From Market Adoption to Meaningful Integration

When Geoffrey Moore wrote Crossing the Chasm in 1991, he illuminated the gap between early adopters and mainstream markets, becoming a cornerstone of enterprise technology thinking. But AI doesn’t play by those rules.

We are not simply witnessing another wave of enterprise technology innovation. We’re seeing a new kind of chasm that can’t be crossed with sleek demos or aggressive sales.

The AI chasm is about integration, not adoption. It demands that we move from scarcity thinking to abundance economics, from market penetration to societal integration, and from data extraction to knowledge stewardship.

SOLVING PROBLEMS VERSUS SELLING PRODUCTS

Many enterprise AI vendors make the mistake of assuming success is revenue. They see crossing the chasm as a marketing problem—gaining more logos, proving scalability, and increasing market share.

But AI is different. It’s not a discrete product with a limited set of use cases. It’s a capability—a layer that gets embedded into everything. Its impact depends as much on the context in which it’s deployed as the code it runs.

To cross the AI chasm, vendors and builders must start with this question: What specific, urgent problem are we solving—and for whom?

This means understanding the total cost of ownership—not just compute and storage, but also data labeling, model maintenance, explainability, and human oversight. Build with an awareness of regulatory exposure, workforce integration, and change management.

The pragmatic majority doesn’t need another proof-of-concept. They need proof of value, contextual, relational, and vibrant in this new landscape.

AI LITERACY IS THE NEW PHONICS

To understand how deep this chasm runs, consider our journey to learn how to read. Reading is not natural. As Maryanne Wolf writes in Proust and the Squid: The Story and Science of the Reading Brain (HarperCollins, 2007), humans have not biologically evolved for literacy. We co-opted existing neural pathways to decode symbols and derive meaning.

AI is now our subsequent cultural adaptation. But we’re treating it like a plugin. We expect everyone to understand and speak fluently about model drift, training bias, vector embeddings, and AGI timelines. However, we haven’t created the learning infrastructure to support this literacy. That’s the fundamental chasm between those who’ve built the mental models to understand AI and those who’ve been left behind, not by ability, but by exclusion.

Just as we needed phonics and structured reading instruction to make literacy universal, we now require a new alphabet for AI—a method to teach people how to establish relationships with intelligent systems, question their inferences, and co-evolve with them.

FROM EXTRACTION TO REGENERATION

The parallels to Indigenous gift economies are instructive. Just as traditional communities manage abundant resources through cultural practices and local expertise, enterprises must develop new frameworks for stewarding AI capabilities.

We’re witnessing the emergence of regenerative knowledge networks, where value comes not from hoarding information but from ethically applying and sharing it. This requires new metrics beyond market penetration and user acquisition. We must measure success in terms of community benefit, knowledge accessibility, and ethical deployment.

There is a cost to ignoring the chasm. If we don’t cross this chasm—if we build AI for the loudest voices, easiest wins, and shallowest integrations—we risk re-creating extractive patterns seen in social media and surveillance capitalism.

We risk trading wisdom for inference, understanding for optimization, and meaning for metrics.

But we can choose another way. We can create enterprise AI grounded in context, aligned with human learning curves, and accountable for real-world outcomes. We can prioritize stewardship over scale, value over vanity, and literacy over leverage.

CROSSING TOGETHER

The future of enterprise AI doesn’t belong to those who build the biggest models or close the most deals. It belongs to those who understand that adoption is not the endgame—integration is.

The next wave of enterprise AI success stories will not come from those who best manipulate markets or extract data. Instead, they will come from organizations that successfully build bridges between local and global knowledge networks, prioritize transparency over black-box solutions, and understand the difference between information abundance and wisdom.

It belongs to those willing to teach, listen, and build systems that serve human comprehension’s slow, sacred work. It belongs to you.