How to Train your Team on AI: Scalable Methods and Use Cases

1. Why training your team on using AI is a priority today

We’ve entered a new era. AI isn’t coming, it’s already here. And it’s not going away.

From content generation to data analysis, automation to language translation, AI is quietly (but radically) transforming the way work gets done. Across departments, it’s already replacing repetitive, low-value tasks with faster, smarter processes.

The companies that embrace this shift gain a critical edge. They save time. Reduce errors. Move faster. Free their teams to focus on what really matters: creativity, strategy, and decision-making.

But those who hold back? They’re not “playing it safe”. They’re choosing to forgo a massive opportunity for efficiency and innovation.

Giving your teams access to AI tools is no longer a luxury. It’s a responsibility. It’s a way to remove friction from everyday work, and to empower employees to spend their time on the tasks that truly deserve human insight.

But here’s the catch: tools alone don’t drive transformation. People do.

That’s why training your teams is step one. Because without the skills, confidence and mindset to use AI well, the tools just sit idle. Or worse, they’re used carelessly, reinforcing bad habits or compliance risks.

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What to expect in this guide :

In this article, we’ll walk through how to design an impactful AI training program, and the internal enablers you’ll need to scale adoption across your business.

From hands-on exercises to smart governance structures, we’ll share the approach we use to turn curiosity into capability.

Ready? Let’s build the future of work, one prompt at a time.

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2. What employees can do with AI: Use cases that matter

One of the biggest blockers to adoption is that AI still feels abstract. Training needs to ground it in the real world. That starts by showing teams the tasks they can already automate or accelerate using tools like ChatGPT, Copilot or Notion AI.

Here are a few practical use cases that resonate across teams:

• Marketing & Comms: write LinkedIn posts, summarise reports, localise content into multiple languages, create first drafts for emails or campaigns.
  
• Retail & Customer Care: respond to FAQs, summarise customer feedback, generate call scripts, translate product descriptions.
  
• HR & Legal: draft job ads, summarise CVs, rephrase legal clauses, write interview recaps.
  
• Finance & Operations: clean up Excel data, check formulas, generate dashboards, translate metrics into insights.
  

In every case, AI is not replacing the team. It’s a starting point. It drafts, organises, translates, rewrites. But it’s the human that stays in control. This is the mindset we want to instil.

If your teams can clearly picture how AI helps them save time or improve output, they’ll start using it with purpose.

Next step: helping them identify which use cases to prioritise, based on impact and feasibility.

3. Training people to prompt like pros: The CRAFT method

If you want your teams to get meaningful results from generative AI, you have to go beyond the basics. Most frustrations with tools like ChatGPT come from unclear or incomplete instructions.

Enter the CRAFT method, a simple structure we use in training to design better prompts:

• C – Context: What’s the situation or background?
  
• R – Role: Who is the AI supposed to act as?
  
• A – Action: What do you want it to do?
  
• F – Format: What should the output look like?
  
• T – Tone: How should it sound?
  
  

For example:

"You work in a healthcare company as a recruitment specialist. Write a job ad for a marketing analyst. Make it concise and friendly, formatted as a LinkedIn post."

This is vastly more effective than “Write a job ad”.

Once you're done with the CRAFT Method, move on to advanced prompting techniques:

• Iterative prompting (refining responses step-by-step)
  
• Reverse prompting (asking the AI how it would answer something, then adjusting)
  
• Style transfer (mimicking brand tone or voice)
  
• File-based prompts (working with PDFs, Excel, etc.)
• Many more
  

In training, the most powerful moment is when people see how small tweaks can dramatically improve outputs. Prompting is a skill – and with a few simple rules, anyone can learn it.

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4. Practical AI Exercises: Build Skills Through Hands-on Learning

To truly embed AI into team workflows, training should move beyond theory. Practical exercises are where understanding turns into capability, and where real engagement begins.

We recommend two types of exercises to maximise learning impact: one individual, one group-based.

1. Individual Prompt Creation (Solo Work)
Start with a solo exercise. Each participant is invited to draft their own “base prompt”. A reusable instruction for a task they often perform. This could be rewriting an email, summarising a report, or generating a LinkedIn post.
The goal here isn't perfection. It’s about ownership: making participants think through how they want to brief an AI tool, and reflect on what works, what doesn’t. Once done, they can test and iterate on their prompt using their preferred AI tool.

