Productivity reimagined: navigating the AI-driven transformation of work

Discover how AI is reshaping productivity, redefining performance, and empowering employees for a transformative future at work.

In brief

• AI is transforming productivity measurement, requiring organizations to adapt their performance metrics to focus on outcomes rather than hours worked.
• Upskilling employees in AI tools, ethics, and collaboration is essential for maximizing productivity and ensuring a successful workforce transition.
• Leaders must redefine their roles to support AI adoption, fostering a culture of innovation, trust, and continuous learning within their teams.

Productivity isn’t changing — it’s already transformed. As organizations across Canada seek to understand, adopt and deploy AI and set their sights on the agentic AI enterprise, the way we measure productivity will have to change, too. What worked in the past doesn’t align with where we’re heading next or, in many cases, where we already stand. Businesses that want to make the most of AI over the long term should start considering how it will influence the way we measure productivity — and what that means for employee experiences overall.

The market knows AI holds tremendous potential across industries and sectors. Even so, many organizations may be underestimating just how much AI is already reshaping productivity and our ways of working. 

The EY AI sentiment index study shows 82% of people are already using AI to improve how they live and work. Some of the most promising AI applications align with areas where businesses are actively developing solutions. That means even as organizations focus on hot-ticket, AI-focused priorities like cybersecurity, privacy and governance, the workforce is already grappling with implications — and businesses need to focus here sooner rather than later.

Put simply: while many see the importance of AI and embed it into their organizational culture, the real challenge is that AI-driven shifts are beginning to outpace organizational preparedness. That includes an urgent need to ready the workforce for a generational shift in what it means to be productive. 

Comprehensively update productivity models to account for AI capabilities and implications 

Keeping humans in the loop is a critical aspect of successful and responsible AI adoption. The way you engage people in that process is equally important. Particularly as AI begins to reframe productivity, historic markers for high performance at work must evolve. For example, someone who uses AI to work smarter ¬— not longer — and delivers great business outcomes before wrapping up early may ultimately be viewed as gold standard in terms of performance. That’s compared to years past, when hours logged might have been recognized as a sign of success.

On the one hand, this reality will require us to evaluate productivity differently from here on out. The EY Work Reimagined Survey found that advanced users don’t just use AI more, they use it differently, treating it as a colleague, coach and thought partner rather than a simple automation tool. Top performers who adopt this collaborative mindset achieve gains more than twice as great as their peers. 

On the other hand, doing so highlights a host of complex workforce and talent issues that must also be addressed. At the core is redefining performance: moving beyond hours worked to focus on the ability to deliver impactful outcomes by using AI effectively. When organizations set clear expectations around results and capability, engagement follows — because employees see their contributions recognized in ways that matter most in the digital age. 

How can organizations in Canada reimagine productivity in light of AI?

Every business is unique. True, too, for the talented people who make up our workforces. With that in mind, organizations here will want to consider leading practices and then adapt them to meet their own people’s specific needs. This will involve redesigning roles alongside AI deployment, assessing what employees should stop doing, what high-value activities they should focus on and how they can elevate their contributions. 

At EY, we recommend focusing on three key priorities now:

1. Upskill employees to meet the needs of the future

To truly redefine productivity, organizations must empower all employees to confidently and responsibly use AI. This means going beyond basic familiarity and building depth across toolset, skillset and mindset. 

Upskilling should cover four dimensions:

• Tool proficiency: e.g., navigating gen AI platforms and prompt engineering 
• Ethical awareness: understanding bias and responsible use
• Collaboration with AI: knowing when to delegate and when to intervene
• Critical thinking: evaluating AI-generated outputs

As technology evolves at an unprecedented pace, upskilling is not just about keeping up — it’s about preparing the workforce to thrive in a future shaped by AI. Organizations should be intentional in their workforce planning, tailoring learning and development to roles while ensuring every employee is equipped to adapt, innovate and lead as new opportunities and challenges emerge. 

This kind of development is a strategic business imperative: without it, organizations risk generational or fluency gaps that create friction, slow adoption and ultimately hold back productivity gains. Upskilling helps level the playing field, so the benefits of AI are realized by both people and the organization overall.

2. Measure employee impact, innovation and collaboration — not just volume

AI has pushed productivity to new heights, but the way we measure performance hasn’t kept pace. Counting tasks or hours worked no longer reflects true contribution. Instead, organizations must shift toward evaluating the value of work — its business impact, problem-solving, quality, innovation and contribution to team success. 

This reframing acknowledges that AI can handle volume, but humans drive strategy, creativity and connection. To support this shift, performance needs to evolve beyond static annual reviews and embrace ongoing feedback that keeps pace with change. Continuous, outcome-focused conversations help employees understand expectations and adapt quickly in a fast-moving environment. These touchpoints complement formal processes, making recognition more timely and development more meaningful.

When metrics align with this new reality, organizations can reward the work that truly moves the needle. Here’s how traditional measures should evolve:

• Transition from measuring tasks completed to evaluating business outcomes achieved.
• Shift focus from hours worked to learning velocity and adaptability.
• Shift evaluations from revenue per employee to collaboration and cross-functional impact.
• Move away from manager ratings to look at peer feedback and coaching effectiveness.
• Adapt from compliance with processes to emphasize innovation and process improvement. 

You’ll want to embed clear expectations about allocating time savings between strategic growth initiatives and creating space for innovation, learning and adaptation in the goal-setting process.

3. Redefine managers’ roles to fuel growth and lead transformation

Leadership vision and cultural alignment are essential for successful AI adoption. As AI seeks to automate some tasks and to elevate employee contributions, you may need to rethink the role your managers play. In that context, leaders will need to support their employees and shift their focus from overseeing tasks to coaching, developing and enabling their teams — becoming true performance accelerators who guide employees in strategic thinking, emotional resilience and continuous learning.

At the same time, this transformation calls for managers to act as ambassadors for AI adoption. This transformation is not just a technology challenge — it’s a leadership opportunity. The real question is not whether AI will reshape the workforce, but whether organizations will rise to the challenge of reinvention. Leaders must become ambassadors for AI adoption, modelling openness to new technologies, championing responsible use and fostering a culture of curiosity and experimentation. Their advocacy is essential for building trust, driving engagement and accelerating organizational transformation.

Success in this new environment depends on managers’ ability to foster trust, collaboration and adaptability. By prioritizing psychological safety and encouraging teamwork, leaders create the conditions for employees to experiment, learn and grow. In the age of AI, productivity is defined not just by output, but by the capacity to innovate and respond to change. Managers who coach, enable and champion AI adoption empower their teams to excel and shape the future of work together.