
Readiness is people before platforms. A team that already works with AI daily adopts what you build; a team without capacity turns every initiative into someone's fourth priority.
What technical capability exists in-house for AI work?
How is your team using AI tools day to day?
If an AI initiative started Monday, who would run it?
05
Governance & Risk
The rules of the road: policy for how AI may be used, clarity on the regulations that bind your data, and how you'd know if a system misbehaved.
Why this matters
Governance is what lets you say yes quickly and safely. Without it, every AI decision re-litigates risk from scratch — or worse, nobody notices a system drifting until a customer or regulator does.
What rules exist for how AI may be used in your organization?
How well do you understand the regulations that apply to your data and industry?
If an AI system started giving bad answers, how would you know?
What your score means
Exploring0–13 of 45
AI investment would be premature this quarter — and knowing that is worth more than a stalled pilot. Your leverage is in foundations: one named use case, one consolidated data source, one integration seam. Small, deliberate groundwork now is what makes next year's AI work land.
Emerging14–24 of 45
You're ready for a narrow, well-chosen pilot — not a program. Pick the use case your strongest dimension can carry, keep its scope honest, and use it to pull the weaker foundations up behind it. The pilot's job is to teach your organization, not just to work.
Ready25–35 of 45
The foundations are in place; the risk has moved from "can we?" to "are we building the right thing?". Choose the first production build for business impact and visibility, hold it to the metric it was funded on, and design it so the second and third builds reuse its plumbing.
Advanced36–45 of 45
You're ready to scale — the question is portfolio, not pilots. The organizations at this level get ahead by industrializing: shared platforms instead of per-project stacks, evaluation and monitoring as standard practice, and a use-case pipeline that leadership reviews like any other investment portfolio.
FAQ
About this assessment
Short answers to what people ask before they take it — and what to do with the score after.
01What is an AI readiness assessment?
An AI readiness assessment measures how prepared your organization is to adopt artificial intelligence successfully. It examines the foundations AI projects depend on — business strategy, data quality, systems, team skills and governance — and shows where you stand today, so you can close the right gaps before investing in development.
02How long does the assessment take?
About five minutes. The assessment asks fifteen multiple-choice questions, three for each of the five readiness dimensions. There is nothing to prepare and no documentation to gather — answer from your working knowledge of the organization, and your scores appear on screen immediately after the final question.
03Do I need to share my email address to see the results?
No. Your score, dimension breakdown and recommendations all appear on screen the moment you finish — nothing is gated behind a form. Sharing details is only involved if you choose to send your results to our team for an expert readout, which uses the standard contact form.
04What does the assessment measure?
Five dimensions that decide whether AI initiatives succeed: strategy and use cases, data foundation, systems and infrastructure, team and skills, and governance and risk. Each dimension is scored from three questions, so the result shows not just an overall level but exactly which foundations need attention first.
05How is the score calculated?
Every answer carries zero to three points, reflecting the maturity it describes. Your fifteen answers add up to a score out of forty-five, which maps to one of four readiness levels — Exploring, Emerging, Ready or Advanced — and each dimension's three questions produce its own score out of nine.
06What should we do after the assessment?
Start with your weakest dimension — the recommendations under each score explain what closing that gap involves. If you want a second opinion, send your results to our team: we review them against the delivery experience behind our AI work and reply with a practical readout, without any obligation.
Want an expert readout of your score?
Send your results through and we'll review them against the delivery experience behind our AI work — where to start, what to defer, and what closing your gaps actually involves. A working conversation, not a pitch.
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