Inaugural Convening Archive • Miami Herbert • June 21–23, 2026
The Experience & Agenda
An archived record of our inaugural convening at Miami Herbert Business School. Our next convening is September 13–15, 2026 at City University of Seattle.
A Curated Community
AI Convergence is not open registration. We intentionally curate each convening to ensure every participant is fully committed to the work—leaders who are ready to engage deeply, share honestly, and follow through on commitments.
This selectivity is what makes the room valuable. When every person is "100% locked in," as one of our advisory board members put it, the conversations go deeper, the peer learning is richer, and the accountability is real. Like building a cohort in our educational programs, we seek leaders who bring different perspectives, institutional contexts, and expertise to create a powerful group learning environment.
We're building a community of innovative leaders who share a mindset about AI's importance and a commitment to action—not a conference that maximizes attendance.
What We Look For
- Senior higher education leaders with decision-making authority over AI strategy, curriculum, faculty development, operations, or student experience.
- A collaborative dyad with the bandwidth and authority to drive change.
- Willingness to engage in honest conversation about challenges, not just successes.
- Commitment to follow through on the personalized action plan developed at the convening.
- Openness to joining AI Convergence on an annual basis.
- Participation in a Transformational Leadership Circle (TLC).
The leaders who thrive in this community are those ready to be beginners again—regardless of how far along their institution already is. If you're looking for a passive conference experience, this isn't the right fit. If you're ready to work, we want you in the room.
Convening Composition
Each convening is intentionally sized and composed to maximize value for all participants:
Institutional Pairs
35–45
Senior leaders and their implementation partners from diverse institution types—R1 universities, regional comprehensives, community colleges, and professional schools
Technology Partners
8–12
Founders, product leaders, and engineers bringing frontier AI expertise and honest perspective—no sales pitches
Industry Experts
10–15
Employer voices and workforce leaders sharing how AI is reshaping the world our students will enter
Three Tracks
Track A
AI for Student Experience and Career Readiness
AI-enhanced pedagogy, building student AI fluency, rethinking assessment, AI advising and career coaching tools, and closing the "employer trust gap."
Track B
Building Faculty AI Fluency and Integration
Building faculty AI fluency from basic prompting to agentic workflows, creating psychologically safe experimentation cultures, and navigating shared governance.
Track C
AI Infrastructure, Governance, and Operations
Enterprise AI platform decisions, data governance, AI policy development, resolving "who owns AI" disputes, and operational AI applications.
Attendees select their primary track during registration but receive exposure to all tracks during plenary sessions.
Agenda
Plenary sessions bring all participants together. Working sessions are facilitated within leadership pairs. Track sessions organize participants by their primary track focus for deeper, context-specific discussions.
Sunday, June 21: AI Convergence Launch
AI Insight Confab #1: Opening the Conversation
A cross-sector dialogue designed to surface assumptions, tensions, and possibilities.
Opening Remarks: The AI Convergence Philosophy
This opening session sets the mindset for AI Convergence: this is not a conference to attend passively, but a working engagement for leaders who are ready to think honestly, learn across sectors, and act with intention. The remarks will frame AI as a strategic, academic, operational, and human challenge, not simply a technology issue. Participants will be invited to approach the convening with openness, humility, urgency, and a commitment to move from conversation to action.
Sandeep Krishnamurthy, Tawnya Means
Dazzling Demo #1: AI Tools Every Leader Should Know
This session introduces AI tools that are already reshaping how academic, administrative, and strategic work gets done. Leaders will see short, practical demonstrations from BoodleBox, AristAI, and Axio, followed by brief Q&A. The session concludes with a moderated panel discussion on what these technologies reveal about the future of higher education, including AI fluency, accessibility, workflow transformation, governance, and institutional readiness. Format: Three 10-minute demos, 5 minutes of Q&A per demo, followed by a 15-minute panel discussion.
Elizabeth Miller, Tony Zhang
Welcome Reception, Networking Dinner & Fireside Chat: Leading Through AI Convergence
This opening evening brings participants together for facilitated introductions, community-building, and informal conversation before the working sessions begin. Hosted by Dean Paul Pavlou, the reception and networking dinner create space for leaders across higher education, technology, and industry to connect across roles and contexts. The evening concludes with a fireside chat that frames the leadership questions at the heart of AI Convergence: how institutions can move from awareness to action, from experimentation to strategy, and from AI adoption to meaningful transformation.
