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Universities Were Built for Autonomy. Now They Must Coordinate Change.
Higher education is experimenting with AI and new technologies, but the deeper challenge is structural: a distributed operating model must now coordinate continuous change.
EDT&Partners
Digital Transformation
AI in Education
Schools & Universities
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In this article
The University Operating Model We Inherited
Signals That Education’s Operating Model Is Under Strain
How AI and Technology Are Exposing Education’s Operating Model
Governance and Assessment Are Misaligned With What Education Values
Scaling Learning Design, Not Just Compliance
Designing Education Systems That Can Absorb Continuous Change
Over the past few years, higher education has moved quickly from curiosity about artificial intelligence to widespread experimentation. Universities have made tools available to faculty and students, issued guidance on responsible use, and launched pilots across teaching, research, and administration. Alongside this experimentation, a new set of questions has begun to emerge. Are these tools actually improving outcomes? And why do so many promising initiatives struggle to move beyond pilots?
These questions have featured prominently in recent discussions across the sector and are themes we have been exploring in The EDiT Journal, particularly around AI adoption, impact, and scaling. But they also point to something deeper: whether universities are structured to absorb the kind of change institutions now face. Increasingly, institutions are discovering that responding to these pressures requires more than adopting new tools. It requires building the governance, infrastructure, and measurement capabilities that allow change to be coordinated responsibly across the institution.
The challenge is not simply technological. It is structural.
Universities were designed to support academic autonomy within stable disciplinary communities. That model has many strengths, particularly for protecting intellectual independence and enabling knowledge creation. But it also means that institutional change rarely occurs through centralized direction alone.
When new technologies begin affecting teaching, research, and institutional operations simultaneously, coordinating change across a decentralized academic system becomes significantly more complex. To understand why coordinating change has become difficult, it helps to look at the institutional model universities inherited.
The University Operating Model We Inherited
Modern universities reflect a set of historical priorities that differ from those of many other large organizations.
Their primary purposes have long been to advance knowledge through research, transmit disciplinary expertise to new generations of learners, and confer credentials that signal professional and intellectual capability.
To support those goals, universities developed governance structures that emphasize academic freedom and distributed authority. Faculty and departments retain significant control over curriculum, pedagogy, and assessment. Academic senates, committees, and peer review processes play central roles in decision-making.
In many ways, universities function less like centrally managed organizations and more like federations of academic communities.
This structure has important strengths. It supports intellectual diversity, encourages disciplinary innovation, and protects the independence of academic inquiry. Different fields develop their own pedagogical traditions, methods of inquiry, and ways of evaluating knowledge.
But the same features that protect academic independence can also make coordinated institutional change difficult.
Decisions about teaching practices, assessment methods, and curriculum design are distributed across departments and programs. Innovations in pedagogy often emerge locally within individual courses or disciplines. While many institutions contain examples of exceptional teaching and meaningful learning experiences, those practices can remain localized rather than becoming part of shared institutional capability.
Universities have long been effective at coordinating administrative processes such as accreditation, credentialing, and regulatory reporting. What has proven more difficult is developing institutional mechanisms that allow effective teaching practices, learning designs, and educational innovations to become visible and transferable across the system.
This challenge is becoming more visible as institutions face new pressures—from evolving workforce expectations to rapidly advancing technologies—that increasingly require coordinated responses across teaching, research, and institutional operations.
The question universities now face is not whether change will occur. It is whether their institutional structures can absorb and coordinate it effectively.
Signals That Education’s Operating Model Is Under Strain
Many of the challenges dominating education conversations today can be understood as signals that the current operating model is under pressure.
One of the clearest signals is student disengagement.
Research across multiple systems shows engagement declining significantly as learners move through their educational journey. Students frequently describe learning as disconnected from real-world relevance, overly constrained by rigid structures, or misaligned with their level of challenge.
Disengagement, in this sense, is not simply a motivational problem. It is a design signal.
Another signal comes from the pressures facing educators.
Faculty and staff are being asked to manage increasing complexity in their roles. Teaching, research, administration, student support, and the integration of new technologies all compete for attention. In many institutions, workload pressures have become a defining feature of the operating environment.
At the same time, expectations around outcomes are evolving. Employers consistently emphasize capabilities such as communication, critical thinking, creativity, and adaptability. These are complex skills that develop through experience, reflection, and application rather than through simple recall.
Taken together, these signals suggest that many of the challenges institutions face are not isolated problems. They reflect deeper questions about how learning is organized, supported, and measured across institutions where authority over teaching and curriculum is distributed across departments, programs, and academic communities. These pressures do not originate from technology alone. But new technologies are making the structural limits of the current operating model increasingly visible.
How AI and Technology Are Exposing Education’s Operating Model
Technological change is not the root cause of these challenges. But it is making them far more visible.
