7 Ways to Get Your Team on the Same Page About AI

A new hire may bring AI habits from a previous company. A longtime employee may have found a few uses that fit the way they already work. Both could be getting value from AI, but they may use different tools, write prompts differently, and apply different standards to the results.

That variation makes it harder to build AI skills across a team. People repeat work that a colleague has already figured out. Managers may also have no clear way to judge whether AI is helping or creating more work to review.

You can give people a common starting point without expecting them to use AI for every task in the same way. Here are seven places to start.

Summary

1. Define your AI environment

Start with the tools. Which AI platforms are approved? What can employees use them for? Are there tasks or types of information that require a different process?

People need answers they can use in the middle of a workday. For example, a marketer preparing a campaign brief should know whether they can use AI to organize their notes, which materials they can upload, and where the finished work needs to live. Clear guidance removes guesswork and gives managers a consistent answer when questions come up.

2. Set working standards

Access to a tool does not tell people what a good result looks like. Set expectations for checking facts, reviewing recommendations, protecting sensitive information, and deciding when a person needs to make the final call.

Make the standards specific to the work. A first draft of an internal meeting agenda and a customer-facing claim need different levels of review. If your guidance only says “use AI responsibly,” employees still have to interpret that instruction on their own.

3. Identify your best use cases

Ask teams where they spend time, what slows them down, and which tasks involve repeated steps. Then choose a few applications worth teaching well.

For a marketing team, that might include turning a product briefing into a draft campaign outline or comparing themes across customer interviews. For a sales team, it could mean preparing for an account meeting using approved materials. Show the steps involved, the input a person needs to provide, and how they should evaluate the output. A useful example teaches far more than a list of possible AI uses.

4. Build shared resources

Once a team finds an effective approach, make it available to others. Save the prompt alongside the context it needs, an example output, and notes on what to check before using the result.

This could become a small library of workflows, templates, brand guidance, and finished examples. Keep it practical. A resource someone can find and use in five minutes is more valuable than a large collection nobody maintains.

5. Make it easy to share what works

Someone in one department may have solved a problem another team is still working through. Create a simple way to capture those discoveries.

You could ask employees to bring one useful AI application to a team meeting each month. Ask what task they were trying to complete, what they did, what they changed after reviewing the output, and whether the approach is worth repeating. Those details help colleagues adapt the idea to their own work.

6. Level-set skills through training

Shared guidance works best when people have the skills to follow it. Start with a foundation in how AI works, how to prompt effectively, and how to assess the results. AI Foundations can provide that common language and baseline.

From there, training should reflect the work people do. Some employees need stronger skills in content creation or analysis. Others may be ready to build more complex workflows. Intermediate and mastery training give people opportunities to practice on relevant tasks, get feedback, and apply what they learn.

The aim is for everyone to understand the basics and for each team to develop the deeper skills that make AI useful in its day-to-day work.

7. Keep your approach current

Your approved tools, internal guidance, and most useful applications will change. Give someone responsibility for reviewing the resources, collecting questions, and updating training when needed.

Pay attention to what people actually use. If a template is ignored, find out why. If several teams keep asking the same question, the guidance may need to be clearer. Treat your approach as something you improve through use.

When people share a foundation, understand the standards, and can see good applications for their roles, they spend less time starting from scratch. Their individual skills begin to add up to a stronger capability across the organization.

Want to build AI skills for yourself or your team? Email [email protected] to explore training options.

FAQs about team AI training

Where should an organization start with AI training?

Start by clarifying approved tools and expectations, then establish a common foundation. After that, identify the tasks each team needs help with and choose training that lets people practice those applications.

Does everyone need the same AI training?

Everyone benefits from shared basics, including effective prompting and reviewing output. Training beyond that should reflect each person’s role, experience, and responsibilities.

How do you identify good AI use cases for a team?

Look for work that takes significant time, follows a repeatable process, or involves organizing and developing information. Test a specific task and evaluate both the time involved and the quality of the result.

What should go in a shared AI resource library?

Include approved workflows, prompts with enough context to use them, templates, examples, brand guidance, and review steps. Assign someone to keep the library useful and current.

How can managers tell whether AI training is working?

Look at whether people apply what they learned to real tasks. Review the quality of their work, the time and revisions required, and whether useful approaches are being repeated across the team.