Our Framework
AI Convergence operates on three interconnected levels: what we believe, how we gather, and what we commit to.
We Don't Pretend This Is Simple
We believe these tensions are real. We don't resolve them with platitudes. We work through them together.
Boards want outcomes. Accreditors want evidence. And yet the most important learning happens through trying things that might not work. We believe leaders can create protected spaces for experimentation while still meeting their accountability obligations—but it requires intentionality, communication, and courage.
Some of you are being asked to use AI to cut costs, increase throughput, and do more with less. We believe AI should amplify human capability, not simply replace it—but we also know you're operating under real constraints. The question isn't just "where can AI save money?" but "where can AI create value we couldn't create before?"
It's tempting to treat AI as a technology problem—something IT handles. But the real challenges are human, cultural, and organizational. The technology is the easy part. AI Convergence focuses on transformation, not just technology—because that's where leaders actually need support.
Benchmarking feels safe. "We're doing what our peers are doing" is an easier conversation with your board than "we're charting our own path." We believe your institution's mission should drive your AI strategy—but we also understand the political reality of leading institutions that are constantly compared to others.
Donors ask about your AI strategy. Employers want "AI native" graduates. Parents wonder if you're preparing students for the future. Then you return to campus where faculty resist relearning, departments fight over who owns AI, and staff worry their jobs are next. We believe leaders can close that gap—but it requires coalition-building and strategic patience.
Real peer learning requires honesty about failures. But higher education rewards positioning, impression management, and strategic silence about what isn't working. We create spaces where vulnerability is safe—because learning requires it.
Research Insights
According to EDUCAUSE, more than 50% of higher education leaders call AI a strategic priority—but only 22% have developed an institution-wide approach. The gap between recognizing AI's importance and acting on it coherently is where most institutions are stuck.
Meanwhile, BCG research reveals that 74% of organizations struggle to achieve expected value from AI—and 70% of those failures trace back to people and process challenges, not technical ones.
This is a leadership problem, not a technology problem.
AI doesn't just create new problems. It exposes ones we've been avoiding:
Pillars articulate the philosophical foundation for AI-infused education. These are the beliefs we hold about how AI should transform learning—not prescriptions, but shared values that guide decision-making.
Guiding Principles define how AI Convergence operates as a community. These principles shape our convenings, our relationships, and our approach to leadership development.
Standards translate beliefs into institutional commitments. These are the operational areas where AI Convergence institutions focus their attention and demonstrate progress.
Together, these frameworks create coherence: a community (Principles) united by shared beliefs (Pillars) working toward common commitments (Standards).
The following pillars represent our core beliefs about how artificial intelligence should transform higher education. They are not compliance requirements—they are values we hold in common.
We lead with integrity, weaving ethical reasoning into every AI decision—not as a compliance exercise, but as a reflection of who we are. At the same time, we embrace experimentation as essential to learning. Rigid prescriptions cannot keep pace with AI's evolution; courageous, principled experimentation can.
We believe AI should amplify human capability, not replace it. While AI excels at pattern recognition, analysis, and generating possibilities, humans bring what machines cannot: relationships, empathy, contextual judgment, and the wisdom to navigate ambiguity. We commit to building institutions where AI extends human insight while preserving distinctly human capacities.
We reject the notion that AI transformation requires adopting someone else's playbook. Every institution has a distinct mission, student population, and identity—and AI implementation should strengthen what makes us distinctive, not flatten education into standardized approaches. We commit to contextual strategies that honor our values.
We invest in the conditions that make transformation possible: environments where experimentation is safe, sustained development that builds faculty and staff confidence, and infrastructure that serves the institution's mission rather than constraining it.
These principles define who we are as a community and how we show up for one another.
AI Convergence exists to strengthen institutions, not judge them. We help leaders see clearly where they are, envision where they want to be, and chart a realistic path forward. We reject one-size-fits-all prescriptions; every institution brings a distinct mission, culture, constraints—and starting point.
Some arrive having barely begun; others arrive years into transformation. Both find value because we focus on your next frontier, not a standardized curriculum.
Institutions cannot change if their leaders do not. AI Convergence invests in the human beings carrying the weight of these decisions—helping them develop the clarity, courage, and capabilities to lead through uncertainty. We encourage bold experimentation over legacy playbooks.
AI Convergence intentionally brings together leaders from a broad cross-section of institutions not because their contexts are identical, but because their challenges often are.
We also bridge the divide between academic leadership and technology innovation, creating honest dialogue between educators and the people building AI tools.
AI Convergence is built for pairs. Each senior leader brings a key decision-maker they will empower to drive implementation. Together, they engage in co-design sessions where you're not just receiving frameworks—you're building them alongside peers, technology thought leaders, and industry voices.
You leave with a 90-day action plan that advances your existing momentum—not a generic starting template. Whether you're launching or scaling, the goal is capability and forward motion, not dependency on consultants.
AI Convergence is not a conference you attend; it's a community you belong to. The convening launches your journey, but the real work happens in the months that follow—through Transformational Leadership Circles that provide ongoing peer support, through the Fellows Program that develops emerging leaders, and through relationships that deepen over time.
Leaders return to future convenings with different implementation partners to tackle new challenges, building institutional capacity across multiple areas.
While our Pillars articulate what we believe, the Standards define where we focus our institutional attention. These six standards represent the operational commitments AI Convergence institutions make to one another and to their communities.
These standards are developmental, not evaluative. No institution has mastered all six. We share them not to judge where you are, but to clarify where we are all headed.
We believe in evidence-based transformation. It's easy to announce AI initiatives, adopt new tools, or create task forces. It's harder to demonstrate that these efforts actually improve student outcomes, faculty capability, or institutional effectiveness.
AI Convergence institutions commit to measuring what matters: not just activities and inputs, but outcomes and impact.
Every institution is somewhere on the journey. We use three developmental levels:
AI Convergence institutions prioritize ethical AI leadership as a fundamental principle. Ethical considerations and fairness are not add-ons but central to how AI is designed, deployed, and taught.
We create environments where faculty, staff, and students can test, learn, and iterate without fear of failure. This requires empowering faculty innovation, creating reflective practice, and designing hands-on learning experiences.
AI Convergence institutions embrace a Human+AI approach where artificial intelligence and human capability strengthen each other. We develop human-in-the-loop methodologies that recognize this collaboration as genuinely reciprocal.
AI is not limited to large language models. AI Convergence institutions recognize the rapidly evolving AI landscape—agentic AI, small language models, analytics integrators. Each unit thinks strategically about which AI solutions fit their context rather than adopting one-size-fits-all approaches.
AI Convergence institutions support their learning efforts through suitable AI infrastructure and sustained investment in professional development. Infrastructure serves learning rather than constraining it.
AI Convergence institutions empower students as informed, critical, and active participants in AI-enhanced education. Students develop foundational AI literacy, understand the capabilities and limitations of AI systems, and cultivate the judgment to know when and how to leverage AI tools effectively.
Employers tell us they need graduates who can work with AI—not people who learned to avoid it. Yet many employers remain skeptical that graduates actually possess the AI capabilities their transcripts or credentials imply.
AI Convergence institutions commit to closing this gap by ensuring graduates have demonstrable AI fluency—not just exposure to AI tools, but documented evidence of applying AI effectively in authentic contexts.
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The Experience Register Interest