Reshaping expertise:

In the evolving landscape of artificial intelligence, it's easy to assume that AI only affects repetitive, mundane work. After all, you can’t automate expertise. Or can you? This assumption overlooks the profound effects of Generative AI (GenAI) on a significant segment of the workforce: knowledge workers.

Knowledge workers are professionals with deep expertise in their domain, gained through years of experience over their career, and most often in careers that follow post-secondary education.

Historically, knowledge worker roles were seen as less susceptible to automation due to the expertise and human judgment required to perform cognitive tasks. However, with the emergence of GenAI, this view is changing rapidly. GenAI can now analyze vast amounts of data, generate insights, and perform complex tasks that once required years of human experience and education.  

GenAI doesn’t replace expertise - it supercharges it by amplifying the value of human judgment, contextual awareness, and experience from years of navigating complex situations. While GenAI may not necessarily replace knowledge worker jobs, those who learn to work with GenAI will be better prepared for knowledge work of the future. 

This new paradigm offers both challenges and opportunities as knowledge workers adapt to a new reality where AI plays a central role in their professional lives. 

Education doesn’t protect from GenAI disruption

In the past, technological advancements often led to the displacement of workers, more commonly for roles requiring lower levels of education and skill. However, GenAI’s integration is different because it affects the day-to-day work for highly educated and skilled workers, too.   

Knowledge workers — such as software developers, financial advisors, teachers, architects, lawyers, doctors, and marketing professionals — are witnessing an evolution of their roles with GenAI.  

Statistics Canada¹ found that GenAI disruption is strongly correlated with education level attainment. Highly educated workers tend to be in knowledge-intensive roles—exactly the kinds of jobs where GenAI excels. GenAI models learn just like humans do, except they can access and absorb much more information at a significantly faster rate. Canada leads G7 countries² with the highest working age population with post-secondary education. Nonetheless, we’ve seen that education alone won’t be enough to stay resilient. 

This raises questions about the future role and value of traditional education. If GenAI can rapidly replicate and surpass many technical skills that higher education once provided, what kind of education truly prepares someone to thrive in an AI-augmented economy? Aspiring professionals will need critical thinking, ethical reasoning, and adaptability to work alongside AI, and when necessary, challenge AI-generated insights.

To protect Canada’s labour market and economy from significant workforce displacement, we must redefine both education, apprenticeship models, and continuous learning for an AI-enabled world.

Experience, reimagined: When knowledge alone is no longer enough

Experience, often gained through apprenticeship and tenure within organizations, has long been the cornerstone for developing the strategic, problem-solving skills and judgment needed in complex cognitive knowledge work. This hands-on learning was not just a pathway to mastery, but also a mechanism for preserving organizational knowledge itself. Consider the common reality of "years of experience" as a requirement on a job posting, which assumes that valuable knowledge and skills are accumulated through prolonged exposure and practice within a field. 

However, our work with clients across different industries reveals a disruption to the apprenticeship model. GenAI is not just altering tasks. It’s rewriting the traditional equation — education + experience = expertise — and commoditizing knowledge. When AI can instantly generate insights, draft documents, write code, or even produce creative strategies, the competitive advantage no longer lies in what you know. Instead, it lies in how effectively you can interrogate, contextualize, apply, and execute what the machine knows, while considering your organization’s complexities.

This shift is particularly pronounced in professional services, where the entire business model has historically relied on the leverage pyramid: junior employees performing foundational work, gradually building knowledge and judgment, while senior professionals focus on higher-value strategy and client relationships. GenAI disrupts this model at both ends — automating foundational work while augmenting senior-level decision-making. The result? Junior employees risk losing critical developmental experiences, and firms must rethink how they cultivate future leaders when “learning by doing” no longer follows a predictable path.  

Quantifying GenAI’s potential impact on work as we know it

Using our proprietary workforce analysis tool, Periscope, we analyzed how GenAI reshapes day-to-day tasks in four knowledge worker career paths. Swipe through the carousel to explore how specific tasks at junior and senior levels are evolving across professions. 

