Lowering Friction

The fastest way to make adaptation more inclusive is to lower the friction at the exact moments when people decide whether to try something new. Not the heroic moments, but the small ones: “Do I sign up for this?” “Do I raise my hand?” “Do I dare touch this tool?” If those moments feel risky, confusing, or vaguely humiliating, only the already-confident will opt in. If they feel safe, guided, and worthwhile, many more people will. The key is to treat these friction points as design choices, not charity. You are not “being nice” to people who are behind; you are shaping an environment where learning and experimentation become the default for everyone.

Offering training is important and not enough organizations make it available. But long, abstract trainings that required a full afternoon and left everyone with a PDF can be overwhelming and difficult to fit into busy schedules. Replace this with small, modular experiences: 45-minute labs or one-hour sessions clinics where people get practical hands-on experience.

Design each module to stand alone, but also make them additive. After a handful of sessions and a bit of real use, someone should be able to credibly say, “I can set up a prompt library,” or “I can run a safe experiment with customer emails.” Map modules directly to the skills of your business and the roles people currently have so that they can experience how those roles are updated by applying generative AI to live work: a real message, a real process, a real decision.

This modular, do-then-document pattern can lower friction dramatically. People with busy schedules, caregiving responsibilities, or who just learn better in shorter bursts can opt in without rearranging their lives. Learning should stop being a special event and become something you can easily tuck into the rest of one’s obligations.

Of course, none of this sticks if managers are only rewarded for short-term output. Give managers a simple playbook for “learning sprints” they can run inside their own teams:

• Set a clear goal (“Reduce handling time on this process by 20% using AI support”).
• Protect a small amount of time each week for experiments.
• Capture whatever worked as a new rubric, prompt pattern, or checklist.
• Then broadcast the win: “Here’s what we tried, here’s what changed, and here’s the artifact you can reuse.”

Managers shouldn’t be expected to be AI experts; but they can be facilitators of experiments. A toolkit can give them language, templates, and examples so they don’t have to invent the format from scratch. The more comfortable managers became with these sprints, the more their teams felt permitted and expected to adapt.

People pay attention to what is measured, and the most powerful measurement is how we are compensated. To make it clear that shared learning is not “nice extra work,” tie a small slice of variable compensation to the creation and reuse of learning artifacts. Let people earn credit not just for hitting their own targets, but for producing things that reduced friction for others. It doesn’t take a huge pool of money. What matters is the signal: if you help the next person adapt faster, that’s rewarded. Over time, this quietly shifts the culture.

None of these levers requires a corporate revolution. They require a shift in mindset: from “Who is talented enough to keep up?” to “How do we make it easy for more people to try, learn, and leave the ladder down behind them?” Inclusive adaptation is about lowering the friction so more people can climb.