AI adoption in higher education is moving faster than institutional change models were built to handle. Students are already using AI at high rates, while many institutions are still trying to decide where AI belongs, who should lead it, and how much change is required.

In this episode of the Changing Higher Ed® podcast, Dr. Drumm McNaughton speaks with Nikki Barua, serial entrepreneur and founder of FlipWork, about why higher education's traditional change management playbook will not work in the AI age.

Drawing on her work with Fortune 500 companies and AI implementation, Barua explains why AI should be treated as institutional infrastructure, not an IT project. She discusses the growing gap between technology adoption and human readiness, why many AI pilots fail, and how institutions can move from slow, episodic transformation to shorter, people-centered reinvention cycles.

This conversation is especially relevant for presidents, boards, provosts, CIOs, and senior leadership teams trying to prepare students, faculty, staff, and institutional systems for an AI-driven future.

Topics Covered

Why incremental change management cannot keep pace with AI
How AI differs from previous technology disruptions like the internet and mobile
Why AI should be treated as infrastructure across the institution
What the AI readiness gap means for higher education leaders
Why many AI pilots fail when organizations focus on tools instead of people
How AI may reshape entry-level jobs and the graduate talent pipeline
Why skills-based hiring is changing what students need from higher education
How faculty roles may shift from content delivery to mentorship, ethics, and judgment
Why liberal arts and human skills may become more valuable in the AI age
How human-in-the-loop design can improve AI use in enrollment, advising, and student support
Why AI literacy must become a core institutional capability

Real-World Examples Discussed

AI adoption among students far outpacing institutional readiness
Corporate AI pilots failing because organizations did not prepare people to use the tools effectively
Entry-level jobs shrinking or changing as AI takes over early-career tasks
Employers moving toward skills-based hiring and project-specific teams
AI tutors, teaching assistants, adaptive learning tools, and student support applications
Enrollment chatbots that create frustration when they replace rather than support human interaction
Human-in-the-loop workflows that know when to hand a student or prospect to a person
Ethics in AI as a foundation for preparing graduates to use powerful tools responsibly

Three Key Takeaways for Higher Education Leadership

AI is an opportunity for reinvention, not an IT project. Institutions should treat AI as a strategic leadership issue that affects competitive position, culture, academic delivery, student support, and institutional agility.
Students are already ahead of many institutions. Without governance, ethical guidelines, and structured leadership, AI use can become unmanaged shadow AI across the institution.
The cost of waiting grows exponentially. AI is advancing week by week, and institutions that delay action will face a widening readiness gap that becomes harder and more expensive to close.

This episode offers a direct look at why higher education cannot rely on its traditional pace of change in the AI age, and why institutional leaders must rethink what they offer that AI cannot replicate.

Read the transcript: https://changinghighered.com/closing-higher-educations-ai-readiness-gap/

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