Abstract:

With the growing pressure to adopt Artificial Intelligence and digital tools, universities worldwide have been investing heavily in technological platforms to modernize their operations. However, most of these initiatives focus on academic innovation (AI-enhanced teaching, adaptive learning, learning analytics) while largely ignoring the operational core of the institution: the administrative and support staff responsible for managing processes such as enrollment, resource allocation, class scheduling, and institutional reporting.

This paper argues that digital transformation in higher education cannot succeed without first addressing two critical and often neglected preconditions: operational process maturity and staff learning. Drawing on two institutional cases, a leading business school in Latin America and a top-ranked technical university in Southern Europe, the study reveals a common pattern. In both institutions, operational inefficiencies were not caused by the absence of technology, but by the lack of structured processes and by staff members who had never been formally trained in how educational operations work, how to manage data effectively, or how to think processually about their own work.

The paper introduces the concept of Workplace Learning for Educational Operations, proposing that the same principles of Learning Science used to develop faculty, such as planning clear learning outcomes, designing active learning experiences on the job, assessing through evidence, and providing structured feedback, should be applied to the development of administrative staff. Current staff training in higher education tends to be content-centered, focused on software instructions and procedural compliance, rather than on genuine learning that enables professionals to understand why processes exist, how they connect to the institutional mission, and how to improve them continuously.

Findings from both cases suggest that when institutions invest in process improvement and staff learning before implementing new technology, adoption rates increase, data quality improves, and operational teams become capable of sustaining transformation beyond the initial consulting engagement. Operational maturity also proves to be the foundation for productive use of Artificial Intelligence: when professionals understand the processes they perform, they are not replaced by AI but learn to use it to their advantage, becoming process managers rather than executors of automatable tasks. However, this development cannot be generic. Education is a sector with its own dynamics (academic cycles, governance structures, regulation, multiple stakeholder relationships) that demands a professional development approach specific to the educational context. Currently, these professionals turn to generic management training, with no access to programs addressing the particularities of the education business. The paper concludes that higher education institutions need a new category of professional development: one that bridges the gap between academic excellence and operational competence, and that treats staff not as system users, but as learners within an educational organization.

Keywords:

Operational excellence, staff development, digital transformation, learning science, higher education, Artificial Intelligence in education.