2. Group Challenge: Prompting with Creativity and Iteration
Next, move to group work. Form teams of 3–4 people. Give them a challenge: use prompting techniques and multiple iterations to achieve an output together. Encourage them to go further than just generating text.

For example, teams might:

• Rephrase an internal email
• Translate it into another language
• Create an image to go with it
• Turn the content into a PDF report
• Extract key actions and write a summary for Slack

Remind teams this is not about “getting it right” on the first try. It’s about exploration: testing, tweaking, and learning by doing. The best outcomes often come from bold and playful experimentation.

Meanwhile, the trainer should actively circulate between groups. Play the devil’s advocate. Challenge their prompt structure. Suggest an unexpected model or technique. Push for more creativity, and more iteration.

This guidance is key to unlocking the full power of generative AI in a safe, hands-on environment.

By the end, participants will have built not only stronger prompting skills but also a deeper confidence in applying AI to real work scenarios.

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5. Secure and Accessible AI Tools for Everyone

Once the training is complete, it’s crucial not to leave teams in the dark. The next step is about enabling them with the right environment to apply what they’ve learned. That starts by giving access to AI tools—securely and at scale.

Many organisations hit a wall when they finish their AI training but don’t provide a sanctioned platform for employees to use. This often leads to one of two outcomes: either the momentum fades, or teams turn to public tools that are not secure.

To avoid this, companies should provide a centralised, enterprise-level AI solution—like ChatGPT Enterprise, Microsoft Copilot, or any AI assistant integrated within their existing tech stack. This ensures that employees have a safe environment in which to experiment with AI using real, sometimes sensitive, internal documents.

Giving teams access to a secure tool is not just about compliance—it’s about relevance. If employees can't apply AI to the documents, decks, or data they work with daily, it all remains theoretical. Let them experiment with reports, financial projections, customer service scripts, or internal memos. Real use cases = real impact.

At MyDigipal, we've created a list of top tools we recommend you using: LINK HERE

Bottom line: if you want people to integrate AI into their day-to-day work, give them the playground and the toys—safely.

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6. Build an Internal AI Council to Drive Advanced Adoption

Beyond individual usage, every AI-literate company needs a team responsible for developing more complex, custom solutions. That’s where an AI Council comes in.

This cross-functional group could include internal profiles already working on data, analytics, or automation, or new hires with foundational AI knowledge. Their role is to go beyond prompting and experiment with advanced integrations: API connections, autonomous agents, workflow automation, and more.

Their job is to listen to employees’ pain points and identify bottlenecks that AI can solve. Think: automating invoice processing, triaging customer emails, generating insights from Excel sheets, or streamlining internal reporting processes.

They should act like internal consultants. Each project should follow a simple cycle: listen to users → build → test → gather feedback → iterate.

This doesn’t mean building everything from scratch. Often, existing AI tools can be layered with APIs or automated scripts to create powerful, workflow-specific solutions.

Over time, this internal team becomes a critical driver of adoption. The more tasks they automate, the more teams are freed up to focus on strategic work. It’s also a tangible way to turn AI from an abstract concept into a force multiplier for business efficiency.

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Conclusion: From AI training to long-term impact – how to make it stick

Bringing AI into an organisation isn’t just a matter of tech deployment. It’s a transformation in how people think, work and create value.

With the right training, teams move from passive users to proactive experimenters. They start seeing AI not as a threat, but as a collaborator. One that helps them write faster, decide smarter, and build better.

But impact doesn’t happen overnight. The companies that truly succeed are those that treat AI enablement as a journey, not a one-off workshop.

Here’s what that journey looks like:

• It starts with relevance. Use cases that resonate with each team’s reality.
• It grows through capability. Structured training in prompting and advanced techniques.
• It scales with support. Tools made accessible in secure environments.
• It deepens through collaboration. Group challenges, peer learning, internal champions.
• It accelerates with structure. An internal AI Council focused on solving real workflow issues with real AI solutions.

More than anything, adoption sticks when employees feel confident. When they’re given the right guardrails—and the freedom to explore.

So don’t stop at the training. Build the culture around it. Celebrate success stories. Track usage. Iterate continuously. That’s how AI becomes more than a buzzword. That’s how it becomes business as usual.

At MyDigipal, we help organisations embed AI where it matters most: in people’s daily work. Because real transformation starts at the team level—and scales from there.

Get in touch to find out more about our training services: Contact us page

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