Preparing your team for AI adoption

Why AI readiness matters for your business

AI doesn’t just improve productivity: It changes how work gets done.

In many cases, the gains are immediate.

Even simple uses, like summarizing emails or organizing information, can save meaningful time.

"For office workers, 10% is very much the minimum productivity boost you can expect," says Trépanier. "Just being able to recap emails and meeting notes and organize information can create meaningful improvements."

In some roles, the impact is even greater. Trépanier notes that software development teams, which spend much of their time creating and working with text-based code and documentation, may achieve productivity gains of 30% or more.

But the upside isn’t the only story.

If you don’t prepare your team properly, the risks show up just as quickly.

• Lost opportunities
  

  If you invest in AI but neglect your people, those tools can go unused.

• Cultural risks
  

  Lack of clear communication breeds anxiety and resentment about AI’s role.

• Talent implications
  

  Younger workers expect AI-savvy employers. Without a clear AI strategy, you’ll struggle to attract and keep them.

• Security exposure
  

  Without strict policies, employees may unintentionally expose sensitive data.

AI doesn’t fail because of the technology.

It fails when people aren’t ready to use it.

Start training your team before deploying AI

One of the biggest mistakes organizations make? Waiting until AI tools are already selected or implemented before involving employees.

By then, you’re already behind.

"It can take months to years of practice. Someone who's never used AI is not going to learn it in a week," says Trépanier.

If you want real adoption, start earlier.

Make sure your team understands:

• why you’re adopting AI
• what business problems you want to solve
• how AI will change day-to-day work

When employees understand the context, they engage differently.

They also identify practical use cases that leaders might otherwise miss.

Address AI security and governance early

AI introduces new risks.

Some are obvious. Others aren’t.

Without clear guidelines, employees may:

• fall for increasingly sophisticated phishing attacks
• share sensitive information
• create software or workflows with hidden vulnerabilities

That’s why governance must be part of AI training before deployment, says Trépanier.

Make sure your team knows:

• which AI tools are approved for use
• what data can be shared
• when human review is required

Clear guardrails don’t slow adoption. They make it sustainable.

Focus on practical AI skills that create value

AI doesn’t deliver value in theory. It delivers value in everyday work.

That’s what your training should focus on.

Employees benefit most when they learn how AI can support their existing responsibilities and “automate the boring stuff,” says Trépanier.

For office workers, that might mean using AI to:

• draft first versions of documents
• summarize long or complex content
• automate repetitive administrative tasks

In other words: remove tasks that add little value.

"It's about understanding a person's work and finding opportunities where AI can help," says Trépanier.

Training should encourage employees to:

• start with small, low-risk applications
• experiment and build confidence
• identify more sophisticated use cases

And don’t forget critical thinking.

AI can generate content quickly, but people still need to evaluate its accuracy and usefulness.

Build AI expertise across your business

AI capability shouldn’t be concentrated in one team. It needs to be developed across the organization.

Who should lead that effort—and who should be trained?

For small and medium-sized businesses that don’t have AI specialists on staff, the first step is to bring in external expertise.

This can help you:

• develop effective training programs
• establish clear governance frameworks
• identify practical, high-impact use cases
• avoid common pitfalls related to security, implementation and change management

But external support is only part of the picture. Over time, the goal is to build internal expertise.

As employees gain experience, encourage early adopters to share what they learn with colleagues. This helps make AI more tangible and relevant to everyday work.

"When the expertise comes from within, it feels more grassroots," says Trépanier. "People can see how AI is being used in real work and how it creates value."

This shift is important.

AI adoption accelerates when people stop seeing it as something introduced from the outside, and start seeing it as something they can use, shape and improve themselves.

That’s also why training should be inclusive.

"Everyone who works on a computer needs to understand how AI works and be trained on how to use it effectively. That's the absolute bare minimum," says Trépanier.

From there, expertise should deepen based on roles.

• Senior leaders focus on strategy, return-on-investment (ROI) and organizational direction.
• Managers and directors focus on implementation, performance and communication.
• Front-line employees focus on practical use, tools and real-world applications.

Beyond that baseline, training should be tailored to each role.

For example, a lawyer, production manager and software developer will not use AI the same way. Their training shouldn’t look the same either.

Address employee concerns about AI early

AI adoption often raises concerns about job security. If you don’t address them directly, uncertainty grows.

Make sure employees know that AI is best suited for:

• automating repetitive tasks
• administrative work
• low-value activities

And it is far less effective at:

• judgment
• creativity
• problem-solving
• relationship-building

AI is changing how work gets done, but it is not replacing knowledge workers. 

"We can build more, we can sell more, we can do more," says Trépanier. "But it's not about doing it with fewer people. It's about doing more with the same people."

Communicating this perspective helps employees see AI as a tool that will enhance their work (and maybe even make it more interesting) rather than threaten it.

How to measure AI adoption success

Training completion is easy to measure. Adoption is what matters.

Look for signs that employees understand AI, feel comfortable using it, and are starting to identify practical applications for it in their own work.

Early indicators of success may include:

• employees use AI tools confidently
• approved tools are used regularly
• time is saved on repetitive tasks
• general productivity improves
• teams find new use cases
• employees understand security and governance requirements

Importantly, employees should understand where human judgment remains essential.

The goal isn’t to use AI for its own sake. It’s to improve performance, decision-making and productivity.

The bottom line

AI won’t transform your business on its own. 

Your people will.

The organizations that succeed with AI aren’t the ones with the most advanced tools. They’re the ones that prepare their teams to use them well.

Start there.

Next step

Learn to make confident choices about advanced technology with our Lead with Innovation and Focus on Technology (LIFT) program and find out how to get your business AI ready.