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The Ecosystem: Weekly Strategic Signals for Decision-Makers Serving Colleges, Universities, and Systems.
Enrollment & Revenue: Common App’s 234-college direct admissions expansion pushes more of the enrollment battle downstream, from generating applications to converting students who are already admitted.
Policy & Regulation: Treasury’s new tax-exemption proposal could force colleges to revisit how race and ethnicity are used across admissions, aid, scholarships, analytics, and student-facing systems.
Tech & Infrastructure: Harvard’s AI push signals a shift from buying access to generative AI toward redesigning teaching, assessment, and faculty workflows around it.
Research & Partnerships: NSF’s NAIRR expansion and the Foundry School point to a more networked federal model, creating new vendor opportunities around shared infrastructure, consortia, and cross-institution delivery.
The Ecosystem is a weekly intelligence brief for decision-makers serving colleges, universities, and higher ed systems. We deliver high-impact developments shaping U.S. colleges and universities: what happened, why it matters, and what to do about it. It is designed for strategy, product, and GTM leaders at vendors serving higher education institutions. Each issue distills complex shifts into decision-grade insight.
1. Enrollment & Revenue
Common App direct admissions expansion shifts more of the enrollment fight to post-admit conversion
What Happened
Common App's direct admissions program will include 234 colleges in the 2026-27 cycle, up from 215 last year, with the first offers expected in mid-September. The expansion gives direct admissions its broadest institutional footprint yet, with participating colleges able to admit students who have not applied to them. As the fall recruitment cycle begins, those institutions will be competing to convert a larger pool of students whose first meaningful interaction with the college may come after an admission decision.
Why It Matters
For vendors serving enrollment teams, direct admissions changes the workflow and economics of the recruitment funnel. Institutions can acquire larger pools of admitted students who have demonstrated little or no prior interest, shifting more of the work from application generation toward identifying intent, communicating value, presenting affordability, and converting an offer into enrollment. As multiple colleges use the same platform to reach overlapping populations, historical signals such as inquiries, applications, and admit rates may also become less reliable inputs for forecasting yield and allocating recruitment spend.
Implications for You
CRM and enrollment-marketing platforms will need to support distinct journeys for direct admits who enter the funnel without traditional inquiry or application histories.
Yield and predictive-analytics vendors may need to recalibrate models as direct-admit populations weaken historical relationships between application behavior and enrollment intent.
Financial aid and net-price tools have an opportunity to move earlier in the conversion journey as institutions compete for students who already hold admission offers.
Enrollment-service providers should expect institutions to put greater emphasis on post-admit engagement, segmentation, and conversion rather than application-volume growth alone.
Vendors should be cautious about positioning higher application or admit counts as evidence of enrollment performance as institutions increasingly focus on net enrollment, yield, and revenue outcomes.
2. Policy & Regulation
New tax-exemption proposal expands compliance risk across institutional systems
What Happened
On September 3, the Treasury Department and IRS proposed regulations that would make private educational institutions ineligible for federal tax-exempt status if they maintain policies or practices that discriminate based on race, color, or national or ethnic origin. The proposal would apply across admissions, educational policies, scholarships and loans, athletics, and other institution-administered or supported programs. It would also eliminate existing IRS guidance permitting certain race-conscious practices in admissions, programs, scholarships, and financial assistance. Treasury estimates that as many as 18,000 private educational institutions could be affected. If finalized, the regulations would apply to taxable years beginning after May 31, 2027.
Why It Matters
For higher education vendors, the immediate issue is not the ultimate legal fate of the proposal but the compliance review it could trigger across institutional customers. Race and demographic information can sit inside admissions, financial aid, scholarship, CRM, student-success, advancement, analytics, and reporting systems. As institutions reassess which programs and decisions may create exposure, vendors could face requests to change eligibility rules, data fields, segmentation logic, reporting, permissions, and audit trails rather than simply update written policies.
Implications for You
Admissions, CRM, financial aid, and scholarship vendors should expect customers to scrutinize where race enters eligibility, targeting, prioritization, and decision workflows.
