The First Step to Scaling AI is People
In Part 1, we explored why the AI models chat and agents are built with are fundamentally designed for individuals to be productive. That reality creates a challenge for organizations trying to scale AI adoption across teams.
But even when employees share the same objective, they may have different workflows, habits, problem-solving approaches, and levels of comfort with technology. Sometimes alignment is possible through training and iterative user feedback exercises. More often the alignment challenge is compounded by each team’s work being just different enough that a one-size-fits-all AI solution falls short.
If you’re struggling to find this balance, this isn’t a sign that your organization is behind. It’s a sign that organizations are complex.
The good news is that scaling AI successfully isn’t primarily a technology challenge. It’s a people-first strategy shift.
I promise that your organization has more AI savvy and AI curious employees than you realize. Channeling this curiosity will enable real AI value to enter your workflows.
Cohorts Create Momentum
One of the most effective ways to channel employee curiosity and expand AI adoption is through cohorts: peers within the organization who learn, experiment, and solve problems together.
A strong cohort provides:
- A safe environment to explore AI and candidly discuss experiences
- Shared accountability, momentum, and ideation
- Opportunities for peer-to-peer learning and networking
The goal of a cohort isn’t to create another recurring meeting that people attend out of obligation.
The goal is to empower, develop, and grow (scale) AI expertise internally, organically.
Empower
Initial cohort participants should be a mix of individuals who are already finding value from AI and those who are AI curious. Organization leaders should provide loose guardrails and the organizations’ strategic goals that are desired to be influenced through AI adoption, and encourage cross-cohort connection. Leadership vision should center the cohorts around a common “what”, while empowering individual cohorts with the resources and creative freedom to explore the “how” that most resonates.
Develop -> Scale
Cohort members should quickly show growth of their AI confidence thanks to the hands-on usage and real problem solving the cohorts encourage. This momentum becomes self-sustaining when these skills are encouraged to be shared outside of the cohort, such as:
- Transparent Q&A sessions with others who would benefit but are resistant to change
- Establish themselves as a subject-matter expert
- Spotlight in or observe another cohort meeting
- Present about what experiments have gone well and which haven’t
However, do not make cohort members solely responsible for broad user adoption – cohort members can be great resources to facilitate user training, but they should always be in a position where they may speak candidly, as their credibility is centered around building and maintaining a culture of trust.
A lack of trust is one of the largest hurdles to user adoption of AI, so prioritizing transparency throughout will reward you with right-sized scale in the end. Crucially, change resistors need to have a safe space to build trust in AI if they are expected to adopt AI-enabled workflows, and will be far more likely to change their behavior when they see someone like them succeed.
When employees watch peers reclaim time, solve frustrating problems, or improve the quality of their work with AI, curiosity naturally follows. Confidence grows. Adoption becomes less about compliance and more about opportunity.
The result is that organizations move beyond isolated “islands of productivity.” Instead, they create networks of employees who share ideas, adapt solutions, and spread successful practices across teams.
Over time, innovation becomes part of the culture rather than a special project.
Signs of Cohort Maturity
A mature AI adoption model doesn’t require every employee to dedicate part of each day to AI initiatives.
In fact, that’s typically neither realistic nor sustainable.
Early cohorts often include a large number of participants. Over time, the structure naturally evolves. Some participants become cohort leaders, helping identify new opportunities and maintain successful solutions. Others become non-members, who are still engaged to contribute when their expertise is needed, participate in targeted training, and provide feedback that helps improve outcomes.
Mature cohorts don’t live within themselves, but leverage the enterprise connections, process understanding, and goal understanding to constantly be driving and evolving progress for the organization
The key is that trust is best built internally, over time, through peers.
Building a Culture That Scales
Mature cohorts not only critically enable AI trust and valued solutions in the organization, but create benefits that extend far beyond AI adoption. to corporate culture itself.
Cohorts can influence culture by:
- Creating new opportunities for leadership and visibility
- Growth in presentation and facilitation skills
- Building cross-departmental connections and credibility
- Reduce fear and resistance to change by meeting employees where they are
- Prevent overreliance or blind trust in a single “silver bullet” solution
- Migrate vocal resistors to vocal contributors
- Create pathways for highly engaged employees to lead future initiatives
Most importantly, they reinforce a culture of participation, ownership, and learning.
In Summary
Organizations scale AI successfully when they focus on evolution, not displacement.
Because AI doesn’t scale organizations.
People do.
When employees feel valued, supported, and included, they become less resistant to change because they’re helping drive it.
That’s how individual AI wins become organization-wide success.
And as a bonus, you might just build the kind of culture people can’t stop talking about.
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