Who Is Building Tomorrow's Leaders?

Why the conversation about workforce transformation may be missing its most critical piece.

By Danielle Tiemy, Managing Director & Founder, Innovate Pathways Advisors

Every organization is focused on doing more with less. Nobody is asking who will lead them in ten years.

The Scale of the Transformation

The scale of this transformation is larger than most people realize.

Two forces have been reshaping the workforce simultaneously, and their combined impact is significant. Over the past year, 52.2% of Canadian businesses outsourced tasks or projects — more than half of the market actively shifting work across borders. At the same time, 26% of layoffs were explicitly attributed to AI, with many more occurring under broader restructuring initiatives that never carried that label. These are not isolated trends. They are converging, and the cumulative effect on the workforce has been substantial.

Source: Statistics Canada (AI adoption and outsourcing data) and recent Canadian labour market reporting on AI-related layoffs and corporate restructuring.

Yet despite this, the narrative around AI remains remarkably optimistic. Adoption is consistently described as inevitable, accelerating, and transformative. The data tells a more complicated story. Only 6.1% of Canadian businesses reported actually using AI to produce goods or deliver services in 2024. The gap between 'we have the tools' and 'we are genuinely using them' is significant — and it is not closing as fast as the headlines suggest.

The reason is not technical. It is human.

Employees navigating repeated rounds of restructuring, watching colleagues replaced by offshore teams or automated processes, are not arriving at AI adoption with open arms. They are arriving with legitimate questions that largely go unanswered. Is this going to replace me too? How does this actually help me do my job? What does my future look like here? These are not irrational fears. They are reasonable responses to a pattern people have already lived through.

Pushing a tool down through an organization and expecting engagement is not a transformation strategy. People do not change because leadership makes an announcement. They change when they understand what it means for them, trust the process, and can see a future for themselves in the new environment. Without that, what organizations get is compliance theater — AI used as a slightly faster search engine while the real productivity gains stay permanently out of reach.

This is not a technology problem. It is a people problem. And it points directly to the conversation we are not having.

The Gap Nobody Is Talking About

We have been looking at all the right data points. We have just not been connecting them.

AI adoption. Offshoring. Restructuring. Each trend has been analyzed extensively in isolation. The technology reports cover AI. The economics reports cover global talent. The HR reports cover restructuring. But very few people are asking what happens when all three converge — and what they are collectively doing to something much harder to measure than productivity or cost savings.

For generations, expertise was not taught. It was grown.

Junior employees did the foundational work. They made mistakes in low-stakes environments. They watched senior people navigate difficult situations and absorbed judgment they could not have learned in a classroom. Analysts became managers. Managers became directors. Directors became executives. The system was imperfect, slow, and sometimes inefficient. But it produced something genuinely valuable — leaders who had actually done the work, at every level, before they were asked to lead it.

That pipeline is quietly disappearing.

Nobody cancelled the leadership pipeline. It is just slowly running out of inputs.

Not because of any single decision. Not because any one organization made an irresponsible choice. But because thousands of individually rational decisions — move this work offshore, automate that process, restructure this team — are collectively removing the very experiences through which expertise has always been built. The foundational work that once trained junior employees is either being done elsewhere or being done by machines. The roles that created the learning opportunities are shrinking. The informal mentorship that happened naturally when teams worked together closely is harder to replicate across geographies and restructured organizations.

And here is what makes this particularly difficult to see: the impact is not immediate. Quarterly results do not capture it. Annual reviews do not flag it. It shows up years later, when an organization looks around for its next generation of leaders and realizes the bench is thin — and by then, the decisions that created the problem are long behind them.

This is the conversation that is missing. Not instead of the AI conversation. Alongside it.

A View From Both Sides

I am not observing this from a distance.

I have spent more than two decades inside large organizations navigating exactly these decisions — watching the business case for offshoring get built with precision, watching the financial models get stress-tested, watching leadership align around the strategy. What I rarely saw built with the same rigor was the people plan. How would local teams be developed going forward? What would their career paths look like? How would the organization preserve institutional knowledge while redistributing the work? Those questions were rarely answered with the same confidence as the cost savings projections.

The human consequence was predictable. Local teams did not arrive at these transitions with open minds and long-term thinking. They arrived with fear. Offshore teams, often highly capable and genuinely committed, walked into environments that were not designed to welcome them. The result was friction — not because either side was wrong, but because nobody had built the bridge between today’s decision and tomorrow’s reality. Instead of a transition plan, people got an announcement. Instead of clarity about the future, they got uncertainty. And uncertain people do not perform, collaborate, or develop the way organizations need them to.

The organizational consequence was equally predictable, just slower to surface. Institutional knowledge walked out the door. Informal mentorship dissolved. The experiences that once developed judgment — the mistakes made in low-stakes environments, the problems solved alongside senior colleagues — moved elsewhere or disappeared entirely.

Now I work with the SMBs navigating the downstream effects of those same market forces. Different scale, different context, same pattern. The foundational question is identical: who is thinking about what this means for the people, not just the quarter?

That question is what this article is really about.

The Leadership Pipeline Problem

There is a statement that has stayed with me:

AI can make a good consultant great. It cannot make a bad consultant good.

