The AI Enabled Organization

We have clearly defined the business outcomes we expect AI to help us achieve.
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We have identified where AI is strategically important to our organization and where it is not currently a priority.
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Our AI initiatives are connected to our broader business strategy rather than operating as a separate technology agenda.
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Our executive leaders communicate a consistent point of view about our AI priorities and direction.
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Executive Alignment Score:         / 8

Discussion Prompt:
If we asked each member of the executive team, “Why does AI matter to our business?” would we hear essentially the same answer?

Employees understand why our organization is investing time and resources in AI.
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Employees understand what leadership expects of them regarding AI use and experimentation.
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Leadership has directly addressed employee concerns about how AI could affect roles, responsibilities, and headcount.
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Leaders openly communicate what we know, what we do not yet know, and what decisions have not yet been made about AI.
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Leadership Communication Score:         / 8

Discussion Prompt:
What information are employees currently missing that they may be filling in themselves?

Leadership Risk:
In the absence of information, people tend to assume the worst. Around AI, that gap can quickly be filled by fear about job security, relevance, and the future of work.

Employees can experiment with approved AI tools without believing every attempt must produce a successful outcome.
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Employees feel safe showing leadership where AI could reduce manual work or increase efficiency in their own roles.
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Employees can openly discuss failed AI experiments and what they learned from them.
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Leaders reinforce that improving a workflow is valuable even when doing so changes how existing work gets done.
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Psychological Safety Score:         / 8

Discussion Prompt:
Would our employees willingly show us how AI could eliminate hours of their own current work?

Consider what the answer reveals about trust, communication, and employees' perceptions of leadership's intentions.

Our organization has a clearly communicated AI acceptable-use policy.
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Employees know which AI tools are approved and which are not.
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Employees understand what organizational, employee, and customer information can and cannot be entered into AI systems.
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We have clearly identified who has responsibility and decision authority for AI governance, tools, and emerging use cases.
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AI Governance Score:         / 8

Discussion Prompt:
Do our guardrails give employees enough clarity to move forward confidently?

Effective governance establishes boundaries people understand and can operate within.

Access permissions for our internal information reflect who should actually be able to retrieve that information in an AI-enabled environment.
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We routinely identify and remove or archive outdated policies, processes, strategies, and organizational documents.
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Employees can identify authoritative sources of truth for critical business information.
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We have evaluated how AI tools could surface sensitive, outdated, contradictory, or incorrectly permissioned information.
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Data Readiness Score:         / 8

Discussion Prompt:
If an AI system could instantly retrieve every document an employee technically has permission to access, what would concern us?

AI changes the consequences of poor information hygiene. Information that once remained effectively hidden because it was difficult to find can become instantly discoverable. Outdated information also becomes more consequential when AI uses it to create new work.

Teams evaluate where AI belongs within a workflow rather than simply adding AI to existing processes.
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Employees understand which types of tasks are well suited for AI assistance.
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Employees understand where AI is unlikely to add value or introduces unacceptable risk.
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We have intentionally identified where human review, judgment, accountability, or decision-making must remain in the workflow.
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Workflow Integration Score:         / 8

Discussion Prompt:
If we designed our most important workflows today, knowing what AI can do, would we design them the same way?

A useful executive question for every significant workflow is:
Where does AI improve this workflow, and where does human judgment create the greatest value?

Employees have a recurring way to share successful AI applications with colleagues outside their immediate teams.
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Lessons from unsuccessful AI experiments are shared rather than remaining isolated within a function or individual.
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We intentionally create opportunities for employees from different functions to solve problems or experiment with AI together.
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Useful AI practices discovered in one part of the organization are actively evaluated for application elsewhere.
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Cross-Functional Learning Score:         / 8

Discussion Prompt:
How quickly does one employee's AI breakthrough become knowledge the rest of the organization can use?

Consider adding ten minutes to an existing weekly or monthly meeting for employees to demonstrate one useful AI application, experiment, or lesson.

Employees receive AI training appropriate to their roles and starting skill levels.
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Employees have access to relevant examples, prompts, or use cases that help them move from theory to practical application.
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We provide ongoing opportunities to build AI capability rather than treating AI education as a one-time training event.
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We actively identify employees across functions who can become AI champions, regardless of title, seniority, or technical background.
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AI Enablement Score:         / 8

Discussion Prompt:
What is preventing employees who have not yet embraced AI from developing greater confidence and capability?

Your most valuable AI innovators may emerge from unexpected functions and levels of the organization. Training, opportunity, relevant use cases, and clear permission can significantly change an employee's willingness to engage.

AI experiments are connected to meaningful business problems or opportunities.
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We have criteria for determining which AI experiments should move forward, be scaled, or stop.
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We actively monitor unfinished AI initiatives rather than allowing experiments to accumulate without ownership.
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Increased AI capability has not caused us to abandon strategic prioritization in favor of pursuing too many ideas simultaneously.
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Experimentation Discipline Score:         / 8

Discussion Prompt:
How many AI projects are sitting at 70 or 80 percent complete inside our organization right now?

AI dramatically reduces the friction required to start something. Organizations still need the discipline required to finish, implement, maintain, and absorb what they create.

