How the Index is Used
The AI Capacity Index is introduced in Phase 2 of the Transformation Roadmap and measured throughout the engagement:
The AI Capacity Index is introduced in Phase 2 of the Transformation Roadmap and measured throughout the engagement:
The AI Capacity Index answers questions leadership actually asks:
These aren't abstract metrics. They're the evidence you need to demonstrate progress, secure continued investment, and build confidence that transformation is real and lasting.
Most AI transformation efforts measure activity: How many people attended training? How many licenses were activated? How many workshops were delivered?
These metrics are necessary but insufficient. They tell you what happened, not what changed.
The AI Capacity Index is Eduvators' proprietary measurement framework designed to answer the questions that actually matter: Is transformation taking hold? Are people using AI effectively? Is it creating real value? Will it last?
The Index tracks transformation across seven weighted dimensions, providing objective evidence of progress, early identification of areas requiring attention, and benchmarking against peer institutions in the Eduvators Network.
Are policies in place and embedded in practice?
This dimension measures whether AI governance exists not just on paper but in daily operations. It tracks policy coverage, risk classification implementation, Playbook adoption, and coordination efficiency across units.
Are proven practices spreading and improving outcomes?
This dimension distinguishes transformation from isolated projects. It measures whether Catalyst Projects scale beyond original teams, whether AI-enabled practices become standard operations, and whether stakeholder outcomes improve. This is where mission ROI lives.
Do people have the skills to use AI effectively and responsibly?
This dimension measures actual competency, not just training completion. It tracks skill levels, confidence ratings, certification achievement, and the depth of AI literacy across roles.
Will transformation continue without external support?
This dimension measures institutional capacity for ongoing innovation. It tracks whether the contributor community expands beyond original champions, whether the innovation pipeline generates new initiatives, and whether dependence on external support decreases over time.
Are people actually using the tools?
This dimension measures real usage, not just license provisioning. It tracks activation rates, frequency of use, Playbook implementation, and the breadth of adoption across the institution.
Is AI being used in alignment with institutional values?
This dimension goes beyond foundational concerns like privacy, bias, and transparency. It measures alignment with Eduvators' expanded Responsible AI framework: accessibility and digital equity, environmental sustainability, intellectual property and open access, cognitive sovereignty and human agency, labor and workforce impact, transparency and explainability, and accountability and human oversight.
Is AI creating capacity for high-value work?
This is where efficiency ROI lives. This dimension measures hours saved, tasks streamlined, bottlenecks resolved, and time redirected to mission-critical work. The formula is straightforward: hours recovered multiplied by cost equals dollars saved.
Phase 2: Baseline established for Governance, Capability, Adoption, and Responsible AI Use
Phase 3: Capacity Created measured as Catalyst Projects deliver results; Scalable Impact baseline established
Phase 4: All seven dimensions measured as practices spread institution-wide; Sustainability tracking begins
Phase 5: Annual measurement as part of Transformation Reviews; benchmarking against Eduvators Network peers
The Index provides a common language for discussing transformation progress with leadership, boards, and stakeholders. It identifies where attention is needed before small gaps become large problems. And it demonstrates ROI in terms that resonate with decision-makers.
As the Eduvators Network grows, the AI Capacity Index enables benchmarking against peer institutions. You can see how your transformation compares to similar organizations, identify practices that work well elsewhere, and contribute your own innovations to the broader community.
This network effect means every institution benefits from the collective learning of all institutions in the Eduvators community.
Are we getting value from our AI investments?
How do we compare to peer institutions?
Where should we focus attention next?
Is this transformation sustainable, or will it fade when champions move on?
Are we using AI responsibly and in alignment with our values?
A discovery conversation is the first step toward understanding where your institution stands today
and what transformation could look like.