Change Navigators
Complimentary

Transformation Adoption Snapshot

Get a directional view of where one initiative may be positioned for adoption—and where greater attention may be warranted.

20 statementsDirectional—not assurance
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Five conditions

Assess what is demonstrated—not what is merely planned.

Use one active organizational change as the basis for your responses. Rate each statement from 1 (not demonstrated) to 5 (consistently demonstrated).

Leadership & Purpose01
Workflow & Role Clarity02
Workforce Trust & Readiness03
Enablement & Support04
Measurement & Sustainment05
Self-assessment

Rate the current condition.

1 = not demonstrated · 5 = consistently demonstrated

About this transformation

Tell us what you are changing, why the investment matters, and where you are in the journey.

These fields are required because the expected business result, technology/transformation, and stage shape how your results are interpreted and which next step is recommended.

AI adoption is built into the assessment. Each of the five dimensions includes a fourth AI-adoption question covering governance, workflow design, workforce trust, capability, and responsible-use measurement. You do not need to select AI as a separate transformation type.
01

Leadership & Purpose

Do leaders share a clear outcome, visible sponsorship, and decision discipline?

Leaders can clearly explain the business outcome this change is intended to improve, why it matters now, and how success will be recognized.
A visible sponsor or sponsor coalition actively reinforces the change, resolves barriers, makes timely decisions, and holds leaders accountable for follow-through.
Leadership expectations, decision ownership, and escalation paths are clear, consistently understood, and reinforced across all affected business areas.
Leaders have agreed where AI should improve work, what decisions remain human-owned, and what guardrails define responsible and acceptable use.
02

Workflow & Role Clarity

Is the future state clear enough for people to know how work and accountability will change?

Priority current-state and future-state workflows are documented well enough to identify how tasks, handoffs, approvals, and decisions will materially change.
Affected roles have clear future-state responsibilities, decision rights, required skills, and performance expectations—including what work will start, stop, or change.
Critical handoffs, dependencies, exceptions, data needs, and process risks have been identified and assigned before the implementation reaches key milestones.
Roles and workflows explicitly define where AI will assist, automate, or recommend—and where human review, judgment, and accountability are still required.
03

Workforce Trust & Readiness

Do employees understand the change, feel heard, and have credible local support?

Affected employees understand why the change is happening, what it means for their daily work, what is not changing, and what will be expected of them.
Leaders actively listen for concerns, resistance, missing voices, and readiness signals—and respond visibly enough for employees to see that feedback influences action.
Managers, supervisors, and trusted influencers are engaged early enough to prepare teams, surface local risks, and reinforce adoption where the work actually happens.
Employees understand how AI may affect roles, decisions, data use, performance expectations, and the safeguards being put in place to address legitimate concerns.
04

Enablement & Support

Are people equipped to perform differently—not simply informed or trained?

Learning and enablement are role-specific, practice-based, and aligned to the real tasks, decisions, scenarios, and behaviors people must perform differently.
Managers have the tools, talking points, practice, time, and escalation support needed to coach teams through resistance, uncertainty, and early performance issues.
Employees know exactly where to go for process, system, policy, or adoption support before and after go-live, including who owns each type of issue.
Users can practice AI-enabled workflows, evaluate the quality of AI outputs, recognize limitations, and know when to escalate, override, or seek human review.
05

Measurement & Sustainment

Will leaders know whether adoption is working and who owns it after implementation?

Success measures go beyond attendance and completion counts to track behavior, proficiency, utilization, adoption quality, and meaningful business outcomes over time.
Leaders review adoption evidence at defined intervals by role, function, or location and take corrective action when indicators show the organization is moving off track.
Business owners remain accountable after implementation for reinforcement, capability, issue resolution, sustained adoption, and the business outcomes the transformation was intended to deliver.
AI adoption measures include output quality, accuracy, appropriate use, human oversight, risk incidents, and the business impact of AI-enabled work—not just usage volume.