AI Adoption Isn’t a People Problem. It’s a Design Problem.

You know it by now:

Someone complains about AI at work.
Leadership sends them to “the AI training.”
They sit through 2 hours of slides, leave with a certificate… and nothing in their day changes.

It’s not that they’re resistant.
It’s that the training was built for someone else’s way of learning.

If you missed the first part, here's the short version: AI adoption isn't a generational divide, it's a learning style divide. Check it out for the analysis breakdown and a detailed personal assessment here

Some people experiment first. Others need to observe, understand the mechanics, or see a clear purpose before acting.

All approaches are valid.

Today, we're fixing that.

Whether you're leading a 5-person startup team or managing a department in a large company, here's how to design AI adoption that fits the people who work with you.

When organizations roll out AI, they usually pick one approach:

"Here are the tools, go experiment!" (Works for experimenters, alienates everyone else)

"Here's mandatory training for everyone." (Works for comprehenders, bores the experimenters)

"Here are case studies from other companies." (Works for observers, frustrates the hands-on learners)

"Here's the business case." (Works for purpose-driven folks, leaves people without practical next steps)

Then leadership wonders why adoption is uneven.

Why some people embrace AI while others resist.

Why the same training produces wildly different results.

Most AI rollouts fail because they're designed for one learning style and expect everyone else to adapt.

Here's how to design for all approaches:

When faced with a new AI tool at work, what's your first instinct? how is it different or similar to the instinct of people around you? Take the quick personal assessment broken down in this edition.

What it looks like:

Why it works: Experimenters need permission to try and fail. Give them space, then capture what they learn.

What it looks like:

Why it works: Observers need proof it works before they'll try. Show them, then support their first attempts.

What it looks like:

Why it works: Comprehenders need mental models. Give them deep understanding, then they'll apply it confidently.

What it looks like:

Why it works: Purpose-driven folks need to see "why this matters" before "how to use it."

Once you've created multiple pathways, you need three things to make adoption stick:

Each style needs different support to feel safe experimenting with AI:

Action: In your next team meeting, ask: "What do you need to feel comfortable with AI adoption?"

Bad metrics favor one learning style:

Better metrics:

Best question: "Can everyone articulate when to use AI and when not to?"

Your AI adoption accelerates when different learning styles work together:

Pair experimenters + comprehenders:
Discovery meets documentation

Connect observers + purpose-driven:
Proof points meet practical application

Create the feedback loop:
Experimenters test → Comprehenders document → Observers validate → Purpose-driven folks scale

Everyone contributes their strength. No one has to become someone else.

We'll explore measurement frameworks, cross-functional team design strategies in a future edition.

… and realizing your current AI rollout was built for only one learning style, you’re not alone.

This is the work I do with leaders and teams: building multi-path AI adoption, readiness and governance programs that fit how your people actually learn, not how a generic training assumes they do - through tailored roadmaps, workshops, and coaching.

Team size matters too. A 5-person startup, 40-person function and a 400-person division each need a different implementation path to optimize resources, adoption velocity, and ROI.

If that’s something you’d like to explore for yourself and your org, hit reply with “AI readiness” and I’ll send details.

Hit reply and tell me: Which learning style does the current AI strategy in your team inadvertently favor? Who's being left out?

I read every response, and I'll share patterns (anonymously).

P.S. If you found this helpful, forward it to someone on your team/network who learns completely differently than you do. They might finally understand why you approach AI the way you do.