A Leader’s Guide to Upskilling Teams for the AI Era

Every week, we hear about new AI breakthroughs. AI models write code, create videos, or analyze data in ways we couldn't imagine just months ago.

But there's a gap: While most companies have adopted AI tools, the majority of employees still don't use AI in their everyday work. As a manager, you see AI's potential to change how your team works. Yet your employees struggle to figure out how AI fits into their daily tasks.

This isn't a technology problem-it's a people problem. Fixing it means changing how you approach training and adoption. It requires focusing on the people who will use AI, not just the technology itself. Here's how to build AI capabilities across your team that make a real impact, based on insights from leaders at PagerDuty.

1. Focus on people, not technology

Many organizations make the mistake of buying AI tools first, then trying to get people to use them. This backward approach leads to expensive software sitting unused while teams stick to old, unproductive ways of working.

The organizations that succeed take a different path. They start by working closely with their teams to understand daily frustrations, workflow bottlenecks, and tasks that drain time and energy. Then, based on those needs, they choose AI tools that directly support those challenges.

Instead of dropping in new technology and hoping it sticks, they treat AI as a teammate selected for a clear purpose: to take on repetitive work, speed up complex tasks, or remove friction from critical processes. When people see that the tool was chosen with their input and for their benefit, they're far more likely to trust it and use it.

2. Map your team's workflow to identify the best AI opportunities

Even with the right mindset, many teams don't know where to begin with AI. AI is most helpful when it targets repeatable, time-consuming tasks. But those vary by role and team. Without a clear workflow to apply it to, some teams don't use AI at all.

Create a simple map of your team's workflow. Talk to the people doing the work and ask questions like:

• "Which part of your job feels the most repetitive?"
• "What takes a lot of time but adds little value?"
• "If a junior employee started on your team today, what would you delegate to them?"

These answers highlight the best opportunities for AI. For example, engineers might spend hours tracing code dependencies; DevOps teams may waste time copying and pasting incident details for the post-incident review. Once you have a list, prioritize based on how often the task happens and how much time it takes.

3. Design learning around real problems, not abstract concepts

Most AI training feels disconnected from actual work. Teams learn how a tool works, but not how it solves their specific problems. As a result, people leave training sessions unsure how to apply what they've learned.

Build training around real tasks your team already does. Don't just teach "how to use ChatGPT." Teach "how to write a post-incident review draft in 5 minutes."

For example, if your engineering team spends a lot of time during incidents digging through wikis, tools, post-incident reviews, and docs for previous incident context, show them how GenAI could surface these insights via natural language prompts immediately. Try it out on a couple smaller, less critical incidents to build confidence.

Keep sessions small and practical. Aim for people to apply what they learned within 24 hours. If they can't, you may not have taught the necessary skills well enough.

4. Build transferable skills

While it's important to train people on the specific tools and workflows they use every day, it's equally important to develop broad AI fluency. Tools change quickly. If your team only knows how to use one platform, that knowledge can become outdated. They may also struggle to apply what they've learned to new tools or problems.

The real value comes from learning how to solve problems with AI, regardless of the platform. Focus on core, transferable skills like:

• Problem decomposition: Help people break complex work into smaller, manageable steps that AI can assist with. For example, instead of asking AI to "optimize our system," teach them to separate the task into monitoring, identifying bottlenecks, and generating recommendations.
• Quality evaluation: Teach your team how to judge whether AI output meets your standards. Provide examples and rubrics specific to your work so people can tell when the output is good enough and when it needs a human touch.
• Integration thinking: Encourage people to notice moments in their workflow where AI could help. This could mean speeding up documentation, summarizing and explaining code, or organizing backlog tickets-without replacing the overall process.
  

Give your team opportunities to practice these skills across different situations. The more they learn to spot patterns, the more confident they'll be using AI effectively no matter what tool they're working with.

5. Create ongoing support systems

One-time training isn't enough. Teams need time, space, and community to build confidence and get curious. Without continued support, people lose momentum, fall back into old habits, or feel stuck when problems come up later.

Set up simple systems that keep learning alive after training ends:

• Give time to experiment: Block off a few hours each week for teams to try using AI in their actual work. This could mean partnering with AI to write and review code, summarize project updates, or analyze system logs. The goal is to build habits like asking "Could AI help simplify this?" whenever they face a repetitive or time-consuming task.
• Encourage peer learning: Create a Slack channel, a shared doc, or a weekly show-and-tell where people can share what they've tried, what worked, and what didn't. These internal communities turn scattered wins into shared progress, allowing employees to benefit from each other's experiences.
• Bring in outside help when needed: Partner with AI vendors or training providers for deep-dive sessions tailored to your team's tools and goals.

6. Connect skill development to professional growth

If AI upskilling feels like just another task, people won't engage deeply with it. They may see it as extra work instead of something that benefits them. People are more motivated to learn when they see how it helps them grow.

Frame AI learning as a long-term investment in your team's future, not just a short-term fix for work problems. Explain how becoming fluent in AI can help employees:

• Take on more strategic responsibilities.
• Contribute to high-impact decisions.
• Grow into leadership roles.
• Stand out in the job market.
  

Make this connection explicit. Don't just say AI is important, show how it has already helped others advance. Share real examples of team members who've used AI to lead new projects, solve complex challenges, or unlock new roles.

7. Measure capability building, not just productivity

Many organizations focus too soon on productivity gains when adopting AI. This can create pressure to deliver immediate results and discourage teams from experimenting and learning. Building real AI skills takes time. Teams need space to explore, make mistakes, and grow their capabilities. If you only track outcomes like time saved right away, you miss the progress happening behind the scenes.

Start by measuring how your team is learning and engaging with AI. Track questions like:

• How many people are trying new AI tools?
• How often do they share tips and discoveries with each other?
• How much time do they spend building AI skills?
• What portion of workflow problems are they tackling with AI?

As your team's skills grow, shift your focus to business impact measures like:

• Time saved on repetitive tasks
• Better quality in work outputs
• Faster problem-solving
• More creative ideas and solutions

By tracking learning first and productivity later, you encourage ongoing growth and set your team up for lasting AI success.

The time to upskill your teams on AI is now

Organizations that invest in their teams' AI skills today give themselves a big advantage for tomorrow. These companies adapt more quickly as AI evolves, solve tough problems more effectively, and free up their people to focus on creative, high-value work. The organizations that take this seriously and intentionally invest in helping their employees build AI skills will lead their industries.

If you want to learn more, our detailed ebook, How to Drive AI Skill Development Across Your Operations Teams , offers practical frameworks, step-by-step playbooks, and real-world lessons from teams that have mastered AI adoption.