AI Strategic Compass
A conversation-driven framework to screen, score, and select AI initiatives, then plan how success will be measured. Scores are a starting point for dialogue, not a verdict.
The decision engine of the framework, a structured, conversation-driven way to weigh candidate AI initiatives on the same six dimensions, including whether there's evidence it actually works, instead of approving them one at a time on instinct.
Turning a scattered list of AI ideas into a prioritized, governed portfolio, deciding what to pursue, at what level of scrutiny, and how you'll know it worked.
Scores start a conversation; they don't end one. A high score isn't automatic approval and a low score isn't a veto, both are prompts to discuss why.
The six dimensions you score on
Every initiative is rated 1–5 on each of these. Scoring all six (not a single gut-check) surfaces the trade-offs a good idea can hide: an exciting tool can be strong on innovation and weak on compliance, or impressive in a demo with no evidence it actually works.
Whether the initiative advances the institution's mission and current priorities, or is impressive but beside the point. Considers alignment with the strategic plan, the academic mission, and leadership goals.
What could go wrong for the people involved and for the institution. Considers bias and fairness, transparency and explainability, data privacy and security, and legal/regulatory compliance (FERPA, ADA, GDPR, and peers).
Whether the institution can afford it and whether the return justifies the spend. Considers up-front and ongoing cost, staff time and resources required, and short- and long-term ROI and sustainability.
Whether it genuinely improves how work gets done. Considers efficiency gains, added capacity, fit with existing workflows, and the degree of real innovation rather than novelty for its own sake.
Whether the AI actually works, distinct from whether it is fast (efficiency) or the right priority (alignment). Weighs the strength of evidence that the tool produces its intended outcome in a comparable context, and whether a rigorous validation plan exists before broader use. Unlike the other dimensions, a LOW score here is the warning sign: little or no evidence it works.
How the people affected will experience the initiative. Considers impact on students, staff, and faculty, equity for those most affected, and public trust and institutional reputation.
How success is measured
Scoring decides whether to pursue an initiative. Measurement decides whether it worked. The framework tracks five measures, three describe the initiative, two describe the institution.
Tangible deliverables, tools deployed, processes automated, people trained. Counts, completion, and adoption.
Mission-level shifts the outputs were meant to produce, retention, equity, satisfaction, research capacity.
Whether the AI itself performs as intended, accuracy, reliability, fairness, error and override rates, and user trust. An output can ship while its efficacy is poor.
How institutionalized the capability has become, on the five-level scale, from ad hoc experimentation to continuous optimization.
How prepared the institution is to activate and scale, leadership, culture, infrastructure, data, and people.
Proportionate review paths
The score doesn't just rank initiatives, it sets how much scrutiny each one needs. A faculty AI writing helper shouldn't clear the same bar as a system shaping financial-aid decisions. The path, the approver, and the requirements scale with the band.
- Self-certify against the AI acceptable-use checklist
- Name a responsible owner
- Standard monitoring; revisit at the annual review
- Short governance review before launch
- Document data sources, access, and retention
- Define success measures (KPIs / OKRs) up front
- Owner-led periodic check-ins
- Full governance-body review and sign-off
- Risk-mitigation plan with named controls
- Bias / equity and privacy impact assessment
- Stakeholder consultation (including those affected)
- Efficacy tracking, accuracy, error and override rates
- Ongoing monitoring against defined metrics
- Executive (cabinet) and governance-board approval
- Mandatory algorithmic impact assessment
- Legal, privacy, and compliance review
- Phased pilot with explicit scale-or-stop gates
- Continuous monitoring, audit trail, and incident response
- Documented rollback / contingency plan
- First ask: does a lower-risk alternative achieve the goal?
GRC Readiness Lens
Overlay each rubric dimension with Governance, Risk, and Compliance maturity. Rate your institution against the framework's Level 1 / 3 / 5 indicators to see where safeguards are strong and where the gaps are, an institutional self-assessment that complements the per-initiative scores above.