The same principle applies across every profession. Accounting. Engineering. Law. Finance. Technology. Marketing. Procurement. Leadership itself. AI is a powerful amplifier. It can make experienced people faster, sharper, and more effective. What it cannot do is replace the experience that made them worth amplifying in the first place.

Judgment is not transferable. It is not downloadable. It is not something that can be prompted.

It is developed through years of doing the work. Making decisions with incomplete information. Navigating difficult conversations. Solving problems that do not have clean answers. Watching senior people handle situations that no playbook covers and absorbing something that cannot be taught in a training session or replicated by a tool.

That development process requires opportunity. It requires exposure. It requires the kind of foundational work that is quietly disappearing from organizations across the country.

When a junior analyst no longer processes the data because a tool does it faster — who develops the judgment to know when the data is wrong? When a junior consultant no longer writes the first draft because AI produces something cleaner — who develops the instinct to know when the thinking is flawed? When foundational project work moves offshore — who builds the contextual knowledge, the client relationships, the pattern recognition that turns a competent professional into a trusted advisor?

These are not hypothetical questions. They are already playing out inside organizations that will not feel the consequences for another five to ten years — and by then, the decisions that created the problem will be long forgotten.

We are not facing a technology gap. We are facing an experience gap.

And unlike a technology gap, you cannot close it quickly. You cannot buy your way out of it. You cannot restructure your way around it. Experience takes time. Leadership takes time. Judgment takes time.

The pipeline does not refill overnight.

Why Canada. Why Now.

Canada is making meaningful investments in its AI future.

The recently launched AI for All strategy reflects genuine ambition — accelerating adoption, building literacy, expanding participation in Canada’s AI economy. These are the right priorities. A country that falls behind on AI adoption falls behind on competitiveness, productivity, and economic growth. The urgency is real and the direction is sound.

But technology strategies succeed or fail on the strength of the people implementing them.

Adoption is not a technology challenge. It is a people challenge. And a people challenge requires more than access to tools — it requires a workforce that has the experience, judgment, and confidence to use those tools in ways that create genuine value. Not as a faster search engine. Not as a shortcut to avoid thinking. But as a true force multiplier applied by people who know what they are doing.

That is exactly what the pipeline problem threatens.

If we are simultaneously automating foundational work, redistributing learning opportunities offshore, and restructuring the teams that once developed junior talent into senior leaders — we are not building the workforce that AI for All needs to succeed. We are hollowing it out. Quietly. Incrementally. In ways that will not show up in this year’s productivity numbers or next year’s adoption metrics.

They will show up a decade from now, when organizations look for experienced leaders who can navigate complexity, exercise judgment, and drive transformation — and discover the bench is thin.

For Ontario businesses specifically, this is not an abstract policy concern. It is already an operational reality. The SMBs and mid-size firms driving growth across this province are navigating these forces right now — making decisions about hiring, automation, and outsourcing that feel rational today but carry long-term consequences that rarely make it into the business case. Who is the next person to lead this function? Who is developing the judgment to run this organization in ten years? These questions are not being asked nearly enough.

AI for All can work. But only if we are also investing in the people who will make it work — not just giving them access to the technology, but ensuring they have the experience and judgment to use it well. That requires a workforce development conversation that matches the ambition of the technology strategy.

We do not have that conversation yet. We need to start.

What Needs to Happen

This is not an argument against AI. It is not an argument against global talent strategies or the business decisions that drove them. Those forces are here to stay and they create real value. The question is whether we are managing them with the long-term in mind — or only the next quarter.

The workforce development pipeline is not a soft issue. It is not an HR concern to be addressed after the technology strategy is complete. It is the foundation on which every technology strategy either succeeds or fails. And right now, we are not treating it that way.

For policy makers, the ask is straightforward. Workforce development needs to be part of the AI strategy — not as a footnote, not as a future consideration, but as a core pillar alongside adoption, literacy, and sovereignty. AI for All is an ambitious and necessary investment. Make it complete. Fund the programs that create pathways for young professionals to develop real experience in an AI-enabled economy. Measure not just how many organizations have access to AI tools, but how many people are developing the judgment to use them well. The technology strategy and the workforce strategy are not separate conversations. They are the same conversation.

For business leaders, the ask is equally direct. Before the next restructuring decision, before the next automation initiative, before the next offshoring expansion — ask the question that rarely makes it into the business case. What happens to our leadership pipeline? Who is developing the judgment this organization will need in five years? What are we doing to ensure that the people who remain have genuine opportunities to grow, not just more work to absorb with fewer colleagues beside them? These are not questions that slow down good decisions. They are questions that make good decisions sustainable.

The manager in the opening of this article is not a fictional character. She exists in organizations across this country right now — navigating uncertainty, carrying institutional knowledge that nobody has thought to protect, wondering what her future looks like in an environment that keeps asking her to do more with less. She deserves a better answer than silence.

We have the technology strategy. We have the economic strategy. We are missing the people strategy — and that is the one that determines whether any of it actually works.

The question is no longer whether we can afford to have this conversation.

It is whether we can afford not to.