Artificial intelligence provides a clear example.
In many institutions, AI tools have initially been introduced as productivity aids. They can help draft text, summarize information, or support administrative tasks. In that sense, they promise to reduce workload and free up time for educators. Yet technology rarely remains confined to a single function. As AI becomes embedded within institutional systems, it begins to influence how information flows through organizations, how assessments are designed, and how decisions are made using data.
In universities, the implications extend further. AI raises new questions about authorship, assessment validity, and how institutions evaluate evidence of learning. These questions sit at the intersection of technology, pedagogy, and academic governance. In other words, technology behaves less like a tool and more like infrastructure. Infrastructure interacts directly with the operating model of the institution.
If systems prioritize compliance and reporting, technology will accelerate those processes. If systems are designed to support deeper learning and insight, technology can help scale those outcomes. This dynamic highlights an important reality: technology will accelerate whatever operating model education already has.
Which means the most important decisions institutions face are not only about which technologies to adopt, but about the systems those technologies will operate within. In universities, those systems are defined largely by governance and academic decision-making structures.
Governance and Assessment Are Misaligned With What Education Values
Education leaders increasingly emphasize the importance of developing human capabilities that extend beyond content knowledge.
Curiosity. Creativity. Collaboration. Critical thinking. Resilience.
These qualities appear regularly in institutional mission statements and graduate profiles. Yet many of the systems used to govern and measure education still focus on narrower indicators of progress.
Completion rates, standardized assessment, and traditional grading structures remain central to how institutions track success. These tools were originally developed because they are measurable and comparable across large systems. But they capture only part of the learning experience. This creates a structural tension. Institutions say they value capabilities that are difficult to quantify, while continuing to rely on measurement systems designed for more predictable outputs.
Governance structures reinforce this tension. At their core, governance models determine who makes decisions, who is consulted, and who is accountable. Many of these structures evolved in environments where change was slower and institutional processes could remain stable over long periods. Today, however, institutions face a much faster pace of change. Technologies evolve quickly. Workforce expectations shift. New pedagogical approaches emerge.
Governance structures designed for shared academic decision-making can struggle to respond quickly to these dynamics. Authority over curriculum, assessment, and academic practice is distributed across faculty, departments, and institutional leadership. While this structure protects academic independence, it can make coordinated responses to new technologies and pedagogical approaches more difficult.
Without alignment between governance, assessment, and the outcomes institutions seek to cultivate, innovation often remains fragmented and difficult to sustain.
Scaling Learning Design, Not Just Compliance
Another challenge many institutions face is the difference between scaling processes and scaling learning. Education systems have decades of experience scaling administrative systems. Policies, reporting frameworks, and compliance structures can be implemented across departments or institutions with relative ease.
Scaling meaningful learning experiences is far more complex. Strong learning design depends on how educators structure challenges, guide student thinking, and create opportunities for exploration and reflection. These practices are often developed locally by individual instructors or small teams.
The difficulty lies in making those practices visible and transferable across larger systems. In many institutions, examples of excellent teaching remain isolated. They are recognized within programs or departments but rarely become part of shared institutional capability. Addressing this challenge requires shifting attention toward learning design as a strategic priority. Institutions need ways to identify effective practices, support educators in refining them, and create structures that allow those practices to spread.
The next operating model must therefore scale not only administrative coordination, but the institutional capacity to improve learning over time.
Designing Education Systems That Can Absorb Continuous Change
Education is entering a period where change is no longer occasional. It is continuous.
New technologies, evolving workforce expectations, and changing student needs will continue to reshape the environment in which institutions operate. Stability alone is no longer sufficient.
The next operating model for education must be capable of absorbing change.
If the challenge is structural, the response must be structural as well.
Institutions that navigate this transition successfully tend to focus on a few core capabilities.
First, governance.
Clear ownership, decision rights, and accountability structures are essential when new technologies begin influencing teaching, assessment, and institutional operations.
Second, data infrastructure.
AI and advanced analytics only become meaningful when institutional systems — from the LMS to the student information system — are connected and capable of supporting responsible data use.
Third, procurement discipline.
Many institutions are currently adopting tools reactively. A structured approach to evaluating vendors, protecting institutional data, and aligning purchases with institutional priorities becomes increasingly important.
Finally, measurement.
Without clear ways to connect new technologies to learning outcomes, operational efficiency, and institutional performance, governance risks becoming compliance rather than improvement.
These capabilities form the foundation of an operating model that can absorb change rather than react to it.
The institutions that benefit most from technological change will not necessarily be those that adopt the most tools.
They will be the ones that build the institutional capacity to adapt as education continues to evolve.
EDT&Partners
The EDT&Partners Editorial Team brings together education and technology experts sharing insights, stories, and strategies shaping the future of learning.
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