Geoffrey Moore's Crossing the Chasm framework explains why AI adoption stalls between enthusiasts and mainstream users. Learn how to bridge the gap and scale AI tools across your organization.
By OperatorRadar Editorial
Geoffrey Moore's 1991 framework, detailed in *Crossing the Chasm*, describes a critical adoption gap between early adopters (visionaries who embrace new technology) and the early majority (pragmatists who need proven ROI and support). Moore argued that products fail not because they're bad, but because they're marketed and distributed as if the early majority behaves like early adopters. The chasm is the valley between these two groups—where many promising technologies die.
Moore built on Everett Rogers's diffusion of innovations curve (1962), which mapped how technologies spread through populations. By the 1990s, Moore observed that high-tech products faced a specific problem: early adopters loved bleeding-edge features and visionary potential, but the early majority wanted proven solutions, references, support, and clear business cases. Companies that treated these groups identically failed to cross over. Successful crossers (Windows, Oracle, Cisco) segmented ruthlessly, built reference customers, and created turnkey solutions for pragmatists.
Moore meant that technology adoption is not a smooth curve. There is a genuine discontinuity—a chasm—between visionaries (who buy on potential and vision) and pragmatists (who buy on proof, peer validation, and risk mitigation). Crossing requires: (1) picking a beachhead market where you can dominate, (2) building a complete solution (not just a tool), (3) creating reference customers and case studies, (4) aligning sales, marketing, and support around pragmatist needs, and (5) establishing yourself as the category leader in that segment before expanding.
AI tools amplify the chasm problem. Early adopters (data scientists, engineers, marketing teams) experiment with ChatGPT, Claude, and specialized models—they tolerate poor documentation, API instability, and unclear ROI. The early majority (finance, HR, operations, compliance teams) won't touch AI until they see: (1) peer companies using it successfully, (2) clear guardrails around data security and bias, (3) integration with existing systems, (4) measurable productivity gains, and (5) vendor stability. The speed of AI iteration also creates a new friction: pragmatists fear adopting a tool that will be obsolete in six months. Additionally, AI's regulatory uncertainty (data privacy, IP ownership, audit trails) makes pragmatists even more cautious than they were with prior technologies.
The core insight holds: you cannot cross the chasm by treating early adopters and pragmatists the same way. Early adopters will champion AI internally, but they are not your mainstream market. To scale AI adoption across your company, you must: (1) identify a beachhead use case where AI delivers measurable, low-risk value (e.g., customer service automation, expense categorization, meeting summarization), (2) build a complete solution with governance, training, and integration—not just a tool license, (3) create internal case studies and peer validation (the finance team seeing HR succeed with AI), (4) establish clear policies on data handling and model selection, and (5) designate champions and support structures for pragmatists. The pragmatist majority still buys on proof, not vision.
Moore's original framework assumed longer technology cycles. AI tools evolve monthly; your internal adoption strategy must account for rapid model updates, shifting vendor landscapes, and the possibility that your chosen tool becomes outdated. The framework also assumed a single 'complete solution'—but AI adoption is now modular. Teams may use different models for different tasks (ChatGPT for brainstorming, Claude for analysis, specialized models for domain work). Additionally, Moore's framework emphasized establishing a single category leader; in AI, your company may need to support multiple tools and vendors simultaneously, which changes how you build consensus and governance. Finally, the chasm was traditionally about external market positioning; internal AI adoption is also about change management, skill-building, and cultural shift—dimensions Moore's framework doesn't directly address.
As an operator rolling out AI, treat your company as a market with distinct segments. Your early adopters (usually product, engineering, or marketing teams) will self-onboard and experiment. Don't let them set the pace for the rest of the company. Instead: (1) Run a structured pilot with a pragmatist-heavy team (finance, operations, compliance) on a high-impact, low-risk use case. (2) Measure and document the outcome—time saved, error reduction, cost impact. (3) Build a governance framework (data security, model selection, acceptable use) that pragmatists need to feel safe. (4) Create internal training and a support channel (Slack, office hours, documentation) so pragmatists don't feel abandoned. (5) Celebrate peer wins loudly—when operations succeeds with AI, finance pays attention. (6) Set a realistic timeline: crossing the chasm internally takes 6–12 months, not weeks. Rushing creates backlash and stalls adoption for years.
Think about your company's current AI adoption. Which team is furthest ahead—and are they early adopters or pragmatists? What use case would convince a pragmatist team (finance, HR, operations) that AI is worth learning? What's the one thing that team needs to feel safe adopting AI (security policy, training, peer validation, integration)? Start there.
Geoffrey Moore
Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customers (1991)
“Moore's framework identifies a critical discontinuity between early adopters (visionaries) and the early majority (pragmatists). Pragmatists require proof, peer validation, and complete solutions—not just innovative features.”
Everett Rogers
Diffusion of Innovations (1962)
“Rogers's diffusion curve maps how technologies spread through populations in stages: innovators, early adopters, early majority, late majority, and laggards. Moore applied this to high-tech markets and identified the gap between early adopters and early majority as a critical failure point.”
OperatorRadar Editorial
AI Adoption in Enterprise: The Pragmatist Problem
“AI tools amplify the chasm problem because pragmatists require governance, security assurance, integration, and peer validation before adoption. Rapid model iteration and regulatory uncertainty make pragmatists even more cautious than with prior technologies.”
Rolling out AI across your company requires more than a tool license—it requires strategy, governance, and change management. OperatorRadar's AI Adoption Playbook walks you through segmenting your audience, running pilots, building trust, and scaling adoption. Get a custom roadmap for your organization.
Request implementationNeed a custom implementation?
Have Ekofi Lyrae design and implement AI agents and automations.