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analytics ADAPT® Toolkit › AI Adoption & Governance

AI Readiness Gap Analyzer

Two tracks. One diagnostic. Separates what you're investing in from what's actually holding you back. Produces a 90-day Organizational Readiness Sprint targeting the three changes most likely to move the needle — because organizational conditions account for nearly twice as much of AI value as personal skill does.

personTrack A: Personal readiness (20 questions)
corporate_fareTrack B: Organizational readiness (24 questions)
sprint90-day sprint plan
48% Org readiness → AI value difference
25% Personal readiness → AI value difference
~2× Org ROI vs personal skill investment

McKinsey research: organizational readiness — strategy clarity, workflow architecture, governance, and measurement — accounts for nearly twice as much of the difference between AI leaders and laggards as personal skill does.

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2A
2B
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Context
person_search
Before you start
A little context helps the AI produce a more specific analysis.
The more specific you are, the more actionable the sprint plan. Industry, what tools you've deployed, what's happened so far.
person
Track A — Personal AI Readiness
Rate each statement on a scale of 1 (strongly disagree) to 5 (strongly agree). Answer based on how things actually are — not how you'd like them to be. The gap analysis is only useful if the inputs are honest.
20 questions · 5 dimensions · ~8 minutes
Skill & Fluency Skill & Fluency
I can produce genuinely useful outputs from AI tools on my first or second attempt, without significant trial and error.
Strongly disagree
Strongly agree
I write prompts that specify format, audience, constraints, and desired output — not just a topic.
Strongly disagree
Strongly agree
I can accurately predict when an AI tool will produce a reliable output versus when it will hallucinate or mislead.
Strongly disagree
Strongly agree
I use at least three meaningfully different AI tools or capabilities in my current work (not just one tool for one task type).
Strongly disagree
Strongly agree
Learning Orientation Learning Orientation
When an AI output disappoints me, my first instinct is to improve my prompt or approach rather than conclude the tool doesn't work.
Strongly disagree
Strongly agree
I actively seek out what other people in my field are doing with AI — not just to stay current, but because I'm genuinely curious.
Strongly disagree
Strongly agree
In the past 90 days I have deliberately experimented with an AI use case outside my normal work tasks.
Strongly disagree
Strongly agree
I can name at least two things I've learned about working with AI in the past 30 days.
Strongly disagree
Strongly agree
Experimentation Habit Experimentation Habit
I use AI tools on most working days, not just occasionally or for specific one-off tasks.
Strongly disagree
Strongly agree
I have tried using AI for something in my work that didn't work out — and I learned something from it.
Strongly disagree
Strongly agree
I routinely use AI as a thinking partner or critic, not just as a production assistant.
Strongly disagree
Strongly agree
I have a personal workflow or habit built around AI use — it's embedded, not add-on.
Strongly disagree
Strongly agree
Judgment & Verification Judgment & Verification
I verify AI-generated facts, citations, and data before acting on them or sharing them.
Strongly disagree
Strongly agree
I can identify when an AI output is plausibly wrong but written in a way that sounds authoritative.
Strongly disagree
Strongly agree
I have clear personal standards for when I use AI output directly, when I edit it heavily, and when I discard it.
Strongly disagree
Strongly agree
I protect the parts of my craft that depend on my own judgment — I don't delegate those to AI.
Strongly disagree
Strongly agree
Integration Depth Integration Depth
AI tools have materially changed how I do at least one significant part of my job — not just made it faster.
Strongly disagree
Strongly agree
I could articulate a specific workflow in my work that AI has meaningfully redesigned, not just accelerated.
Strongly disagree
Strongly agree
I use AI in ways that save me more than 2 hours per week on tasks that previously required significant effort.
Strongly disagree
Strongly agree
I have used AI to improve the quality of something — not just the speed — in a way I can point to.
Strongly disagree
Strongly agree
Answer all 20 questions to continue.
corporate_fare
Track B — Organizational AI Readiness
Now rate your organization — not your team or your manager's intentions, but the actual conditions that exist for people trying to work with AI at scale. 1 = this does not exist or is aspirational only. 5 = this is real, documented, and consistently applied.
24 questions · 6 dimensions · ~10 minutes
Strategic Clarity Strategic Clarity
Leadership has articulated a specific, written AI strategy that goes beyond "we will use AI to stay competitive."
Not at all
Fully in place
Employees know which business outcomes the organization is trying to move with AI — not just which tools are approved.
Not at all
Fully in place
There is a defined owner of AI strategy at the executive level (not just IT).
Not at all
Fully in place
The AI strategy distinguishes between automation (doing existing work faster) and transformation (changing how work is done) — and leadership is clear about which is the priority.
Not at all
Fully in place
Infrastructure & Access Infrastructure & Access
Employees who need AI tools for their work have access to them — without having to request, justify, or wait.
Not at all
Fully in place
The data that AI tools need to be useful for our work is clean, accessible, and appropriately governed.
Not at all
Fully in place
There are no significant technical barriers (security blocks, procurement delays, device restrictions) preventing people from using approved AI tools in their daily work.
Not at all
Fully in place
People have access to AI tools appropriate for their role — not a one-size-fits-all rollout of a single product.
Not at all
Fully in place
Workflow Architecture Workflow Architecture
AI has been integrated into at least one core workflow — not as an optional add-on, but as a redesigned process.
Not at all
Fully in place
When AI is embedded in a workflow, there are defined human checkpoints — points where a person reviews and decides, not just approves.
Not at all
Fully in place
Workflow redesign for AI is treated as an organizational change initiative, not just an IT deployment.
Not at all
Fully in place
We track whether AI is eliminating handoffs and friction — not just whether people are using it more.
Not at all
Fully in place
Governance & Norms Governance & Norms
There are documented norms for when AI output should and should not be used without human review.
Not at all
Fully in place
Employees know which tasks and decisions AI should not be the primary input for — and those limits are enforced, not just suggested.
Not at all
Fully in place
There is a clear process for raising concerns about AI errors, biases, or misuse — and people actually use it.
Not at all
Fully in place
Leaders model the AI norms they expect from their teams — they don't have one set of rules for themselves and another for everyone else.
Not at all
Fully in place
Capability Building Capability Building
There is a structured program to develop AI capability in our workforce — not just access to a library of optional courses.
Not at all
Fully in place
AI skill development is treated as a talent investment, not a compliance exercise. People actually develop capability, not just complete training.
Not at all
Fully in place
Managers are equipped to coach their teams on AI use — not just direct them to tools.
Not at all
Fully in place
We distinguish between the AI skills different roles actually need — and tailor development accordingly.
Not at all
Fully in place
Measurement & Feedback Measurement & Feedback
We measure the actual impact of AI on business outcomes — not just adoption rates or licenses used.
Not at all
Fully in place
There are leading indicators that tell us whether AI investments are creating value before we see results in annual reports.
Not at all
Fully in place
When AI initiatives underperform, there is a structured review process to understand why — not just a decision to deploy more or give up.
Not at all
Fully in place
AI investment decisions are made using evidence about what has worked and what hasn't in our specific organization.
Not at all
Fully in place
Answer all 24 questions to continue.
Analyzing your readiness gap…
Scoring both tracks, identifying the investment pattern, and building your 90-day sprint plan.
AI Readiness Gap Analysis
ADAPT® AI READINESS GAP ANALYZER · AI VERDICT
PERSONAL READINESS
—
25% of AI value difference
(McKinsey)
ORGANIZATIONAL READINESS
—
48% of AI value difference
(McKinsey)
WEIGHTED COMPOSITE
—
Reflects where value
is actually created
TRACK A — PERSONAL · Dimension Scores
TRACK B — ORGANIZATIONAL · Dimension Scores
insights INVESTMENT PATTERN & MCKINSEY CONTEXT