In the legal profession, GenAI can now handle much of the legal research and document reviews that junior lawyers typically perform. This accelerates delivery, but also compresses the traditional apprenticeship process, requiring junior lawyers to engage in higher-order thinking far earlier in their careers. Similarly, in software development, AI-assisted coding and debugging mean that junior developers must quickly pivot from learning syntax to mastering systems thinking and architectural problem-solving. 

The traditional career ladder was built for a world where knowledge accumulation was linear, and experience was gained by gradually layering insight on top of experience. GenAI breaks that linear path. Organizations that fail to redesign their apprenticeship pathways risk losing not just their next generation of talent, but their competitive edge. 

If education and experience are both being disrupted, how will we build the experts of tomorrow? This question sits at the heart of the future of work — and the future of entire industries. 

Expertise for tomorrow's knowledge workers: 4 strategies

The traditional pathway to expertise is evolving. While GenAI provides easy access to knowledge, it cannot replace human-only capabilities like judgment, creativity, empathy, deep understanding and critical thinking. Sustained success requires action from both organizations and individuals. Organizations must adapt their strategies to support our economy and ensure the continued growth of expert knowledge workers. Simultaneously, knowledge workers must demonstrate adaptability and curiosity as they integrate GenAI in their work. 

Here are four key steps to ensure your organization continues to grow with the emergence of GenAI:

1. Redefine Expertise as Sense-Making, Not Memorization: Expertise means knowing how to interpret, evaluate, and apply knowledge in context — something only experience brings. But when AI can generate knowledge, leaders must pivot their organizations toward developing workers who can rapidly synthesize, contextualize, and apply knowledge in ways that are uniquely human. This means organizations must train and prioritize discernment, judgment, and intellectual agility, rather than static mastery of a domain that GenAI is disrupting.
2. Build Experience at the Speed of AI: Traditional models of skill-building rely on long cycles of apprenticeship and trial-and-error learning. AI accelerates these cycles dramatically, meaning workers need structured, high-velocity experiential learning. Our research shows that most workers are excited to use this technology, so get it into their hands! We surveyed over 1,800 cross-industry professionals and found that 79% of early-career employees were enthusiastic about AI³, along with 66% of late-career employees. Leaders should create AI-powered simulation environments, real-time project rotations, and decision-making sprints to replace slow, linear career development models.
3. Make Learning Frictionless and Personalized: If expertise is built through education and experience, both need to be continuous, adaptive, and AI-enhanced. Organizations must shift from static training programs to agile, context-aware learning ecosystems where workers solve problems and develop skills in real time. This style of learning on the job is the only way we can keep up to the skill gaps ahead, since 39% of current skills⁴ will be outdated by 2030. Learning should be as seamless as using AI itself—embedded in workflows, personalized, and dynamically responsive (e.g., AI-powered learning assistants with real-time recommendations, personalized learning paths).
4. Shift Value from Individual Expertise to Collective Intelligence: The lone ‘expert’ model is outdated. In an AI-powered world, the highest-value organizations will be those that create high-trust, high-speed knowledge-sharing networks. This means putting less pressure on single individuals and backgrounds to fill a role, especially with 34% of Canadian organizations predicting talent availability to worsen in the next 5 years⁴. Leaders must break down silos, incentivize collaborative intelligence, and build cultures where value is measured by how well knowledge flows and evolves, not just how much one person knows.

Discover your organization’s potential with AI

AI doesn’t erase the value of knowledge work; instead, it rewrites the rules of what it means to be an expert. With the right training, knowledge workers can augment their work with AI to unlock capacity for human-centric tasks, and create business value faster.

Deloitte is here to help organizations not only navigate AI's effects on their workforce, but to thrive within them.

Want to unleash the power of GenAI in your business? Let’s talk.

End notes

1. Statistics Canada, “Experimental estimates of potential artificial intelligence occupational exposure in Canada,” Published 2022. 
2. Statistics Canada, “Canada leads the G7 for the most educated workforce” Published December 6, 2024. 
3. Deloitte US, “AI is likely to impact careers. How can organizations help build resilient early career workforce?,” Published December 6, 2024.  
4. World Economic Forum, "Future of jobs report 2025," Published January 7, 2025.