Editor’s note: This is the 19^th article since May 20, 2026 in an ongoing series by Dr. Andrew Maxwell, the Bergeron Chair in Technology Entrepreneurship in the Lassonde School of Engineering at York University. Every week – and occasionally every other week – we’ll present a new article by Maxwell, in a series whose wide-ranging and incisive themes encompass: Canada and innovation policy; productivity and industry; innovation frameworks; AI and higher education; research and intellectual property; technology adoption; entrepreneurship and commercialization; universities and higher education; entrepreneurship education; and AI and the future of work. 

 This is Part 2 of a four-part series on innovation, leadership and organizational adaptation. Part 2 was published on September 30, 2026.  

In Part 1 of this series, I argued that most organizations struggle with innovation not because they lack ideas, talent or intent, but because they are structurally designed to suppress learning.

Most large organizations don’t believe they are good at innovation.

In fact, many leaders will tell you – often candidly, and sometimes with frustration – that much of what their organization does in the name of innovation feels like theatre. Hackathons that go nowhere. Labs that sit at the edge of the business. Metrics that look impressive in board decks but rarely change decisions.

This self-awareness is important. It means the problem is not ignorance or complacency. Leaders are not confusing activity with impact because they are naïve – they are doing so because they don’t know what else to rely on.

And that is where measurement quietly enters the story.

Organizations fall back on the metrics they know how to use, even when they suspect those metrics are misleading. In innovation, this is not just unhelpful – it is actively dangerous.

Those same systems – built for efficiency, predictability and risk minimization – do exactly what they were designed to do. They reward certainty, penalize ambiguity, and quietly suppress learning.

If that diagnosis is correct, then the next question is not how to innovate, but something more fundamental:

How do you know whether your organization is capable of innovating – and what should you be paying attention to in the first place?

Innovation theatre and the measurement vacuum

Innovation theatre persists not because leaders are naïve, but because they are constrained. When organizations don’t know what meaningful innovation signals look like, they fall back on the metrics they already trust. Hackathons, labs and dashboards become substitutes for understanding, not because leaders believe in them, but because they lack better instruments.

In environments dominated by uncertainty, familiar performance metrics offer psychological safety. They create the appearance of control. But in doing so, they quietly reinforce the very habits that suppress experimentation and learning.

This brings us to measurement – not as a technical problem, but as a behavioral one.

Measurement is never neutral. What you choose to measure shapes behavior, priorities, and decision-making – especially in large organizations.

In performance contexts, this is usually beneficial. Measuring cost, quality or throughput helps organizations optimize known systems. But innovation is not about optimization; it is about learning in the face of uncertainty.

When innovation is measured using performance-style metrics, learning is deferred in favor of defensible outcomes. Exploration is quietly penalized because it produces ambiguous data, early-stage ideas are judged using late-stage criteria, and people optimize for what can be counted rather than what needs to be understood.

Over time, organizations do not become less innovative by accident – they become better at avoiding the kinds of activities that produce uncomfortable numbers.

This is why measuring the wrong things doesn’t just fail to support innovation. It actively pushes organizations in the wrong direction.

When metrics distort innovation

This dynamic is particularly visible in environments that are heavily metric-driven, such as universities and public institutions.

Consider the emphasis placed on patents, licenses or royalty revenue as indicators of innovation success. These measures are easy to count, easy to compare, and easy to report. They are also deeply misleading.

When patents become the metric, organizations produce more patents – not necessarily more impactful innovation. When licensing revenue becomes the benchmark, attention shifts toward incremental, defensible opportunities rather than risky, exploratory ones. In many cases, the behaviors required to maximize these numbers actively stifle collaboration, delay learning, and discourage openness.

The same pattern appears in corporate settings. When innovation is measured through outputs alone, organizations learn to delay engagement, oversell certainty, and avoid experiments that might “fail on paper,” even if they generate valuable insight.

In other words, measurement becomes a control mechanism, not a learning tool.

A core misunderstanding sits at the heart of this problem: the assumption that innovation metrics should behave like performance metrics.

They shouldn’t.

Innovation metrics are not precise instruments designed to optimize efficiency. They are directional signals, conceptual guides and boundary markers. Their role is not to prove success, but to inform judgment.

Good innovation metrics help leaders answer questions like:

• Are we learning fast enough?
• Where is uncertainty increasing or decreasing?
• Which parts of the organization are experimenting – and which are avoiding risk?
• Where do perceptions of risk, value, and feasibility diverge?

These are not questions that can be reduced to a single number. They require interpretation, dialogue and comparison across perspectives.

This is uncomfortable for organizations that prefer clean dashboards and decisive thresholds. But simplification in the face of uncertainty is exactly what leads to poor innovation decisions.

The Innovation Quotient as a starting point

The Innovation Quotient (IQ) was developed as a response to this challenge – not as an exhaustive measure of innovation, and not as a score to be optimized, but as a diagnostic starting point.

Its purpose is to surface patterns, norms, and tensions across key dimensions that shape innovation capacity: strategy, processes, resources, relationships and culture. Individually, none of these tells you much. Together, they begin to reveal how the system behaves.

One of the most powerful aspects of the IQ is not the absolute scores, but the differences it reveals – between functions, between leadership levels, and between how people perceive intent and how they experience reality.

When multiple people across an organization complete the assessment, patterns emerge. Misalignments become visible. Hidden constraints surface. In practice, these differences often point directly to low-hanging opportunities – small design changes that unlock disproportionate learning.

The value lies not in precision, but in shared insight – in giving leaders a way to see what had previously been implicit.

Innovation cannot be reduced to numbers without losing what makes it valuable. But that does not mean it cannot be measured.

It means it must be measured differently –  with humility, with context, and with an explicit focus on learning rather than justification.

Organizations that mistake measurement for control end up with theatre. Organizations that use measurement to guide inquiry build capacity.

Looking ahead: Why measurement alone isn’t enough

This article has focused on why innovation measurement is so difficult – and why getting it wrong leads organizations to reinforce the very behaviors they are trying to change. But even when leaders begin to measure innovation capacity more thoughtfully, another constraint quickly becomes visible.

Learning requires exposure. Exposure creates vulnerability. And most organizations are deeply uncomfortable with relationship risk – uncertainty about how others will behave, what will be shared, and who might be blamed when experiments fail.

In Part 3, this series turns to trust – not as a cultural aspiration, but as a practical mechanism for managing relationship risk. We will examine why control-heavy systems slow learning, how trust enables faster experimentation, and why organizations that over-control risk often end up increasing it.

If measuring the wrong things sends organizations in the wrong direction, then managing the wrong risks ensures they never change course.

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