How to Get Your Team Actually Using AI (Not Just Talking About It)

There's a growing gap in most organisations: everyone's talking about AI, but very few are actually using it day-to-day. You might have a handful of enthusiasts experimenting with ChatGPT, while the rest of your team carries on as before. Sound familiar?

The challenge isn't awareness—most people know AI exists and could probably help them. The challenge is turning that awareness into consistent, practical usage. And that requires more than sending round a few links or hoping people figure it out themselves.

When left to their own devices, most employees hit the same barriers. They try an AI tool once, get a mediocre result, and conclude it's not worth the effort. Or they're simply too busy with their actual job to spend time learning something new. Without structure and support, AI remains a curiosity rather than a capability.

• Initial experiments produce disappointing results (usually due to poor prompting)
• No time carved out specifically for learning and practice
• Uncertainty about which tools are approved or appropriate for work
• Lack of relevant examples for their specific role
• No one to ask when they get stuck

Successful AI adoption requires two complementary elements: structured learning moments and ongoing reinforcement. One without the other rarely sticks. Live training creates the initial momentum and skill foundation, while continuous sharing keeps AI top-of-mind and builds a library of practical examples.

Prong One: Synchronous Learning (Live Sessions)

Hands-on workshops where people actually use AI tools together—not just watch a presentation about them. These sessions should be practical and role-specific. A marketing team doesn't need the same training as your operations staff. The goal is for everyone to leave having done something useful with AI, not just having heard about what's theoretically possible.

Prong Two: Asynchronous Learning (Ongoing Channels)

Create a dedicated space—a Slack channel, Teams group, or regular email digest—where people share what's working for them. This serves multiple purposes: it provides a constant stream of practical examples, normalises AI usage as part of everyday work, and creates peer pressure in the best sense. When colleagues see others getting results, they're more likely to try it themselves.

• Encourage sharing of wins, however small
• Document prompts and workflows that work well
• Create a searchable library of use cases
• Celebrate experiments, even failed ones—they're still learning

Every successful AI adoption has someone driving it. This doesn't need to be a technical person—in fact, it's often better if they're not. What matters is enthusiasm, willingness to learn publicly (including sharing mistakes), and the time to support colleagues. In larger organisations, you might have champions in each department. In smaller teams, one committed person can make all the difference.

One of the biggest blockers to adoption is uncertainty about what's actually allowed. Can I put client data into ChatGPT? Which tools has the company approved? What about sensitive financial information? Without clear answers, cautious employees will simply avoid AI altogether.

Work with your legal, security, and finance teams to establish clear guidelines. Which tools are approved? What types of data can and can't be used? Are there specific prompts or workflows that have been vetted? Document this clearly and make it easily accessible. Remove the ambiguity that causes hesitation.

You can't improve what you don't measure. But measuring AI adoption isn't as straightforward as tracking login counts. Some useful approaches include:

• Regular sentiment surveys: How confident do people feel using AI? Has this improved?
• Usage frequency: How often are approved tools being accessed?
• Quality of sharing: Are people posting useful examples in your AI channel?
• Business impact: Can teams point to specific tasks that AI has made faster or better?

You don't need a massive initiative to begin. Start with these practical steps:

The key is to start small but start structured. Random experimentation rarely leads to organisation-wide adoption. But a clear framework, even a simple one, gives people the confidence and direction they need to actually give AI a proper go.