Appendix BInstitutional Readiness Assessment

An institution may possess ambitious goals, strong technical capabilities, or isolated examples of innovation without being prepared to undertake broad transformation. Conversely, a university at an early stage of AI adoption may be well positioned to advance because its mission is clear, its leadership is aligned, its governance is credible, and its community is prepared to learn.

Readiness asks whether the university possesses the leadership, trust, knowledge, infrastructure, professional capacity, and organizational discipline required to move from experimentation toward sustained institutional change. It also helps leaders distinguish between opportunities that can be pursued immediately and those that require additional preparation.

The assessment is organized around eight areas of readiness:

1. Mission and Strategic Purpose

2. Leadership and Institutional Alignment

3. Faculty Readiness

4. Staff and Workforce Readiness

5. Student Readiness

6. Data, Technology, and Institutional Knowledge

7. Governance, Ethics, and Risk

8. Capacity for Implementation and Continuous Learning

Each area includes a series of statements that institutional teams can evaluate using a four-point scale:

• 1 — Not Yet Established: The condition is absent, informal, or understood only by a small number of individuals.

• 2 — Emerging: Initial work has begun, but practices remain inconsistent or limited in reach.

• 3 — Established: The condition is operating across meaningful parts of the institution and is supported by defined responsibility.

• 4 — Institutionally Ready: The condition is mature, broadly understood, measured, and capable of supporting expanded AI adoption.

The numerical score is useful only as a starting point. A president may believe strategic direction is clear while faculty perceive uncertainty. Technology leaders may consider infrastructure ready while operational teams identify unresolved workflow challenges. Students may experience institutional policy differently from the way it is described in official documents.

For that reason, the readiness assessment should be completed by a cross-functional group rather than by a single executive or department. The group should include academic leadership, faculty, students, staff, research representatives, technology leaders, privacy and security professionals, legal or compliance counsel, accessibility experts, and individuals responsible for student services and institutional operations.

1. Mission and Strategic Purpose

Artificial intelligence should enter the university through mission rather than novelty. The strongest institutions can explain why artificial intelligence matters without referring first to efficiency, competition, or technological inevitability. They connect AI to questions the university already considers important: how to improve learning, expand access, support faculty, strengthen research, serve more diverse learners, reduce unnecessary complexity, and build institutional resilience.

Evaluate the following statements:

Interpreting This Area

A low score in this area suggests that AI activity is being shaped primarily by vendor offerings, local enthusiasm, competitive pressure, or fear of falling behind. Such institutions should resist broad expansion until they can explain what problems they are attempting to solve and why those problems matter to mission.

A high score indicates that the university has developed a shared sense of purpose strong enough to guide difficult choices. This does not require agreement on every implementation decision. It requires sufficient clarity that proposed initiatives can be evaluated against common institutional priorities.

2. Leadership and Institutional Alignment

AI transformation crosses traditional organizational boundaries. Teaching involves faculty and academic leadership. Student success involves advising, student affairs, financial aid, faculty, and technology. Research requires coordination among scholars, libraries, compliance offices, data services, and external partners. Administrative transformation affects nearly every department.

No single office can lead this work independently. The president and governing board provide direction and accountability. The provost protects the integrity of teaching, curriculum, and scholarship. The CIO or equivalent technology leader develops the institutional architecture. Faculty governance shapes academic policy. Operational leaders redesign services. Students contribute direct knowledge of the educational experience. Readiness depends upon alignment among these groups.

Evaluate the following statements:

Interpreting This Area

A low score often reveals that AI is being treated primarily as an IT project or that multiple groups are working independently without shared responsibility. The institution may generate promising pilots, but broad transformation will remain fragile because decisions lack durable ownership.

A high score does not mean that leadership speaks with one voice on every question. Universities benefit from disagreement and shared governance. It means that the university knows who is responsible for which decisions and possesses a trusted process for reaching conclusions.

3. Faculty Readiness

Faculty readiness is not measured by the percentage of professors using generative AI. Adoption alone reveals little about educational quality, disciplinary appropriateness, or institutional trust. Faculty are ready when they possess sufficient understanding, time, support, and authority to determine how AI should influence teaching and scholarship within their disciplines.

Some faculty will become early adopters. Others will proceed cautiously. Both perspectives can contribute to a stronger institutional strategy. Enthusiasm without academic judgment produces shallow innovation, while skepticism without experimentation may prevent the university from understanding important new possibilities. Readiness requires an environment in which faculty can investigate AI seriously rather than merely respond to it.

Evaluate the following statements:

Interpreting This Area

A low score may coexist with high levels of informal AI use. Faculty and students may already be experimenting, but without sufficient guidance, support, or common expectations. Institutions in this position should prioritize academic leadership and professional development before investing heavily in large-scale learning applications.

