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AI Readiness Scorecard

AI adoption gets expensive when organizations move faster than their ability to choose good use cases, prepare their processes and people, manage risk, and measure whether the technology is actually creating value.

The AI Readiness Scorecard helps you assess the conditions your organization needs to move from experimentation to useful, responsible, and sustainable AI adoption.

It takes about 5–7 minutes. At the end, you’ll receive an overall readiness assessment, scores across six dimensions, your strongest area, your biggest constraint, and practical next steps.

AI Readiness Scorecard

Assess the organizational conditions needed to use AI effectively, responsibly, and sustainably.

"*" indicates required fields

Rate each statement based on how your organization operates today, not what is planned or what you hope will eventually be true.

1: Not in place
2: Mostly ad hoc
3: Partly established
4: Mostly established
5: Consistently established

There are no “good” or “bad” scores. The objective is to identify where your organization is prepared to move forward and where additional work may be needed before AI can scale successfully.

1. Business Purpose

AI should start with a business problem worth solving, not with the technology itself. These questions look at whether your organization has enough clarity to direct AI investment toward meaningful outcomes.

Clear Business Problems*
We can clearly identify the business problems or opportunities where AI could create meaningful value.
Defined Outcomes*
Our AI initiatives are connected to specific outcomes we want to improve, rather than simply increasing AI usage.
Prioritized Opportunities*
We have a practical way to compare AI opportunities based on potential value, feasibility, effort, and risk.

2. Process Readiness

AI can accelerate a good process—or make a bad one fail faster. These questions examine whether the work itself is understood well enough to automate, augment, or redesign.

Process Clarity*
Before introducing AI, we understand how the underlying work actually happens from beginning to end.
Process Health*
We identify unnecessary steps, rework, delays, and other process problems before deciding what AI should automate or augment.
Ownership & Decisions*
The processes being considered for AI have clear owners, responsibilities, decision points, and expected outcomes.

3. Data & Technology

AI depends on reliable information and an appropriate technical environment. Readiness does not require perfect data or a massive technology program, but organizations need to understand what their AI systems will rely on.

Information Availability*
We know what information our priority AI use cases need and where that information comes from.
Data Fitness*
We have reasonable confidence that important data is accurate, accessible, appropriately protected, and suitable for its intended AI use.
Technology Fit*
We evaluate whether AI tools fit our existing systems, security requirements, privacy obligations, and technical environment before adopting them.

4. People & Capability

Successful AI adoption depends on more than access to tools. People need the skills, judgment, expectations, and support required to use AI effectively in their actual work.

Practical AI Skills*
Employees receive training that helps them use AI effectively in the context of their actual roles and responsibilities.
Judgment & Boundaries*
People understand when AI can assist their work, when its output needs to be verified, and when human judgment must take precedence.
Manager Readiness*
Managers are prepared to help teams change workflows, responsibilities, and expectations as AI becomes part of the work.

5. Governance & Risk

Organizations do not need bureaucracy around every AI use. They do need enough governance to know where AI is being used, who is responsible, what risks matter, and when human oversight is required.

Rules for Use*
Employees have clear guidance about acceptable and unacceptable uses of AI, including sensitive information and externally provided AI tools.
Ownership & Visibility*
We can identify important AI systems or use cases in the organization and know who is accountable for them.
Risk & Human Oversight*
We have a proportionate way to evaluate AI risk and determine when review, approval, monitoring, or human oversight is required.

6. Adoption & Measurement

An AI initiative isn’t successful because it launches. Organizations need to know whether people are using it, whether the work improved, what was learned, and whether the investment should be expanded, changed, or stopped.

Test & Learn*
We introduce AI in manageable ways that allow us to test assumptions, learn, and adjust before scaling broadly.
Adoption Support*
When AI changes how people work, we deliberately address communication, workflow changes, training, leadership support, and adoption.
Evidence of Value*
We measure whether AI initiatives produce the intended business results and use that evidence to decide whether to scale, modify, or stop them.

A Little Context

These questions do not change your readiness score. They help put the results in context and make the recommendations more useful.

Where is your organization today?*
Choose the option that best describes your current stage.
What are you primarily hoping AI will help your organization accomplish?*
Choose up to three.
What is currently making AI adoption most difficult?*
Choose up to three.

Get Your AI Readiness Results

Enter your information to see your AI Readiness profile, including your overall readiness level, scores across all six dimensions, your strongest area, your biggest constraint, and practical next steps.

Name*
Share any context that would help us understand your goals, concerns, or current AI efforts.