AI Deep Dive: AI for Institutional Research and Decision Support

Primary Audience: IR directors and analysts, strategic planning and institutional effectiveness leaders, select cabinet members

Institutional research is being asked to become a different function than it was three years ago. Its own professional association now frames the moment as a structural inflection point — demographic contraction, financial stress, political scrutiny, and generative AI converging at once — and argues that analytics can no longer be primarily about compliance reporting and retrospective benchmarking. The expectation now is that IR anticipates the decisions leaders are about to face and supplies the analysis to inform them: a shift from a descriptive, backward-looking function to a strategic, integrative one. This session is about how AI serves that shift — not how it makes the old job faster.

The clearest example is the move the field is already naming: from single-point forecasting to scenario-based strategic foresight. When a president must weigh program mix, pricing, modality, or consolidation, a single projection is the wrong instrument; leaders need to see the trade-offs across plausible futures. AI can help build, populate, and narrate those scenarios at a speed IR offices couldn’t reach manually. But this is precisely where the multiplier principle bites: AI is a multiplier, not a fixer, and scenario work built on inconsistent definitions or unreconciled data doesn’t surface the truth — it manufactures a confident, well-written version of the wrong answer. And it bites hardest here because the output now feeds restructuring and program decisions. Drafting and summarizing sit at Tier 1; a scenario model that informs a cabinet or board decision is Tier 3 work, where reproducibility, an auditable trail, and the analyst’s judgment cannot be optional.

The session will demonstrate AI-assisted workflows for synthesizing across board reports, institutional plans, and effectiveness documents, and for turning analysis into cabinet- and board-ready narrative — with attention to a design principle worth carrying into any IR deployment: bounding the AI to the institution’s own authoritative documents rather than letting it draw on the open web, so that what it produces can be traced, defended, and trusted. This session is demonstration-driven — nothing to install. Participants will leave with a clearer sense of where AI genuinely extends IR capacity and where it would quietly put the office’s credibility at risk. Bring a sample dataset (anonymized enrollment, retention, or survey data in CSV) and/or a recent board report or institutional effectiveness document (PDF) if you’d like to think through the workflows against your own material.