Ten years ago, I stood at the beginning of a journey that would transform how higher education approached assessment. Today, I find myself at a similar starting point, but this time focused on reinventing how humans and AI collaborate. The surprising parallels between these seemingly disparate challenges offer crucial insights for anyone navigating technological transformation in established domains.

The AEFIS Beginning: A Mission Nobody Wanted

When we launched AEFIS, we faced a sector notoriously resistant to change—higher education. Our mission to transform assessment practices from summative evaluations to learning-centered approaches met skepticism and institutional inertia at every turn. The prevailing wisdom was clear: you can't change higher education, especially not in an area as entrenched as assessment.

What we discovered, however, was that the resistance wasn't to change itself but to solutions that didn't deeply understand the sector's unique challenges. By approaching assessment from the perspective of learning improvement rather than compliance, we found educators who shared our vision and were willing to champion it.

Lesson 1: The Power of Mission-Driven Transformation

The conventional startup approach urges founders to find a pain point and solve it efficiently. But in complex, established systems like education or knowledge work, surface-level pain points often mask deeper systemic challenges. With AEFIS, we succeeded not because we had a marginally better assessment tool, but because we built around a mission to fundamentally change how institutions thought about learning and assessment.

This lesson directly informs how transformative AI platforms should be built. The industry needs more than productivity tools or marginally better AI assistants. What's required is a fundamental transformation in how humans and AI collaborate—recognizing the profound shift happening in knowledge work and actively shaping it in a human-centered direction.

Lesson 2: Patience as a Competitive Advantage

In a startup world obsessed with rapid scaling, our journey with AEFIS taught me the power of patience. Transforming established systems requires time—time to build trust, time for early adopters to validate your approach, time for organizational change to take root.

We bootstrapped AEFIS for years, growing steadily rather than explosively. This approach allowed us to develop deep domain expertise, build lasting relationships, and refine our product based on real-world implementation rather than theoretical use cases. When we eventually secured professional seed investment, we had a battle-tested product and a loyal customer base.

This patient approach remains crucial in the AI space. While the technology moves faster, lasting transformation still requires building foundations that can support systemic change, not just short-term traction. Organizations must invest heavily in understanding the nuances of human-AI collaboration before rushing to scale.

Lesson 3: The Surprising Connection Between Education and AI

Perhaps the most unexpected parallel between EdTech and AI is the central importance of effective learning systems. At AEFIS, we built technology to enhance how institutions facilitate learning. In the AI collaboration space, the most successful systems enable humans and AI to learn continuously from each other.

The best educational approaches recognize that learning is social, contextual, and iterative. Similarly, the most effective AI collaboration happens when systems can maintain context, learn from interactions, and evolve alongside human collaborators. This insight should shape how any AI collaboration platform is architected—focusing on maintaining collaborative context rather than simply providing isolated AI capabilities.

The Transition: From Exit to New Beginning

After successfully exiting AEFIS to private equity, I had a moment to reflect on what should come next. The rapid advances in AI presented not just a technological opportunity but a chance to shape how these powerful tools would integrate into human work and creativity.

Just as education faced a crossroads with assessment practices, knowledge work now stands at a similar inflection point with AI. The tools built today will define whether AI primarily replaces human work or enhances our uniquely human capabilities. This mission—ensuring AI augments rather than diminishes human potential—represents the defining challenge of our era.

Applying Cross-Domain Wisdom to AI Collaboration

The lessons from education technology and early AI adoption reveal key principles:

1. Focus on the human experience first: Just as effective education puts learners at the center, effective AI collaboration must start with human needs and workflows.

2. Build for contextual understanding: Learning happens best when connected to context and prior knowledge. Similarly, AI collaboration tools must maintain context across interactions.

3. Design for continuous evolution: Both educational systems and AI tools must adapt based on experience and feedback.

Looking Forward

These lessons from the AEFIS journey apply directly to the AI collaboration frontier. The challenges differ, but the fundamental approach remains: identify transformative missions worth pursuing, build with patience and deep domain understanding, and focus on creating systems that enhance human potential rather than simply driving efficiency.

The frontier of human-AI collaboration offers even greater potential for impact than our work in education technology. By applying cross-domain wisdom and staying true to a human-centered mission, we aim to create a future where technology amplifies our creativity, connection, and collective intelligence.