A high score indicates that faculty are being treated as designers of the AI-native learning environment rather than as end users of a technology chosen elsewhere. This condition is essential because no institutional strategy can succeed if it is disconnected from the people responsible for the academic mission.

4. Staff and Workforce Readiness

Staff members often experience AI transformation through the possibility of automation. Their questions are therefore practical and personal. Which responsibilities will change? What new skills will be required? Will professional judgment continue to matter? Will technology reduce workload or simply create additional expectations?

Universities should answer these questions honestly. Reassurance without evidence will not build trust, and technical training alone will not prepare employees for changing roles. Staff readiness depends upon whether people understand the purpose of transformation and have meaningful opportunities to shape how their own work is redesigned.

Evaluate the following statements:

Interpreting This Area

A low score suggests that AI may be perceived as something being done to employees rather than developed with them. This condition creates resistance, limits practical insight, and increases the likelihood that new systems will reproduce inefficient processes instead of improving them.

A high score indicates that staff expertise is informing institutional design. Employees understand that the objective is not simply faster administration, but a shift toward work requiring stronger judgment, relationships, creativity, and service.

5. Student Readiness

Students are not a uniform population. Their experience with artificial intelligence varies according to age, discipline, geography, previous education, disability, access to technology, and professional background. Some arrive with extensive practical experience. Others have used AI primarily to generate text without understanding how the systems work or where they fail.

A university cannot assume that regular use constitutes literacy. Student readiness means that learners understand how to use AI responsibly, critically, and transparently. It also means that they understand how the institution uses AI in services and decisions that affect their education.

Evaluate the following statements:

Interpreting This Area

A low score indicates that students may be using AI extensively while receiving little structured preparation. Inconsistency can create confusion, inequity, and academic integrity disputes. Institutions should establish clear educational expectations before assuming that students are prepared for deeper AI-supported experiences.

A high score means that students are treated as informed participants rather than passive recipients. They understand both their responsibilities and the institution’s responsibilities, and they possess the critical judgment required to work with AI beyond graduation.

6. Data, Technology, and Institutional Knowledge

Universities often possess strong enterprise systems but fragmented knowledge. Policies may exist in multiple versions. Procedures may be stored across websites, shared drives, and individual documents. Identity and permission structures may not correspond cleanly to how people actually work. Integrations may have been designed for transactional data rather than intelligent services.

Before expanding AI, institutions must understand whether their knowledge, data, and architecture are ready to support it.

Evaluate the following statements:

Business continuity and incident response plans include AI-supported systems.

Interpreting This Area

A low score suggests that the institution may be ready for limited experimentation but not for high-impact or cross-functional AI services. Deploying intelligent systems on top of unreliable knowledge or weak permissions can magnify existing problems while making them harder to detect.

A high score indicates that the university possesses a sufficiently governed technical and knowledge foundation to support integration, orchestration, and institutional memory. It does not require replacing existing systems. It requires connecting and governing them coherently.

7. Governance, Ethics, and Risk

Universities need governance capable of evaluating different levels of risk, assigning responsibility, enforcing human oversight, reviewing outcomes, and responding when systems fail.

Not every AI use presents the same institutional consequences. A faculty member generating discussion questions creates a different risk from an admissions system recommending applicants for additional review. A tool summarizing a public report differs from one accessing student records. Governance should reflect these differences without becoming so burdensome that responsible experimentation becomes impossible.

Evaluate the following statements:

The institution can suspend or retire an AI system when evidence indicates harm, unreliability, or misalignment with mission.

Errors, incidents, and complaints can be reported and investigated through established procedures.

Governance decisions are documented so that institutional learning accumulates over time.

Interpreting This Area

A low score indicates that the university may have principles but lacks operational governance. High-impact applications should not advance until authority, review, accountability, and escalation procedures are sufficiently clear.

A high score means that governance is capable of enabling responsible innovation. Review requirements correspond to risk, accountability remains visible, and the university can learn from both success and failure.

8. Capacity for Implementation and Continuous Learning

Universities frequently underestimate the work required after a pilot demonstrates promise. Scaling an initiative requires technical support, process redesign, professional development, communication, evaluation, funding, and ongoing ownership. Without these capabilities, promising experiments remain permanently experimental or are expanded before the institution can support them reliably.

Readiness therefore depends upon implementation discipline. The university must be able to select priorities, define outcomes, operate pilots, learn from evidence, and make deliberate decisions about whether to expand, revise, or discontinue an initiative.

Evaluate the following statements:

Interpreting This Area

A low score suggests that the university may be capable of experimentation but not sustained transformation. Leaders should strengthen project ownership, evaluation, support, and institutional learning before increasing the number or scale of initiatives.