Data and analytics providers may face demand for greater visibility into how demographic variables influence models, dashboards, segmentation, and recommendations.
Vendors supporting configurable institutional workflows should make rules and decision logic easier to audit and modify as customers respond to changing legal interpretations.
Student-success and engagement platforms may need to distinguish between collecting demographic data for reporting and using it to determine access to programs or services.
GTM teams should expect compliance, legal, and procurement stakeholders to play a larger role in purchases involving sensitive student data or automated decision-making.
For Further Reading: U.S. Department of the Treasury
3. Technology & Infrastructure
Harvard’s AI push shifts the technology challenge from access to implementation
What Happened
On September 2, Harvard College Dean David Deming called for a more deliberate approach to generative AI in undergraduate education, arguing that faculty should encourage AI use where it improves learning while also designing assessments that require students to demonstrate knowledge independently. Deming characterized the ideas as a direction for further consultation rather than a new institutional policy, but the proposal points toward a model in which institutions distinguish between AI-enabled learning and environments where independent mastery must be verified.
Why It Matters
For higher education technology vendors, the next phase of institutional AI adoption is becoming less about providing access to models and more about integrating AI into teaching, assessment, and faculty workflows. If institutions increasingly differentiate between where AI should be encouraged and where its use should be constrained, LMS, assessment, academic-integrity, and AI providers will need to support more granular controls and workflows. The competitive question shifts from who can add an AI feature to who can help institutions operationalize different approaches across courses, instructors, and assessment types.
Implications for You
LMS and assessment providers should expect demand for more granular AI settings at the course, assignment, and assessment level rather than institution-wide controls alone.
Academic-integrity vendors may need to move beyond AI detection toward tools that help institutions verify student mastery and redesign assessment workflows.
AI platforms selling into higher education will need stronger faculty enablement and workflow integration as institutions move beyond standalone access.
Vendors that can support both AI-enabled learning and controlled assessment environments may have an advantage as institutions formalize more differentiated AI policies.
GTM teams should expect academic affairs, teaching centers, and faculty governance to become more influential stakeholders in AI technology decisions alongside CIO organizations.
For Further Reading: The Harvard Crimson
4. Research & Partnerships
What Happened
On September 1, NSF established the National Artificial Intelligence Research Resource Operations Center, led by the San Diego Supercomputer Center at UC San Diego in collaboration with the Texas Advanced Computing Center at UT Austin. The center will coordinate access to computing, data, models, software, training, and other resources across the NAIRR network as NSF transitions appropriate functions from the pilot into a sustained national capability. The move comes against a tighter conventional NSF funding backdrop. Nature reported in August that the agency was on track for roughly 6,100 new grants in FY2026, its lowest annual total in about four decades.
The federal government is also using multi-institution partnerships to build capacity in other strategic technology areas. On September 3, the State Department launched the Foundry School under its Pax Silica initiative, with Stanford developing advanced-manufacturing curriculum for delivery through eight participating universities focused on semiconductors, defense, energy, and related industries.
Why It Matters
For vendors serving university research, the route to market is shifting as federal resources become more concentrated around shared infrastructure and strategic technology initiatives. Tighter conventional grant volumes can constrain discretionary research spending, while initiatives such as NAIRR and the Foundry School concentrate resources around shared infrastructure and multi-institution networks. Vendors may increasingly need to support institutions participating in federally backed ecosystems rather than selling infrastructure or services campus by campus.
Implications for You
Research technology vendors should track NAIRR participation and federally backed consortia as potential channels for reaching multiple institutional customers.
Cloud, compute, data, and research-software providers may see greater demand for tools that connect institution-owned environments with shared national research infrastructure.
Vendors selling into research offices should expect tighter conventional grant funding to increase scrutiny of infrastructure costs and demonstrable research ROI.
Products that support interoperability, identity, data movement, governance, and collaboration across institutions could become more valuable as research infrastructure becomes more distributed.
GTM teams should map federal technology priorities alongside individual university budgets, as new purchasing opportunities may originate through consortia, research centers, and government-backed partnerships.
For Further Reading: National Science Foundation: NAIRR Operations Center
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