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Move your organization from AI strategy to results

The missing link between AI investment and real returns? Work itself

Insight | June 01, 2026 |

We’re four years into the artificial intelligence (AI) revolution. Many Canadian organizations have invested in strategy, governance, and responsible AI. They’ve started to deploy and prioritize AI use cases. And many vendors are bullish on the evolving capabilities of AI. 

But for many organizations, adoption remains low and returns remain elusive. Only 14% of the nearly 50,000 workers we spoke to in our Global Workforce Hopes and Fears Survey say they use generative AI (GenAI) daily. This remains well below what many global executives estimate—and it’s only a small increase from the 12% we reported in 2024. Our recent global AI performance study found that for most organizations, AI activity has yet to translate into measurable return on investment (ROI). In fact, 20% of companies globally are capturing 74% of AI-driven returns. 

So what’s missing? Here in Canada, we see many organizations jumping straight from AI strategy to deploying agents. But AI agents aren’t just tools that make people more productive. AI agents give rise to agentic workers that will work alongside humans. Most organizations haven’t yet rethought the long-held assumptions behind how work is organized. These assumptions no longer apply when your workforce includes both humans and AI. 

According to our 29th Global CEO Survey—Canadian insights, only 37% of Canadian CEOs whose organizations have started using AI say they have a clearly defined AI roadmap. But even those that do tend to focus on optimizing one process or use case at a time, and that will never access the full potential of a human and agentic workforce. 

Giving your people the best tools is only part of the equation. Without formally and systematically redesigning work, we’ll continue to see people afraid of experimenting with the tools and anxious about how they’ll impact work. Organizations need to be relentless about rethinking how work gets done, identifying what remains for humans, and building career and skills pathways for their people to evolve alongside AI. 

20%

of companies globally are capturing 74% of AI-driven returns

PwC global AI performance study

Six stages of reinventing work around AI

Here at PwC Canada, we’ve mapped what this evolution looks like in practice: six stages of AI maturity. Each stage represents a deeper shift in how organizations think about and organize work around humans and AI. Organizations don’t necessarily need to start these stages sequentially, but they’ll need to build some maturity at each to progress significantly further. 

At the first stage, organizations encourage their people to embrace AI by embedding it into their everyday work. This is a change management and reskilling exercise. 

Our Global Workforce Hopes and Fears Survey shows that breaking through the adoption barrier pays off. Nine out of ten workers who use GenAI daily say they’ve experienced productivity and quality improvements—and expect to see further advantages. By contrast, workers who use AI less frequently are missing these benefits, and they’re significantly more likely to feel anxious about AI’s impact on their role. 

Beyond productivity gains, the data is clear: the more people use AI, the more likely they are to be curious and excited about it rather than worried.

While most organizations rightly focus on AI implementation first, implementation alone doesn’t provide significant boosts in productivity or capacity. We’re seeing a lot of organizations stall at this stage.

At the next stage, teams start identifying, prioritizing, and testing AI-enabled use cases within their specific workflows. This is still decentralized and democratized. The focus is on discovering where AI creates value within individual functions. Organizations aren’t yet consolidating their AI infrastructure or measuring returns across the enterprise.

What this stage builds is important: a clearer picture of where AI creates the most value across the organization and the momentum to move beyond experimentation. 

Organizations at the re-engineer phase have moved beyond experimentation and are ready for changes to become permanent. Now leaders across the organization must deeply rethink work by breaking down processes and value chains across departments, roles, and platforms. The goal is to re-engineer these processes end to end—instead of optimizing individual tasks.

As processes are re-engineered, individual tasks will be automated, and jobs will merge, expand, and be redefined. The challenge for organizations is to trace that evolution deliberately rather than let it happen by default. At this stage, organizations are reconfiguring individual value chains, but they haven’t yet investigated how those changes will reshape the function as a whole. 

In the past, many organizations centralized, standardized, and consolidated because expertise was scarce and productive capacity was bound to people. AI removes those constraints. There’s now no inherent limit on expertise or scalability. Instead, the constraints are around trust, adoption, and human judgment.

This stage of work reinvention marks an inflection point. The difficulty curve steepens significantly, but so does the value generated. At this stage, the organization rearchitects its strategy, governance, operating model, and agentic tech stack. Entire functions, for example, HR and finance, are redesigned with AI at the core of value generation.

In practice, this means moving from a model where AI helps human workers to one where AI generates core value via insights, transactions, and analysis. Humans orchestrate, interpret, and direct that work toward outcomes. 

Now the organization operates as a coherent AI-native entity, not just a collection of AI-enabled functions. Multiple functions are architected and orchestrated similarly and, importantly, there’s enterprise-level alignment on agentic workforce being part of the strategy. There’s a central conductor at the top defining the strategy and framework, and this is executed across the organization by individual functions, all of which are empowered and bought in.

At this stage, organizations have a well-defined but dynamic AI strategy, governance model, and responsible use framework. They’re intentional about risk thresholds and tolerance, and they understand where they’re going to push the boundaries—and where they’re not.

This is what makes the final stage of work reinvention possible. Without operating as a coherent AI-native entity, organizations can’t pursue entirely new sources of value or move effectively across markets.  

Eventually, organizations move to the last stage of work reinvention. AI is the backbone of the enterprise operating model, enabling the organization to pursue new business ventures and sources of revenue.

Consider what it takes to move into entirely new industries and markets, for example, moving from a software company to an energy company to space exploration. To cut across these very different industries and generate value and scale, organizations need a digital AI core and people with fluid skill sets who can move across domains. New ventures are already being built this way. For established organizations, the path is longer, but the destination is the same.

This stage can’t be rushed. It’s difficult to achieve without first building momentum from the preceding stages of work reinvention. Some organizations have had success with ring-fenced parallel teams that operate independently of the core business and move quickly. But for most, this kind of agentic business model reconfiguration requires a foundation only the earlier stages of work reinvention can provide. 

Organizations that haven’t yet reached the third or fourth stage are unlikely to see the productivity gains they’re looking for. The effort required at each stage grows dramatically—but so do the returns if done correctly. 

The future of work belongs to curious, high-potential teams

The later stages of work reinvention are coming. The only question is whether organizations walk into them deliberately or get pulled there by competitive pressure. Either way, organizations that pursue this reconfiguration without bringing their people along will create anxiety. 

But for leaders willing to take this on, the opposite will be true. If you bring humans along as you evolve work, you’ll be left with curious, high-potential workers who are passionate about their work and see AI as extending what they can do beyond what was possible before. 

Organizations that close the gap between AI deployment and work redesign will achieve the elusive returns they’re looking for—and build workplaces where their people thrive.

Ready to reinvent how work gets done?

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Work, rewired

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Farbod Nassiri

Farbod Nassiri

Partner, Rewire Work Leader, PwC Canada

Tel: +1 416 869 2414

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