Paul Pavlou, Sandeep Krishnamurthy
Monday, June 22: AI Convergence in Action
Breakfast and Orientation
AI Insight Confab #2: Deepening the Institutional Question
A small-group conversation focused on what AI means for strategy, governance, learning, work, and institutional readiness.
Plenary: The AI-Transformed University
This opening plenary frames the strategic landscape for AI Convergence. Sandeep Krishnamurthy and Tawnya Means will lead a series of conversations with Daniel Liebeskind of Topia Interactive, Casey Evans of American University, and other leaders about how AI is changing the future of higher education. The session will examine AI’s trajectory from today’s large language models to agentic systems and longer-term AGI planning horizons, with a focus on what institutional leaders need to understand now. Together, the conversation will explore essential use cases every university must address, including teaching and learning enhancement, student advising, recruitment and enrollment, career connection, and operations.
Sandeep Krishnamurthy, Tawnya Means, Daniel Liebeskind, Casey Evans
Networking Break
Working Session 1: AI Readiness Diagnostic, Track A: AI for Student Experience and Career Readiness
Participants will assess their institution’s current readiness to use AI in ways that improve the student experience, strengthen AI fluency, support advising, and connect learning more directly to career outcomes. This session helps leadership pairs identify where they are already making progress, where students are experiencing gaps or mixed messages, and where AI could most meaningfully improve support, belonging, learning, and workforce preparation.
Alex Sevilla, Sharon Lydon
Working Session 1: AI Readiness Diagnostic, Track B: Building Faculty AI Fluency and Integration
Participants will examine their institution’s current capacity to support faculty AI fluency across teaching, research, assessment, and academic innovation. The session focuses on the conditions faculty need to experiment responsibly, including psychological safety, practical training, shared governance, clear expectations, and support for moving from basic prompting to more advanced AI-enabled workflows.
Paul Pavlou, Tawnya Means, Sandeep Krishnamurthy
Working Session 1: AI Readiness Diagnostic, Track C: AI Infrastructure, Governance, and Operations
Participants will assess the institutional foundations required for responsible and scalable AI adoption, including governance structures, enterprise platforms, data policies, security, operational use cases, and ownership of AI strategy. The session helps leadership pairs identify where fragmented decisions may be creating risk and where stronger infrastructure could enable innovation across the institution.
Joe Abraham, Daniel Liebeskind
Lunch
Dazzling Demos #2: AI Technologies Every Leader Should Know
This session continues the exploration of AI tools that every higher education leader should understand. Through focused demonstrations from Syneurgy, QuantHub, and CollegeVine, participants will examine how AI is changing learning, workforce preparation, student support, analytics, and institutional decision-making. The session concludes with a panel discussion focused on what is real now, what is emerging, and what leaders need to understand before making strategic AI decisions.
Michael Mannino, Josh Jones
Industry Perspective: The Changing Face of Careers
How is AI reshaping the work our students are preparing to enter, and what will it take for graduates to thrive? Led by Alex Sevilla in conversation with industry professionals, this session brings employer and workforce perspectives into the center of the AI Convergence conversation. Panelists will explore how AI is changing hiring expectations, skill requirements, career pathways, and the qualities that differentiate graduates in AI-augmented workplaces. The discussion will help institutional leaders think more clearly about how curriculum, career readiness, student AI fluency, and employer partnerships must evolve together.
Alex Sevilla
Networking Break
Track Sessions: AI Implementation Case Studies, Track A: AI for Student Experience and Career Readiness
Institutions and partners will share concrete examples of AI implementation focused on students: advising, career readiness, student AI fluency, learning support, assessment, and the employer trust gap. Participants will examine what worked, what failed, what conditions mattered, and what lessons can transfer across institutional contexts.
Alex Sevilla, Josh Jones, Sandeep Krishnamurthy
Track Sessions: AI Implementation Case Studies, Track B: Building Faculty AI Fluency and Integration
This track session highlights real institutional approaches to building faculty AI fluency and integrating AI into teaching, learning, assessment, and research practice. Participants will explore examples of faculty development, course redesign, research support, governance engagement, and culture-building, with attention to both early wins and implementation challenges.
Paul Pavlou, Elizabeth Miller
Track Sessions: AI Implementation Case Studies, Track C: AI Infrastructure, Governance, and Operations
This session focuses on how institutions are building the operational, technical, and governance foundations needed for AI at scale. Case studies will explore enterprise AI decisions, data governance, policy development, operational efficiency, procurement, risk management, and the challenge of coordinating AI work across academic and administrative units.