person PERSONAL READINESS ANALYSIS

STRENGTHS
GAPS
corporate_fare ORGANIZATIONAL READINESS ANALYSIS

STRENGTHS
GAPS
sprint 90-DAY ORGANIZATIONAL READINESS SPRINT

person_check A NOTE ON PERSONAL DEVELOPMENT

The 48/25 split

McKinsey research on AI value creation identifies that organizational readiness — strategic clarity, workflow architecture, governance, capability building, and measurement — accounts for 48% of the difference between AI leaders and laggards.

Personal readiness — individual skill, judgment, and experimentation habit — accounts for 25%.

Most organizations invest heavily in personal readiness (training, licenses, access) and underinvest in organizational readiness (workflow redesign, governance, measurement). This tool shows you whether that's what's happening here.

What the sprint targets

The 90-day sprint focuses exclusively on organizational changes — things that alter the conditions for everyone, not individual skill development. Each sprint action names:

· What specifically changes
· Who owns it
· What done looks like by day 90
· What you'll see in week 2 if it's working

The sprint targets the three lowest-scoring organizational dimensions — the bottlenecks most likely to be eroding the value of your personal readiness investment.

Honest scoring matters

The diagnostic is only useful if the inputs are accurate. Rate the organizational track on what actually exists, not what leadership has announced or intends. A written AI strategy that employees can't describe is a 1, not a 4.