A high score indicates that the institution can translate ambition into durable practice. It knows how to learn from pilots, scale responsibly, and discontinue work that does not produce sufficient value.

Readiness Profile

After completing the assessment, calculate an average score for each of the eight areas. The objective is not to produce one overall institutional number. A single score can conceal the imbalances that matter most.

Use the following ranges to interpret each area:

The resulting profile may be recorded as follows:

Readiness Patterns

The assessment becomes most useful when leaders examine patterns rather than isolated scores. Several recurring patterns deserve particular attention.

Strong Innovation, Weak Governance

Some universities possess energetic faculty, ambitious technology teams, and numerous pilots but limited governance. These institutions may appear advanced because AI activity is visible throughout the campus. In reality, expansion may be creating unmanaged risk, inconsistent student experiences, and fragmented procurement.

The priority should not be suppressing innovation. It should be building a governance structure capable of supporting it responsibly.

Strong Technology, Limited Academic Alignment

An institution may possess secure platforms, well-developed integrations, and capable technology staff while faculty participation remains limited. This pattern often emerges when AI is approached primarily as infrastructure.

The priority should be academic leadership, faculty development, and identifying educational purposes that justify the technology.

Strong Strategy, Weak Implementation Capacity

Some institutions articulate impressive visions but lack the staffing, funding, ownership, or project discipline required to turn those visions into practice. The strategy may be sound while execution remains distributed and under-resourced.

The priority should be narrowing institutional focus, assigning accountable owners, and developing repeatable implementation methods.

Strong Local Practice, Weak Institutional Coherence

Individual colleges, departments, or offices may demonstrate mature AI use while the university as a whole remains fragmented. These local successes are valuable sources of learning but can create duplicated systems and inconsistent governance when expanded independently.

The priority should be converting local expertise into shared institutional capability without extinguishing the creativity that produced it.

Strong Compliance, Limited Trust

An institution may have comprehensive policies and technical controls while faculty, staff, or students remain uncertain about purpose, fairness, or professional implications. Formal compliance does not necessarily produce confidence.

The priority should be transparency, participation, communication, and evidence that AI is improving the lived experience of the university community.

High Enthusiasm, Limited Knowledge Foundations

A university may possess strong demand for AI assistants while policies, documents, and institutional data remain outdated or contradictory. Intelligent systems built upon unreliable knowledge will produce unreliable support with greater speed and confidence.

The priority should be organizing institutional knowledge before expanding access to it.

Determining Institutional Priorities

After reviewing the readiness profile, the institution should identify no more than three major priorities for the next planning period. Attempting to improve every area simultaneously often disperses leadership attention and produces superficial progress.

A useful priority should satisfy four conditions. It should address a significant readiness gap, support an explicit institutional objective, have a clearly accountable owner, and be achievable within a defined period.

Examples may include:

• Establishing a standing AI governance body with defined decision authority.

• Developing institution-wide faculty and staff professional learning.

• Organizing authoritative policies and procedures into a governed knowledge system.

• Selecting a secure institutional AI platform with role-based access.

• Introducing AI literacy across the student experience.

• Redesigning one cross-functional student service rather than automating isolated departmental tasks.

• Creating evaluation standards for educational and administrative AI use cases.

• Clarifying executive ownership and institutional strategy.

• Developing a transparent workforce transition and professional development plan.

• Establishing consistent student disclosure and academic integrity expectations.

The priorities selected will differ according to mission and maturity. A small liberal arts college may focus first upon faculty-led learning design and community trust. A large research university may prioritize secure scholarly infrastructure and research governance. A community college may emphasize advising, accessibility, adult learners, and student support. Readiness does not require uniformity. It requires coherence between institutional purpose and institutional action.

Final Readiness Questions

Before approving a major expansion of artificial intelligence, institutional leaders should be able to answer the following questions with clarity:

1. What institutional purpose does this initiative serve?

2. Who is accountable for its outcomes?

3. Which people and communities have participated in its design?

4. What information will the system access, and why is that access necessary?

5. What decisions may it support, and which decisions must remain human?

6. How will privacy, security, accessibility, transparency, and fairness be protected?

7. What evidence will demonstrate that the initiative is working?

8. What will happen when the system is wrong?

9. How can users reach a qualified person when artificial intelligence is insufficient?

10. How will the institution preserve what it learns from implementation?

11. Can the initiative be sustained financially and operationally after the pilot ends?

12. Does the project make the university more faithful to its mission?

If an institution cannot answer these questions, the appropriate response is to recognize that additional preparation is required.

The institution prepared to become AI-native is the institution that possesses enough clarity of purpose, strength of governance, technical coherence, professional capacity, and community trust to learn responsibly while uncertainty remains.