If you’re a founder trying to hire right now, you’re operating in the loudest talent market in years. Applications flood in, resumes blur together, and AI tools designed to help often add more noise than clarity.

According to Caitlin MacGregor, CEO of Plum, a talent intelligence platform that helps organizations measure human potential and durable skills, companies need to adapt to shifts in the recruitment process. That starts with rethinking which skills matter most today.

“Forever, companies have focused on hard skills, what we call ‘perishable skills’. The problem is the shelf life of perishable skills has dramatically diminished in the last few years. This has led to mis-hires and turnover as new employees aren’t able to adapt to evolving roles.” MacGregor says.

In 2025, Canada’s tech talent market surged, growing nearly six percent, more than five times the U.S. rate, driven by a wave of AI-driven roles reshaping the workforce.

Waterloo Region has felt the impact in a big way, jumping to #7 among North America’s top tech talent markets, joining the ranks of Toronto, Vancouver, and Boston as a hotspot for AI innovation. And with Ontario adding 17,000 new AI-related jobs in the last year alone, the region could be in the midst of a hiring boom that promises opportunity. But one that also presents a critical challenge: are employers prepared to match this demand with the right skills?

Many companies aren’t. They’re still relying on rigid systems that measure talent by technical checklists such as proficiency in Excel, programming languages like Python, or specific software platforms. These skills had greater value before generative AI and will continue to be automated. The problem is that checklist hiring optimizes for yesterday’s work, not tomorrow’s outcomes.

The limits of traditional models

Legacy talent evaluation systems were designed to define “good performance” by task-based technical proficiencies like Excel, programming languages, and software credentials. But in a fast-moving, AI-enabled workplace, what once guaranteed competency can become outdated almost overnight.

What once was a safe bet, like proficiency in a specific tool or programming language, may no longer guarantee longevity or adaptability. As MacGregor puts it:

“We've spent a lot of time educating the market on the value of durable skills, aka soft skills. These are the transferable, human skills that allow us to actually perform, no matter the changes in our job, the changes in things that we learn. They endure throughout our career, and they're often missing from the AI conversation, and they’re actually the most critical data and the most critical skill when we talk about AI.”

From “what you’ve done” to “what you’re capable of”

As the local tech ecosystem in Waterloo accelerates AI-related hiring, companies are looking for people with potential, adaptability, and a willingness to experiment, not just fixed technical credentials. Repetitive and manual tasks are increasingly subject to automation, making human capabilities like innovation, learning agility, and problem-solving more valuable than ever.

“I think there's a gap in education that people talk about, but nobody is realizing that you can measure it in a way that's really beneficial for the employee,” says MacGregor. “How do we empower employees to have better self-awareness?”

This is showing up in hiring trends. Employers are reframing roles not simply as legacy functional jobs, but as AI-augmented, adaptive positions.

For example, a “Technical Program Manager” role at a large global tech firm in Waterloo, categorized under “AI / Cloud / AI‑Native,” blends technical oversight with adaptive, collaborative execution. The job description highlights adaptability, collaboration, and working effectively alongside AI-driven systems and not just deep technical credentials.

In other words, candidates need curiosity and judgment as much as domain knowledge. 

Roles like this reflect the shift MacGregor and her team at Plum tackle daily, blending human judgment and durable skills with AI-enabled workflows to help employees thrive as technology reshapes work.

Rethinking leadership in an AI era

The conversation about skills isn’t just for individual contributors. Leadership is changing, too. As AI takes on analytical and repetitive tasks, effective leadership is less about micromanaging and more about coaching, guiding transformation, and nurturing talent.

“The problem is that if we go into the organization treating the early adopters and the fast followers and the laggers all the same way, then you’re not going to progress,” MacGregor explains. “It is the durable skills that help you understand who are the early adopters, who are the fast followers, and who are the laggers.”

Leaders who understand this can design better development paths and help their teams succeed by adopting AI and thriving as active contributors alongside it.

What this means for employers (in Waterloo and beyond)

Based on insights from Plum, as AI reshapes roles and expectations, companies looking to stay ahead should:

  • Prioritize durable skills over credentials: Innovation, adaptability, communication, and learning agility are now as important, or maybe more than, technical know-how.

  • Modify job descriptions to reflect AI-native workflows: Titles and requirements should signal flexibility and AI integration, not rely on outdated role definitions.

  • Invest in diagnostic tools and upskilling programs: Use durable skills data to personalize and guide career development.

  • Build leadership capacity for change: Coaches and guides, not just managers, will drive adoption from early experiments to organization-wide success.

By aligning hiring and talent development with this new reality instead of shoehorning AI into old frameworks, companies can create workplaces that are more adaptive, equitable, and ready for the future.

“When it comes to transforming your organization with AI, identify your early adopters and empower them to rapidly innovate. Then identify the fast followers who can take the best ideas and execute on them. Then bring in the laggers once you’ve demonstrated success. We can do all of that when you have the durable skills data.”

 

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