Governance Signal | Governance Must Scale—Why Institutional Capacity Challenges Governance
Governance Signals examine governance as institutional architecture. Each note isolates a specific governance condition affecting decision authority, accountability, or institutional capacity. Signals define the condition at the leadership level; In-Practice Briefs examine how institutions design systems to address it.
Leadership Question
Can governance systems operate at the same scale as institutional innovation?
Institutions have always managed complexity. What is changing is the speed, scope, and interconnectedness of institutional decision-making.
Artificial intelligence, advanced analytics, digital platforms, and automated workflows increasingly influence decisions across academic affairs, student success, research administration, finance, compliance, and operations. Individual decisions remain local. The systems that support them increasingly operate across institutional boundaries.
Governance now operates within larger and more interconnected decision environments.
Scale is the defining governance condition.
Governance structures were often developed around discrete functions, defined processes, and identifiable decision points. Contemporary decision environments increasingly span multiple units, systems, technologies, and oversight structures.
Data moves across organizational boundaries. Technologies influence decisions in multiple contexts. Operational choices made in one area increasingly affect outcomes in another.
Institutional innovation increasingly outpaces governance capacity.
Leadership teams remain accountable for institutional outcomes while decision environments become more complex, interconnected, and difficult to oversee through traditional governance mechanisms.
Institutions possess governance structures. Their effectiveness increasingly depends on whether they can operate across institutional scale and complexity.
Governance Evidence
A common pattern is emerging across higher education. Technologies scale through existing institutional functions, while governance responsibilities increasingly span the institution as a whole.
Accreditors are beginning to formalize expectations for institution-wide oversight of artificial intelligence. In 2025, the Middle States Commission on Higher Education adopted accreditation policies and procedures addressing institutional use of AI, signaling that governance responsibility extends beyond individual use cases and into institutional accountability.[i]
Higher education systems are responding in similar ways. In 2025, the California State University system announced one of the largest higher education AI initiatives in the United States, expanding access to AI tools and capabilities across its 23 campuses. Efforts of this scale illustrate how technology adoption increasingly operates across academic affairs, student support, administration, workforce development, and institutional operations simultaneously.[ii]
Peer institutions are confronting comparable challenges. EDUCAUSE research and convenings increasingly focus on AI adoption across the institution, reflecting the reality that technology-enabled decisions now affect multiple institutional functions at the same time.[iii]
Organizations across sectors are also investing in governance capability. In 2026, the U.S. Marine Corps required completion of foundational AI literacy training across its workforce, recognizing that effective oversight depends upon organizational capacity rather than isolated technical expertise.[iv]
Taken together, these developments point to a common governance condition. As technology-enabled decisions become more integrated into institutional operations, governance responsibilities increasingly extend across organizational boundaries, decision environments, and oversight structures.
Governance requires accountability, coordination, and visibility at institutional scale.
Leadership Implication
Scale is a governance condition.
Institutional leaders must ensure that governance capacity expands alongside institutional complexity. Governance systems that function effectively for isolated decisions may not provide sufficient oversight when technologies operate across multiple units, systems, and decision environments.
Responsible innovation depends on governance systems capable of operating at the scale institutional innovation requires.
Governance must scale.
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Sources & Citations
[i] Middle States Commission on Higher Education (MSCHE), Use of Artificial Intelligence Accreditation Policy and Procedures (effective July 1, 2025) (establishing expectations for institutional governance, oversight, and accountability related to the use of artificial intelligence within accredited institutions), available at https://www.msche.org/policies-guidelines/use-of-artificial-intelligence-policy/. See also Middle States Commission on Higher Education (MSCHE), New Use of Artificial Intelligence Accreditation Policy and Procedures, July 2, 2025, available at https://www.msche.org/2025/07/02/new-use-of-artificial-intelligence-accreditation-policy-and-procedures/.
[ii] California State University, Artificial Intelligence Workforce Acceleration Initiative (2025) (systemwide initiative expanding access to artificial intelligence tools, training, and workforce development opportunities across 23 campuses), available at https://www.calstate.edu/csu-system/news/Pages/CSU-Launches-Innovative-AI-Initiative.aspx.
[iii] EDUCAUSE, 2025 EDUCAUSE AI Landscape Study (examining institution-wide adoption of artificial intelligence and associated governance, policy, and operational challenges across higher education), available at https://library.educause.edu/resources/2025/2/2025-educause-ai-landscape-study. See also EDUCAUSE, AI Across the Institution Summit, available at https://events.educause.edu/educause-summit/2025.
[iv] U.S. Marine Corps, MARADMIN 214/26, Mandatory Completion of the Basic Artificial Intelligence Course (May 2026) (requiring foundational artificial intelligence literacy across the workforce to support organizational readiness and responsible adoption), available at https://www.marines.mil/News/Messages/Messages-Display/Article/4177540/mandatory-completion-of-the-basic-artificial-intelligence-course/.
TL Advisory references independent academic and policy research for contextual illustration; findings cited here have not been independently verified. This publication reflects the professional judgment and authorship of TL Advisory. All analysis and interpretation are the product of human expertise, supported by structured editorial review.