Know honestly where you stand, and build the capability to go further.
Aligns withNIST · MapISO/IEC 42001
The idea
Readiness is the institution's capacity to adopt AI responsibly across leadership, culture, infrastructure, data, skills, and resources; this pillar concerns assessing that capacity rather than assuming it. It is the diagnostic pillar, intended to prevent the launch of an ambitious initiative on a foundation that cannot support it.
Because a general expectation of readiness is difficult to act on, this pillar is operationalized through specific instruments: a maturity assessment scored against defined levels across the eight pillars; a gap analysis that converts low scores into prioritized work; and a readiness baseline that sequences initiatives according to capacity. The objective is not the score itself but a shared, evidence-based assessment of the institution's current state, so that ambition and capability are aligned.
Readiness is not only diagnostic; it is where the institution builds the human-capability side of that capacity. Running through this pillar is the Campus AI Proficiency Continuum, which describes how students, faculty, and staff progress from foundational AI literacy to role-specific competency and, where appropriate, to fluency in integrating AI into their teaching, research, operations, and community engagement. This anchors the institution's AI literacy programs and role-based competency so that professional learning and curriculum integration intentionally build the human capabilities trustworthy, mission-aligned AI use requires.
Proficiency development is stewarded with units such as the Center for Teaching & Learning, Information Technology, the Library, Human Resources, and Student Affairs, each contributing specialized expertise and targeted development pathways, and it is put to work through the participation channels and communities of practice of AI Engagement & Collaboration (Pillar 5). This keeps capability-building a coordinated institutional priority rather than an isolated initiative.
Why it matters
Readiness mismatch is a top cause of failed AI initiatives: the strategy outruns the foundation, and a high-profile pilot fails not because the idea was wrong but because the data, skills, or governance weren't there to hold it. Assessing readiness converts vague anxiety ('are we behind?') into a concrete, fundable plan, and gives leadership a defensible basis for pacing.
AI proficiency & enablement
“Role-based competency” only becomes real when you can name the actual programs. Building capability has two axes: WHO you're building it for, and HOW you do it. This is where the institution turns a readiness gap into people who can actually use AI well — and it is put to work through the participation channels and communities of practice of AI Engagement & Collaboration (Pillar 5).
The core capability this pillar builds
The Campus AI Proficiency Continuum
AI proficiency is the demonstrated ability to use AI in practice across a continuum, from foundational literacy (understanding and critical evaluation) through role-specific competency (responsible application) to advanced fluency (creative, adaptive integration and collaboration with AI systems).
FocusDescribe and support how students, faculty, and staff progress from foundational AI literacy to role-specific competency and, where appropriate, to fluency in integrating AI into teaching, research, operations, and community engagement.
The three levels of the continuum, the bar rises with responsibility
1
AI Literacy
Everyone
Foundational understanding of what AI is, how it works at a high level, its strengths and limitations, and how to critically evaluate its use and societal impacts.
2
AI Competency
People who use AI in their role
Consistent, responsible application of AI tools in one's role, aligned with institutional policies, ethics, data governance, risk management, and best practices.
3
AI Fluency
People who decide and govern
Creative and strategic integration of AI into workflows, including designing new AI-enabled practices, evaluating outcomes, and helping others use AI effectively.
The bar rises with responsibility. A governance body fluent in neither the technology nor its consequences is itself a risk: it will either over-restrict out of fear or wave through harm it didn't see. Match the level to the role, and make sure the people governing AI reach fluency, not just literacy.
Literacy is built, not assumed. These are short, interactive explainers that make the mechanics of AI concrete, the kind of thing to hand someone who wants to understand how the tools actually work, not just how to prompt them. A growing set; each opens in a new tab.
This is where the pillar stops being a principle and becomes work. Each practice below is something you can build, assign, and evidence, with a concrete first move, an owner, and what proof looks like. Move one practice up one level at a time.
1
Readiness assessment cadence
Recurring, evidence-based assessment of people, process, data, and technology capability.
1There is nowhere safe to experiment, so people use personal accounts with institutional data.
3Institutionally provisioned platforms and sandbox environments exist for staff, faculty, and students.
5An annual enablement adequacy review checks whether platforms, tools, and training meet demand.
Next moves
Provision a sanctioned AI platform tier with institutional data protections.
Open sandbox environments for low-stakes experimentation by role.
Review enablement adequacy annually, is supply meeting demand?
Who owns it
CIO
Evidence it exists
Platform availability by role
Sandbox usage data
Annual adequacy review
In practice
Worked example
Ambition paced to capacity
A college announces an AI tutoring rollout across every gateway course. A readiness assessment under this pillar reveals the blockers before launch: inconsistent data quality, no faculty development capacity, and unsettled privacy review. Rather than fail publicly, leadership sequences the work (fix data and privacy first, build faculty support second, pilot in two courses third) and re-scores in two terms. The rollout still happens, later but on a foundation that holds, and the maturity score becomes the story leadership tells the board.
Watch for
Self-assessment inflation, scoring aspirations instead of evidence.
Assessing once and shelving it, so it never drives the work.
Treating readiness as a gate that blocks rather than a map that sequences.
Treating literacy as a one-time event rather than an ongoing, role-specific capability.
Readiness & maturity questions
Readiness asks: are we prepared? Use these to surface blind spots before you build, honest “no” answers are where the work is. Representative prompts for reflection, not a scored test \u2014 for the scored version, use the Maturity Assessment.
Institutional readiness
Has AI readiness been assessed at the campus level?
Is there a framework used to measure readiness?
Are infrastructure and data pipelines AI-ready?
Do departments vary widely in their AI readiness?
Is faculty development supporting AI adoption?
Is staff training aligned with AI needs?
Is cultural readiness part of planning?
Are readiness findings shared with leadership?
Do AI project approvals consider readiness?
Is there an equity lens to assess AI gaps?
AI Literacy & Competency
Is AI literacy recognized as a campus-wide priority?
Have learning outcomes for AI citizenship been defined?
Are faculty and staff offered AI literacy training?
Is there a shared definition of ethical and responsible AI use?
Are students aware of AI's opportunities and risks?
Are learning modules or workshops available?
Is digital literacy integrated with AI ethics?
Are culturally relevant approaches considered in training?
Are literacy needs assessed across different groups?
Are incentives or requirements tied to AI literacy participation?