A
Assess
Understand your institutional context
R
Redesign
Map workflows with AI as a partner
I
Implement
Roll out with governance and ethics
A
Adapt
Build feedback loops and evolve
A
Assess
Where is your institution, really?
8:00 – 20:00
Why this matters: The most common mistake in AI adoption is skipping assessment. Before any implementation, you need an honest picture of your institutional readiness across four dimensions. This is not a technical audit — it is a leadership question.
Exercise 1
Institutional Readiness Self-Assessment
Rate your institution on each of the four readiness dimensions (1 = very low, 10 = very high). Be honest — this exercise only works if you resist the urge to score high.
Move sliders to generate your readiness profile
R
Redesign
Map one workflow through an AI lens
20:00 – 32:00
The question is not: what AI tools exist? The question is: what do our people need to do their best work — and how might AI reshape the conditions for that? Tool-first thinking leads to adoption for adoption's sake. Need-first thinking leads to transformation.
Exercise 2
Workflow Mapping
Choose one high-friction process in your institution. Decompose it into tasks, then identify which are genuinely human, which AI could handle, and where they intersect.
Current Tasks
Must Stay Human
AI Integration Points
Automate = routine tasks with clear rules · Augment = AI informs, human decides · Accelerate = AI drafts, human refines
Fill in at least one column to mark complete
I
Implement
Governance, ethics, and responsible rollout
32:00 – 44:00
Most AI projects don't fail because the technology doesn't work. They fail because the implementation was not designed. No governance structure. No training. No communication plan. No accountability. This exercise builds the minimum viable governance foundation.
Exercise 3
Five Governance Questions Checklist
For the AI initiative you mapped in Exercise 2, tick each governance question you can currently answer with confidence. This shows you where the gaps are.
0 / 5 addressed
Who has authority to approve or restrict AI use?
Is there a named role or committee? Or is this currently informal/unclear?
How are AI-assisted decisions documented and reviewable?
Can you trace why AI recommended or flagged something? Is there an audit trail?
What is the escalation path when AI produces harmful outputs?
Who gets notified? What powers do they have to pause or correct the system?
How is student and staff data protected in AI workflows?
GDPR / local data law compliance? Clear data retention and access policies?
How will the institution communicate its AI practices to its community?
Do students and staff know when AI is involved in decisions about them?
Check at least one item to continue
A
Adapt
Feedback loops and long-term strategy
44:00 – 54:00
The Adapt phase is where most institutions stop short. They implement something, declare success, and move on. But AI systems — and the contexts they operate in — change. Adaptation is not optional. It is how you sustain the gains you worked to create.
Exercise 4
Strategic Reflection
This is a thinking exercise, not a planning template. Write freely — the goal is to connect the ARIA phases to your real institutional context.
↺ Feedback Mechanism
◎ Strategic Question
→ Realistic Next Step
✦ What Must Stay Human
Write in at least one reflection field to continue
Your ARIA Profile
A snapshot of your thinking — based on what you have entered above. This updates in real time.
A
ASSESS — Priority Area
Complete Exercise 1 to see your priority area here.
R
REDESIGN — Process
Complete Exercise 2 to see your mapped process here.
I
IMPLEMENT — Governance Gap
Complete Exercise 3 to see your governance priority here.
A
ADAPT — Next Step
Complete Exercise 4 to see your next step here.