Companies are investing heavily in AI. The tools keep improving, and employees have more access to them than ever before.
Yet many companies are struggling to turn that investment into meaningful results. Some employees use AI throughout their day. Others have tried a few prompts, gotten mixed results, and returned to their usual routines. Giving everyone access was the easy part. Helping people use AI well takes more work. A big part of that work comes down to AI fluency.
What is AI fluency?
AI fluency is the ability to use AI confidently and effectively in your everyday work. It involves knowing what AI can help with, asking useful questions, and evaluating the answers.
You don't need to understand how large language models are built or become an expert in prompt engineering. You need to know how to work with AI.
Learning to use AI is a bit like learning a new language. In the beginning, you spend time searching for the right words and figuring out how to express yourself. With practice, conversations become easier. You focus less on the mechanics and more on what you're trying to communicate.
The same applies to AI. As you gain experience, you learn how to provide context, ask better questions, and improve the results. You also start noticing opportunities to use AI in tasks you've been doing the same way for years. Eventually, using AI becomes a familiar part of your workday.
Giving people AI tools is only the beginning
Imagine a company that has just rolled out Claude to its employees. Everyone gets access. There's an introductory training session. People experiment with a few prompts and come away impressed.
Then Monday morning arrives. Emails need answers. Meetings fill the calendar. Reports are due.
Without a clear understanding of how Claude fits into those responsibilities, many people return to their usual habits. A few employees will experiment on their own and discover useful ways to incorporate AI into their work. Others may not know where to begin. They might have questions about what information they can share, whether the answers are reliable, or how much time they should spend experimenting.
Another demonstration of AI's capabilities probably won't solve those problems. People need practical examples that connect AI to their responsibilities.
Consider someone preparing for a client meeting. They might spend an hour reviewing emails, reading past meeting notes, researching the client's business, and organizing their thoughts.
Claude could help summarize the material, identify questions worth asking, and prepare a meeting brief. The employee would still review the information and decide what's relevant. But they could spend less time organizing material and more time preparing for the discussion. Experiences like that give people a reason to keep using AI.
Three skills that help people become AI fluent
AI fluency involves three skills worth developing.
1. Learning how to work with AI
The first step is becoming comfortable with the tools. That includes writing clear prompts, providing context, asking follow-up questions, and improving responses.
Say you're preparing a follow-up email after a client meeting. You could ask Claude to "write a follow-up email." You'll get a draft, but it may be too generic to use.
Instead, explain who the client is, what you discussed, what decisions were made, and what needs to happen next. The result will likely be much closer to what you need.
With practice, you become better at explaining tasks and guiding AI toward useful results. You also learn that you can keep working with AI after the first response rather than starting over.
2. Recognizing where AI fits into your work
Once you're comfortable with the tools, the next step is identifying where they can help. Look at how you spend your workday. How much time goes into reading information, researching topics, writing emails, or preparing reports?
We organize these activities into three categories.
- Understand (Insights): Reading and processing information to identify what matters. Examples include summarizing reports, reviewing meeting notes, and finding decisions or action items in email conversations.
- Explore (Research): Gathering information and exploring ideas before making a decision. This could involve researching a prospective client, comparing approaches to a business problem, or learning about an unfamiliar topic.
- Produce (Create): Turning information and ideas into something useful. Examples include drafting emails, building presentations, preparing proposals, and outlining reports.
Most people move between these activities throughout the day. Recognizing them makes it easier to identify tasks where AI might help. Start with a few activities that take up a meaningful amount of your time. Experiment and see which approaches work.
3. Knowing when to trust the results
AI can produce a convincing answer that contains incorrect information. A report might include an inaccurate fact. A summary could leave out an important detail. A recommendation may sound reasonable but overlook something critical.
AI fluency includes knowing how to spot those problems. Ask whether the information is accurate, whether the response answers your question, and what might be missing.
For important decisions, verify facts against reliable sources and involve the right people.
There will also be tasks where your own judgment and experience matter more than anything AI can provide. Knowing where those boundaries exist is part of becoming a capable AI user.
How can organizations build AI fluency?
Introductory training helps people get started. Developing confidence takes practice and support. A few approaches can make training more effective.
Start with real work
Ask employees to bring a task they're already working on. It could be a report they need to summarize, a presentation they're preparing, or a problem they're trying to solve. Using familiar work makes the training immediately relevant. Employees can see what AI does well, what needs improvement, and how they might use it again.
Give people room to experiment
The first response won't always be useful. Sometimes people need to explain a task differently, add information, or try another approach.
Give them opportunities to experiment, ask questions, and learn from what doesn't work. Confidence develops as people become familiar with the process.
Encourage people to share what works
Someone in marketing might discover a useful approach to competitor research. Someone in finance may find a better way to analyze a spreadsheet.
Give them opportunities to demonstrate what they've learned. A short example from a colleague can help others find new ways to approach their own work.
Include leadership
Leaders need to understand how AI can help their teams and where it could change the way their organizations operate. They also need to recognize its limitations and make informed decisions about where to invest.
Including leadership in training helps build a shared understanding of what's possible and gives employees support as they try new approaches.
Continue learning beyond the first session
Questions will come up once people begin using AI at work. They'll encounter tasks where the results aren't useful or discover opportunities they want to explore.
Follow-up sessions, practical exercises, and opportunities to ask questions can help people continue developing their skills.
At Trackmind, we've built our AI Foundations program around this approach. We work with leadership and employees to develop practical Claude skills using the work they already do.
Start with the work that's taking too much time
A useful place to begin is by asking your team what takes longer than it should.
Your sales team may spend hours preparing for client meetings. Managers might devote significant time to reviewing reports. Employees across the organization could be spending too much time gathering information before they can make decisions.
Ask people to identify a few activities they'd like to improve. Then help them experiment with AI and evaluate the results.
Some approaches won't work. Others may save time, improve the quality of work, or help people solve problems differently.
As employees become more comfortable, they'll begin finding new opportunities on their own. They'll ask better questions, explore different approaches, and share what they've learned. Over time, AI becomes part of their regular work.
The last mile comes down to people
AI technology will continue to improve. New models and capabilities will keep arriving.
But having access to those capabilities doesn't mean employees will know how to use them. People need opportunities to learn, practice, and develop confidence.
At Trackmind, our AI Foundations program helps organizations build those skills.
We work with leadership to explore where AI can make a difference and help employees develop practical Claude skills they can use in their daily work. Our training focuses on familiar tasks, hands-on learning, and continued support.
If your organization has already invested in AI, helping your people use it well is a practical next step.
