Beyond AI Adoption: How High-Trust Workplaces Turn AI Into Business Results

Company Culture, Employee Engagement , Talent Acquisition, Training & Development

Key Takeaways:

A year ago, the AI question on most leadership agendas was a simple one: will our people actually use these tools? For most organisations, that question has been answered. The harder question now is whether all of that use is creating value the business can see.

Great Place To Work's latest global workforce study found that six in 10 employees use AI for work at least weekly, and 31% use it every day. Canadian data shows the same momentum. As of March 2026, just over one in three Canadian workers (35.9%) had used generative AI tools at work in the previous 12 months, and 19.2% of Canadian businesses reported using AI to produce goods or deliver services, up from 12.2% a year earlier and 6.1% in 2024.

Momentum is not the same as impact, though. AI adoption is still uneven and fragile. Nearly two-thirds of Canadian users apply generative AI to some of their tasks rather than most of them, and while 58% of employees in the global study say AI improves the quality of their work, better individual output does not automatically turn into lasting, measurable gains in revenue or cost.

That is why the challenge has shifted for so many organisations. The goal is no longer getting employees to use more AI. It is making sure everyone uses it in ways that strengthen the business today and over the long term.

Whether an organisation operates in finance, logistics, hospitality or manufacturing, the central question is now the same: how can AI become something that builds a better workplace and a better business for everyone?

Part of the answer lies in where the real obstacles sit. Because AI in the workplace is still at an early stage for much of the general workforce, the toughest problems tend to be human ones. People in technical roles are used to breaking work into structured, logical steps. For many of their colleagues, working alongside AI calls for a genuinely new way of thinking.

Why AI Implementation Has Not Yet Paid Off

Now that AI implementation has spread across most of the organisation, the bill is growing. Even if individual models become cheaper, AI agents could consume up to 30 times more tokens, which would keep enterprise costs high.

That spending may still prove worthwhile, but the productivity gains have yet to show up clearly in profits. Half of global CEOs say AI tools were the single biggest driver of productivity improvements in their organisations last year, yet only 5% of CEOs can attribute at least 5% of their earnings before interest and taxes (EBIT) to AI investments.

Canadian research tells a similar story. A Statistics Canada study on AI adoption and productivity found that firms using AI recorded labour productivity about 16.8% higher than non-adopters. Once researchers accounted for how productive those firms already were and the capabilities they had in place, such as data analytics and employee technology training, the advantage shrank to a level that was no longer statistically significant. The researchers concluded that productivity gains depend on organisational change and complementary investments, not on the technology alone.

In short, the costs are adding up faster than the returns. Marcus Erb, Executive Vice President of Data and Insights at Great Place To Work, points out that because AI is expensive, organisations need to move quickly through a period of uncertainty while weighing risks that no one can fully see yet.

Workplace Culture Is the Real Engine of AI Adoption

The biggest challenges of the AI era are human rather than technological, a conclusion Great Place To Work's research has reached again and again. That is why workplace culture, not the tools themselves, so often decides whether AI adoption succeeds.

The connection between trust and performance is well documented. Great Place To Work's global research, drawn from more than 20 million employees across 22,000 companies, shows that high-trust workplaces have 27% greater agility than average organisations and a 2:1 advantage in innovation, and that nine in 10 of their employees are willing to go above and beyond. In Canada, organisations that build this level of trust are recognised through the Best Workplaces™ programme.

High-trust workplaces offer a perspective most AI reinvention efforts are missing: real experience combining technology with genuinely empowered people. As Erb puts it, the leaders who build them believe people are the reason a business wins.

Rethinking Workflows for AI Transformation

Getting full value from AI rarely comes from adding a new tool to an old process. The most effective approach is to go back to first principles. Start by defining what a workflow is meant to deliver for the organisation, then use AI to reach that outcome faster and better. Often, that means reimagining the workflow from start to finish.

For HR and people leaders, a few practices help that shift take hold:

• Define the outcome first: Agree on the result a workflow exists to produce before deciding where AI fits, so the technology serves the goal rather than the other way around.
• Redesign rather than bolt on: Look at the full sequence of work and ask which steps still need to exist, instead of automating each step as it stands today.
• Build capability alongside the tools: Pair new systems with training and support. The Statistics Canada findings suggest that complementary skills and investments explain much of the productivity gap between adopters and non-adopters.
• Protect trust through every change: Explain what is changing, why it is happening and how it affects people's roles, so each transformation builds confidence instead of draining it.

That last practice matters more than many leaders realise. According to Edelman's 2026 Trust at Work research, which surveyed more than 10,000 employees in 10 countries including Canada, three in four employees went through at least one transformation event in the past 12 months, such as a reorganisation, AI integration or layoffs. Four in 10 went through three or more.

When those changes are handled poorly, the cost is steep. Employees with negative transformation experiences report trust in their manager 25 points lower and trust in their CEO 33 points lower than employees whose experiences were positive. They are also less than half as likely to embrace AI transformation at work (18% compared with 40%). A poorly managed change, in other words, can undermine the very AI adoption it was meant to speed up.

Better Questions Lead to Better Employee Productivity

No one has yet found the formula that unlocks AI's full potential for building better workplaces. What is becoming clear is that the answers will not come from asking the same questions as before.

That rethink raises questions every leader should be exploring: How do people develop wisdom at work? How do they collaborate with the technology day to day? And how can they lean into the qualities that make them valuable as humans?

The organisations that see lasting gains in employee productivity will be the ones willing to explore those questions openly with their people. For them, this moment is a rare opportunity to build something genuinely new.

Sustainable artificial intelligence adoption starts with understanding how much your people trust the organisation, its leaders and the changes ahead. See where your organisation stands with the Trust Index™ employee survey, and explore how high-trust cultures translate into stronger business results through the Great Place To Work Effect.

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This article is an adaptation of the original Great Place To Work®, written by Ted Kitterman, Content Manager.

 

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People Also Asked

How do you measure AI adoption in the workplace?
Look beyond whether people have access to tools. Track how many employees use AI regularly, which tasks they use it for, and whether it changes outcomes the business already measures, such as quality, speed or revenue per employee. Pair that usage data with employee feedback on trust and readiness.

What are the pros and cons of AI in the workplace?
On the plus side, many employees say AI improves the quality of their work and speeds up routine tasks. The trade-offs include rising usage costs, returns that can be slow to appear, and a loss of trust when change is introduced without care.

How many Canadian workers use AI at work?
As of March 2026, just over one in three Canadian workers (35.9%) had used generative AI tools at work in the previous 12 months, according to Statistics Canada. Most users apply it to some of their tasks rather than most of them.

Why does a workplace need an AI policy?
A clear AI policy explains why AI is being introduced, how it affects roles and workflows, and which safeguards are in place. That transparency gives employees the confidence to use AI well and protects trust as adoption grows.

What kind of AI training helps employees most?
Practical, role-specific training delivered alongside new tools tends to work best. Statistics Canada research links capabilities such as employee technology training with a higher likelihood of AI adoption, and hands-on practice helps people use AI with confidence.

How can leaders build trust during AI transformation?
Communicate early about what is changing and why, involve employees in shaping new workflows, and follow through on commitments. Each well-managed change strengthens the trust that future AI adoption depends on.

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