Our AI roadmap accounts for the amount of organizational change employees are already navigating.
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We distinguish between what AI makes technically possible and what our organization can realistically absorb.
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Leaders intentionally sequence AI-related changes rather than introducing every viable opportunity at once.
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We are willing to delay a valuable AI initiative when the organization does not currently have the capacity to absorb the change well.
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Change Capacity Score:         / 8

Discussion Prompt:
What are we capable of implementing that we should intentionally choose to delay?

Just because your organization can do ten things does not mean your people can absorb ten changes.

Executive Alignment         / 8

Leadership Communication         / 8

Psychological Safety         / 8

AI Governance         / 8

Data Readiness         / 8

Workflow Integration         / 8

Cross-Functional Learning         / 8

AI Enablement         / 8

Experimentation Discipline         / 8

Change Capacity         / 8

TOTAL         / 80

0–24 | Experimenting Without a Foundation

AI activity may already be occurring throughout the organization, while the leadership, governance, information, or change infrastructure needed to translate experimentation into sustainable enterprise value remains underdeveloped.

At this stage, organizations may experience isolated experiments, inconsistent practices, unclear expectations, employee uncertainty, and activity disconnected from business strategy.

Executive Priority

Establish the foundation before accelerating usage.

Start by examining your lowest-scoring dimensions and identifying which one presents the greatest organizational risk.

25–47 | Building the Operating Model

Important elements are emerging, but AI is likely being absorbed unevenly.

Some employees or functions may already be highly capable while others remain uncertain about how, when, or whether to use AI. Policies may exist without being fully understood. Experiments may be plentiful without a clear mechanism for scaling what works.

Executive Priority

Move from pockets of capability to organizational capability.

Identify the two or three dimensions where greater consistency would have the largest enterprise impact.

48–64 | Scaling With Discipline

Your organization has many of the conditions necessary for AI absorption.

The leadership challenge now shifts toward ensuring successful AI practices translate into better workflows, stronger decisions, appropriate governance, and measurable business value.

Increased capability can also produce a proliferation of ideas and initiatives that compete for organizational attention.

Executive Priority

Scale what works while maintaining strategic focus.

65–80 | Moving Toward AI Native

AI is becoming an organizational capability rather than a collection of tools or isolated experiments.

Employees increasingly understand where AI belongs in their work. Leaders have established direction and guardrails. Learning travels across the organization. Workflows are beginning to be designed with AI capabilities in mind.

As capabilities accelerate, executive leadership becomes increasingly important in determining priorities, sequencing change, maintaining accountability, and protecting the organization's capacity to execute.

Executive Priority

Protect strategic focus, human judgment, information integrity, and organizational capacity for change.

With your executive team, identify:

Your two highest-scoring dimensions

What can you leverage from these strengths?

Your two lowest-scoring dimensions

Which creates the greatest risk to your AI strategy?

Your Greatest Gap
Where is the largest difference between our AI ambition and our organizational readiness?

Your Hidden Strength
Where is AI already creating value that the rest of the organization may not know about?

Your Human Risk
What are employees likely afraid or reluctant to tell us about AI?

Why

Employees need to understand leadership's intentions before they can confidently experiment with AI.

A broad directive to “use AI” without context can increase uncertainty, particularly when employees are simultaneously hearing predictions about AI-related workforce reductions.

Practice

Give employees a clear narrative:

• Why AI matters to the organization

• What business outcomes you are pursuing

• What employees are expected to do

• What they are not expected to do

• What leadership knows

• What leadership does not yet know

• How AI relates to your workforce strategy

Executive Prompt
What information are employees currently missing that they may be filling in with fear?

Why

Employees need enough clarity to understand how to experiment responsibly.

Practice

• Clearly establish and communicate:

• Approved AI tools

• Acceptable-use policies

• Data restrictions

• Human-review requirements

• Decision-making ownership

• Escalation processes for uncertain situations

Executive Prompt
Do our employees know enough about the boundaries to move confidently within them?

Why

People often need to experience AI solving a meaningful problem before its potential becomes tangible.

Practice

Create a focused AI hackathon or experimentation session.

Choose meaningful business problems.

Build cross-functional teams.

Establish clear guardrails.

Give employees protected time to experiment.
Allow experimentation to produce learning even when it does not result in a production-ready solution.

The objective is to build capability, collaboration, confidence, and exposure to new ways of working.

Executive Prompt
What meaningful problem could a cross-functional team explore with AI in four hours?

Why

Individual AI capability becomes far more valuable when learning travels across the organization.

Practice

Create a recurring mechanism for sharing:

• Useful prompts

• Successful workflows

• Failed experiments

• New agents or automations

• Lessons learned

• Emerging risks

This does not necessarily require another major meeting. Ten minutes during an existing meeting may be enough to create valuable connections between employees and functions.

Executive Prompt
How quickly does one person's AI learning become organizational learning?

Why

AI capability can accelerate faster than human capacity for change.

Executive teams therefore face an important leadership responsibility: deciding which opportunities the organization should pursue, in what sequence, and at what pace.

Practice

Treat AI initiatives as a portfolio of organizational change.

Prioritize.

Sequence.

Stop low-value work.

Account for the cumulative amount of change employees are already experiencing.

Continue to distinguish the organization's ability to initiate work from its ability to successfully absorb and sustain change.

Executive Prompt
What should we deliberately choose to delay?