Editorial
Every week, I meet organizations that say they need "company-wide AI training, as soon as possible." A few months later, a few employees are the A+ students of AI adoption, and the rest are using AI as a writing aid, not in their actual workflows. The training happened. The adoption didn't.
When people don’t use new technology, we assume they don’t understand it. So we schedule an enablement session. Book the meeting room. Record the session. Share a few “helpful resources” links that never get clicks. A checkbox for the enablement team; little AI ROI.
The issue is that the enablement treats adoption as an awareness problem. If people just knew what the tool could do, the thinking goes, they'd use it. However, awareness is rarely the constraint. Most people already know the AI tool exists. They've seen the announcement in one of the important channels. Some have even tried it once or twice.
What they lack isn't awareness. It's use-cases, habits and infrastructure.
There are two outcomes worth aiming for in order to drive adoption. First, make the AI already in your stack usable every day. Second, when the basics hold, co-build a few real workflows where the ROI is obvious.
AI Training Teaches Features. AI Adoption Requires Use-Cases.
The most common request I hear: "Can you train our team on what the tool can do?"
Wrong question. A much better question is: "What problems are costing us time every week?"
Training shows tool capabilities. "Here's how to use AI for summaries. Here's how to search across tools." People nod along. Then they go back to their desks and keep working exactly the way they did before. The training didn't tell them which of their actual problems to solve first.
Use-case-driven enablement works differently. You start by asking what wastes time. Where do people spend 20 minutes hunting for documents? What questions get asked in Slack three times a week because the answer isn't written down?
Pick one problem per team. Make it specific enough that you can test if it's fixed.
Good use cases are repetitive, painful and measurable. Customer support needs a searchable database of common issues. Finance needs a template for monthly reporting. Product needs design decisions in one place so new people can onboard without scheduling six meetings.
Now put your use case to the test. Ask three people: if this is set up tomorrow, how often would you use it? If the answer is "maybe once a month," it's not painful enough. If the answer is "every day," build it. Check back in a week.
Training says, "Here's what you can do." Use cases say, "Here's what problem you'll solve." One creates awareness. The other creates adoption.
Training Happens Once. Habits Require Repetition.
Training assumes that once people click through a deck, they’ll adopt the change. We’re professionals with sophisticated titles our distant relatives don’t understand, but more than anything, we’re still creatures of habit. We open the same 25 tabs. Ping the same three colleagues. Follow the same click path. Breaking autopilot takes more than a 60-minute session.
Training is an event. Habit formation is a system. The teams that train the hardest often see the lowest adoption. The teams that build nudges into daily workflows are rewiring their corporate neuropathways.
The organizations that change behavior add small prompts inside tools people already use. One example is when someone drops a policy link in Slack, a small prompt asks to file it in the team hub, add an owner, and set a review date. Another example, when someone asks a Corporate Travel Policy question in the HR channel, a bot nudges them to query the AI search in the company wiki. A gentle hint to stop wasting time on virtual ping pong, bouncing wiki links back and forth.
Training Assumes Infrastructure Is Ready. It Usually Isn't.
Here's the most common failure pattern. A company rolls out AI capabilities. They schedule training. Attendance is strong. Then someone tries to use it and runs into a lack of integrations, siloed knowledge, sync and permissions errors.
Training on top of broken infrastructure is a waste of time, money and employee morale.
The organizations that succeed do a few weeks of boring setup work before any training. Set up and validate permission groups. Connect the integrations and test with a few beta testers. Create templates tailored to common workflows. Pick and collaborate with champions per department.
Then people can actually use what they're learning. Not in theory. In practice, right now, on their real work.
Co-building Workflows That Drives Adoption
Once the basics and nudges are in place, move to co-building. The pattern in successful rollouts is consistent. No big training event. Instead, identify champions. One or two per team. Not by seniority. By willingness and credibility. Those champions get hands-on time building real use cases. Not "here's how the tool works." More like "bring your workflow, let's build this together."
Then the classic training, but follow it with team sessions. Department champions run short sessions with their teams. Not features. Application. The Finance champion walks through the monthly report they automated. People learn by watching someone they trust solve a problem they recognize.
These companies track behavior, not sentiment. Who's using it weekly? Which workflows are getting activity? Where are people stuck? Keep the feedback loop. One company ran training twice. Usage stayed at 8%. The behavior data showed why: their critical workflow required accessing a legacy system that wasn't integrated. Two days of API work fixed it.
This is what enablement as a system looks like. Accessible documentation. Empowered champions. Use-case libraries. Chat channels where questions get answered fast. Short recorded clips. Office hours. Shared success stories.
Start Smaller Than Training
If you're planning company-wide AI training right now, stop. Try this instead.
Pick two or three teams. Ask what wastes their time. Build the simplest version of a solution for one specific problem. Test it with three people for a week. If they use it, expand. If they don't, find out why and fix that first.
Get the infrastructure right. Make sure integrations are connected and the baseline experience actually works. Then have champions show their teams how they're using it to solve real problems. Track who's using it and for what. Adjust based on behavior, not surveys.
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This is slower than booking a conference room and running an enablement session. But it's honest. And it works.
Training isn't adoption. Training is what we do when we don't want to do the harder work of identifying real problems, building infrastructure, and creating systems that change behavior.
Stop scheduling training. Start solving problems.
Editor's Note: For more tips on improving AI adoption:
- What AI Upskilling Looks Like at Every Level of the Organization — A 3-tiered approach to AI upskilling for leaders, managers and individual contributors.
- EZCater's Mark Christianson on Building an AI Mindset to Drive Adoption — EZCater's senior manager, digital workplace and AI strategy discusses their efforts to encourage AI adoption, with the goal of making every team an R&D hub.
- Round Pegs and Square Holes: Why AI Adoption Requires a Focus on Culture — AI’s impact isn't inherent in the technology itself but in how it is deployed. Will it be a means to cut corners, or a catalyst for growth and innovation?
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