Crossing the chasm - how to drive generative AI adoption in organizations
Are you looking to drive the adoption of generative AI in your organization?
There’s a story about farmers and corn seeds from 1943 that can help.
In the 1940s, hybrid corn was a trailblazing innovation in farming. Suddenly, there was a corn seed that was more resistant to pests and offered higher yields.
In retrospect, adopting this new type of seed seems blatantly obvious. But this wasn’t the case.
Researchers found that farmers varied wildly in how quickly they adapted to the new innovation. Based on their findings, the researchers divided people into five categories of innovation adoption:
1. Innovators: These are the first individuals to adopt an innovation like hybrid corn. They are willing to take risks and are often the smallest group.
2. Early Adopters: This group represents opinion leaders. They adopt new ideas early but carefully.
3. Early Majority: These individuals adopt new ideas just before the average person. They require evidence that the innovation works before committing.
4. Late Majority: They are skeptical of change and will only adopt an innovation after it has been tried by the majority.
5. Laggards: The last to adopt an innovation. They are bound by tradition and very skeptical of change
Research by Rogers (1962) on the diffusion of innovation in different groups can help us understand how new technologies like generative AI spread.
Later, one of the researchers, Everett Rogers elaborated on the concept of the “Technology Adoption Lifecycle” in his 1962 seminal book “Diffusion of Innovations”.
You can see how this applies to the adoption of generative AI, right?
Based on my work with dozens of clients to increase their generative AI adoption, the theory holds quite nicely for this year's version of hybrid corn.
Early Adopters like you have most likely already embraced generative AI. The key to broader adoption is to help the more skeptical Early Majority embrace the technology. This is what Geoffrey Moore called “Crossing the Chasm” in his book bearing the same name.
So, how do we do it?
Borrowing from the historical lens of Rogers and Moore and my experiences working with clients on generative AI, here are three principles for helping generative AI cross the chasm.
Offer education and support
The Early Majority is pragmatic about new technologies.
They’re hesitant to jump in unless they receive what Moore calls the “Whole Product.” They want a robust education program, support, and tools to help them get started. Unlike the innovators and early adopters, they’re not looking to browse Reddit to find instructions and hacks on their own.
To make the Early Majority feel comfortable getting started with generative AI, I recommend the following:
Start a thorough foundational training program on generative AI. It should focus on the basics of gen AI and its practical applications to everyday work.
Consider having training programs for different teams to ensure the takeaways are hyper-specific to their roles. My Designer 2.0 and Work 2.0 trainings have helped design and general knowledge work teams kickstart using gen AI.
Offer a hotline, office hours, or Slack community for support using generative AI. The Early Majority should feel confident they can access expert help if they encounter any issues.
2. Provide ready-to-use tools
Another part of the “Whole Product” package are the tools.
The Early Majority seeks a safe, true, and tested way of using generative AI. They’re not looking to start using tools that feel risky or require out-of-pocket expenses.
Consider buying an enterprise license to GPT-4 through Open AI or Microsoft.
This is important to access the state-of-the-art model and ensure data safety. Some organizations have created custom chatbots augmented with their own data to go a step further.
3. Build on early success stories
Finding and sharing early success stories is key to driving adoption beyond the Early Adopter.
As discussed above, the Early Majority seeks proof that this technology is the real deal.
To build on early success stories, consider:
facilitating a discussion to share how Innovators and Early Adopters have used generative AI successfully in their work
cystallizing the shared best practices in a “Generative AI Playbook” unique to your organization or team. It could showcase the best practices and guidelines for constructive and responsible use of AI in your field
identifying the most promising pilots of using generative AI to solve both customer and internal challenges
From corns to generative AI adoption
One thing is glaringly obvious as we examine the adoption of technology through the lens of history: It’s not about technology; it’s about human psychology—our attitudes, experiences, fears, and hopes.
I would argue that this is even more true for AI, a technology full of promise and, at the same time, perceived threat.
By understanding the dynamics of technology adoption in different groups of people, we can take a more nuanced approach to supporting the spread of generative AI responsibly. Engage the Early Adopters to offer support, education, and inspiring case stories for the Early Majority.
That new type of corn might look scary to the Early Majority, but with a robust support program, it might just make it.
