Cross the Chasm: The Enterprise AI Adoption Challenge

AI spending surged to $13.8 billion in 2024, but only 28% of workers actually use AI at work. At current adoption rates, it'll take 3-5 years to reach majority usage across your organization.

Slow adoption of new tech is normal. There's a known pattern to how people adopt new technology. First it’s the innovators and early adopters, a small portion of your workforce who likely adopted AI before you paid for the licenses. Then there’s the rest.

There’s an adoption chasm between the early minority and the later majority.

The big question for business leaders: How do you cross the adoption chasm at your organization quickly?

Geoffrey Moore's book "Crossing the Chasm" lays out how people actually adopt new tech. The adoption curve breaks down into five groups:

1. Innovators (2.5%): Try anything new just because it's interesting
2. Early Adopters (13.5%): See strategic advantage and willingly experiment with imperfect solutions THE CHASM: the critical gap between early adopters and mainstream users
3. Early Majority (34%): Pragmatists who need proven ROI and references before adopting
4. Late Majority (34%): Skeptics who adopt only when they feel pressure to keep up
5. Laggards (16%): Resist change until absolutely forced

The chasm exists because early adopters tolerate problems and enjoy experimentation, while the majority demands reliability and proven results. Early adopters will spend hours figuring out effective AI prompts. The majority won't.

Applied to AI adoption: Your innovators and early adopters are already experimenting with ChatGPT and Copilot. But the majority—the pragmatists and skeptics—won't adopt without seeing specific, proven applications that clearly benefit their daily work.

It’s likely that most of your workforce is still on the wrong side of the chasm.

The competitive stakes are too high for slow adoption.

• Companies effectively using AI see 40% productivity improvements
• Workers using AI properly save 5.4% of work hours weekly
• Market leaders will be determined in the next 18-24 months, not 3-5 years

The numbers will look a bit different across departments and industries. Whatever they end up being in your business, the key to realizing the gains is speed to adoption. Employees need to start using AI for their day to day work if the business is going to gain any advantage.

Business leaders can't skip steps in technology adoption, but they can speed up the process and condense the timeline to cross the chasm quicker.

The key is getting your innovators to prove out the AI workflows for your pragmatists and skeptics. Let your early adopters become the testing ground for refined prompts, proven workflows, and documented benefits for different departments.

This approach works because your early adoptions have a high tolerance for testing, while your pragmatists and skeptics need "whole product solutions." They need to see complete, tested applications. New technology doesn’t excite them. For AI adoption, this means:

• Role-specific use cases tested and refined by early adopters
• Proven prompts and templates that consistently deliver results
• Documented business impact from real workplace applications
• Peer validation from colleagues in similar roles

One proven way to do this is using on-the-job activities that give employees bite-sized and clear instructions, including AI prompts, to complete role-specific daily tasks using AI.

Use early adopters from different departments to refine the activity-based training—not because they need to learn but because they can provide the necessary feedback to ensure the activities will work for the pragmatists.

Have your AI-loving sales manager test activities for account research, document the time savings, and refine the prompts until they work consistently. Have your data-savvy analyst test AI for report generation, measure the quality improvements, and refine templates others can follow.

Proven success with AI workflows becomes the bridge. When pragmatists see specific colleagues achieving measurable results with AI in similar roles, they're willing to try the same proven approach. The early adopters' experimentation becomes the majority's roadmap.

On-the-job activities work because they solve the core problem: people are hesitant to experiment with AI, but they need hands-on practice to build confidence and skills.

Once early adopters validate an activity—better prompts, clear outcomes, measured benefits—it's ready for the majority. Pragmatists then have the peer proof, specific instructions, and predictable results they need.

Why this approach helps you cross the chasm:

• Reduces risk: Activities are tested and proven by peers
• Provides context: Always related to actual job responsibilities
• Enables practice: Hands-on experience without high-stakes experimentation.
• Leverages cognitive science: Fits with how people actually learn.
• Shows value: Clear connection between AI use and work improvement
• Builds confidence: Success with structured activities leads to broader adoption

Implementation can be scaled through technology platforms that deliver personalized activities across departments, though L&D teams can also deploy manual approaches for smaller initiatives.

The acceleration effect: When pragmatists successfully complete AI activities and see immediate workplace benefits, they become advocates for broader adoption across their teams. This transformation from individual users to advocates follows proven principles of workplace behavior change.

After you’ve got the activity feedback and proven success from your early adopters, it’s time to cross the chasm.

The structure: One AI activity per week for 12 weeks. Each activity requires participants to use AI for actual work tasks—analyzing data, drafting communications, or streamlining processes. Before and after surveys measure skill development and confidence levels.

Manager involvement: At the halfway mark, a one-on-one meeting helps managers understand what's working and provide support. This also creates accountability and keeps up momentum.

The practice effect: Twelve weeks of consistent practice builds genuine AI skills and confidence. Habits actually take 10+ weeks to develop, not just 21 days.

After 90 days, your AI adoption is firmly into your majority—you’ve crossed the chasm. New habits are set, and the behavior change is measurable through the before and after surveys. This measurement approach demonstrates how learning initiatives can drive measurable business outcomes. And measurably improved business outcomes with AI is the ultimate point.