Leadership & strategy
Is there a shared purpose, ownership and sequence for AI adoption?
University AI Readiness
Assess readiness across the whole institution.
University AI readiness is not one score. It spans leadership, teaching, administration, research, information protection, infrastructure, approved tools, governance and people.
Readiness dimensions
Find the gaps before scaling
AFA reviews the institution as a connected system.
Students & teaching
Are approved uses, academic expectations and AI literacy clear?
Research & IP
Are unpublished findings, sponsored research, datasets and commercialization pathways protected?
Workplace AI
Are permissions and information ready for Copilot or other enterprise AI?
Sovereign architecture
Are sensitive workloads routed to appropriate private, hybrid or approved environments?
Governance & capability
Are policies, evaluation, training, escalation and ownership practical enough to operate?
Assessment
From institution-wide questions to prioritized action
The output should identify what can move now and what requires groundwork.
Discover
Interview key functions and review representative workloads.
Map
Document current tools, controls, information and responsibilities.
Prioritize
Identify near-term opportunities and high-risk gaps.
Sequence
Define workshops, policy, training, architecture or pilots in the right order.
Continue the journey
Related pathways
Research protectionGoverned adoptionFaculty and staff enablementPractical roadmap
The institutional decision
Enable AI without surrendering research value
The assessment distinguishes what can use approved public services, what needs additional controls and what should remain in institutionally governed environments.
Understand governance, data, infrastructure, people and current experimentation.
Identify opportunities in teaching, research, administration and student services.
Address confidential research, patent timing, sponsor restrictions and commercialization.
Define the first responsible pilot, owners, training and decision gates.
Request the next conversation
Share the workload, decision or outcome you need to clarify. A human will review the context.
Protect discovery while building institutional capability.
Begin with a clear picture of what the university should enable, govern, protect and build first.