Daniel Liebeskind, Casey Evans, Joe Abraham
Working Session 2: AI Strategy Design, Track A: AI for Student Experience and Career Readiness
Leadership pairs will begin designing a focused AI strategy to improve the student experience and strengthen career readiness. Participants will identify a high-impact opportunity, clarify the student problem they are trying to solve, consider equity and access implications, and define the institutional partnerships needed to move from idea to implementation.
Alex Sevilla
Working Session 2: AI Strategy Design, Track B: Building Faculty AI Fluency and Integration
Leadership pairs will design a practical strategy for advancing faculty AI fluency in ways that support teaching quality, research productivity, responsible experimentation, and academic integrity. Participants will identify the next frontier for faculty support at their institution, including the training, incentives, governance processes, and peer-learning structures needed to make progress.
Tawnya Means, Hussein Issa, Alyssa DeNaro
Working Session 2: AI Strategy Design, Track C: AI Infrastructure, Governance, and Operations
Leadership pairs will design a strategy for strengthening the institutional systems that make responsible AI adoption possible. Participants will identify priority decisions related to governance, platforms, data, policy, operations, and risk, then begin shaping a realistic plan that aligns technical infrastructure with academic mission and institutional strategy.
Joe Abraham, Michael Mannino
Dinner - On Your Own
Tuesday, June 23: Convergence Continued
Breakfast and Reflection Sharing
Table discussions on overnight insights.
AI Insight Confab #3: From Insight to Leadership
A reflective dialogue that helps participants name what has shifted, what matters now, and what they are ready to carry forward.
Whiteboard Session: Building the AI-Ready Institution
Many institutions want to make it easier for deans, faculty, administrators, and students to develop safe, ethical, and high-impact AI use cases. But scaling AI responsibly requires more than selecting the newest tools. It requires the right architecture, governance, data practices, and operational foundations underneath the work. In this whiteboard session, Joe will walk participants through the institutional “plumbing” needed to enable AI at scale, support innovation across units, and improve the outcomes that matter most. A recording of this session will be available for participants to share with key stakeholders after the event.
Joe Abraham
Working Session 3: 90-Day AI Action Plan Finalization, Track A: AI for Student Experience and Career Readiness
Participants will finalize a 90-day action plan focused on improving student experience, AI fluency, advising, career readiness, or student support. Leadership pairs will identify first steps, key stakeholders, likely barriers, equity considerations, and evidence of progress, then prepare to share a clear commitment with the full AI Convergence community.
Alex Sevilla, Sharon Lydon, Sandeep Krishnamurthy
Working Session 3: 90-Day AI Action Plan Finalization, Track B: Building Faculty AI Fluency and Integration
Participants will finalize a 90-day action plan for advancing faculty AI fluency and responsible integration into teaching, research, assessment, or academic practice. Leadership pairs will define immediate actions, faculty engagement strategies, governance needs, support structures, and potential obstacles so they leave with a plan that is ambitious, credible, and ready to begin.
Tawnya Means, Paul Pavlou
Working Session 3: 90-Day AI Action Plan Finalization, Track C: AI Infrastructure, Governance, and Operations
Participants will finalize a 90-day action plan focused on the institutional foundations for AI adoption, including governance, platforms, data use, policy, operations, security, or cross-unit coordination. Leadership pairs will identify Week 1 actions, decision points, stakeholders, risks, and mitigation strategies, then prepare to present the AI infrastructure or governance commitment they are ready to advance.
Joe Abraham
Closing Session: From Convergence to Action
This closing session brings the AI Convergence community back together to reflect on what surfaced, what shifted, and what comes next. Participants will hear key themes from across the tracks, consider the leadership commitments emerging from the 90-day action plans, and identify opportunities for continued collaboration beyond the convening. The session is designed to move participants from individual insight to shared momentum, closing the event with clarity, accountability, and a renewed sense of possibility for AI-enabled transformation in higher education.
Sandeep Krishnamurthy, Tawnya Means
📍 Storer Auditorium
Closing Lunch and Departures
From our inaugural convening
This agenda is an archived record of the inaugural AI Convergence, held June 21–23, 2026 at Miami Herbert Business School. Join us for our next convening, September 13–15, 2026 at City University of Seattle.
Register for Seattle