Measuring What Matters: Beyond Vanity Metrics

By Leah C. Jochim | Convergence Technology Solutions

Every organization I've worked with has a metrics problem. Not a shortage of metrics — most have too many. The problem is that the metrics they're tracking are not the metrics that tell them whether the business is actually working.

Vanity metrics are seductive because they trend upward. Total users, features shipped, meetings held, training sessions completed. These numbers go up over time almost regardless of whether the organization is performing well. They create the feeling of progress without the substance of it.

The discipline of identifying and tracking metrics that actually matter — that genuinely indicate whether the organization is achieving its strategic objectives — is one of the most important and most difficult things a leadership team can do.

The Vanity Metric Test

A vanity metric has a specific characteristic: it can improve while the business gets worse. Total users can increase while revenue per user decreases. Features shipped can increase while customer satisfaction decreases. Training sessions completed can increase while behavior change doesn't happen.

The test I apply to any proposed metric is this: can I construct a plausible scenario where this metric improves but the objective fails? If yes, it's a vanity metric. It may be worth tracking as context, but it shouldn't be a key result.

The metrics that pass this test are the ones that are genuinely predictive of the outcomes that matter — that are causally connected to the business results you're trying to achieve, not just correlated with activity.

Leading vs. Lagging Indicators

One of the most important distinctions in metric design is between leading and lagging indicators.

Lagging indicators measure outcomes that have already occurred. Revenue, customer retention, market share — these are lagging indicators. They tell you how the business performed. They don't tell you whether you're on track until after the fact.

Leading indicators measure the conditions that predict future outcomes. Pipeline conversion rate, customer engagement score, employee retention — these are leading indicators. They tell you whether you're building the conditions for future success.

Effective measurement frameworks use both. Lagging indicators provide the accountability for outcomes. Leading indicators provide the early signal that enables course correction before it's too late.

In the OKR framework I implemented at a well-known Fortune 10 bank, we were explicit about this distinction. Every key result was designed to include at least one leading indicator and one lagging indicator. The leading indicator told us whether we were on track. The lagging indicator told us whether we succeeded.

The AI Measurement Challenge

AI transformation creates a specific measurement challenge that I encounter frequently in my advisory work: the metrics that are easy to measure are almost never the metrics that matter.

It's easy to measure AI tool adoption — license counts, login rates, query volumes. These metrics are readily available and trend upward. They're also almost entirely disconnected from business outcomes.

The metrics that matter for AI transformation are the ones that measure whether AI is actually changing how work gets done: cycle time reduction, error rate improvement, decision quality improvement, employee time reallocation from low-value to high-value work. These metrics are harder to measure, require more investment in instrumentation, and are less likely to trend consistently upward in the early stages of adoption.

But they're the only metrics that tell you whether the AI investment is working. And organizations that measure AI adoption rather than AI impact are setting themselves up for a rude awakening when the board asks whether the investment was worth it.

Building a Measurement Framework That Works

The measurement frameworks I've seen work most effectively share three characteristics.

They start with the business outcome and work backward. What is the strategic objective? What leading indicators predict movement toward that objective? What AI or technology capabilities are designed to move those leading indicators? This chain of causality is the foundation of a measurement framework that actually tells you something.

They are designed for decision-making, not reporting. The question for every metric is: if this number changes, what decision would we make differently? If the answer is "none," the metric doesn't belong in the framework.

And they are reviewed regularly with a focus on learning. The value of a measurement framework is not in the numbers themselves but in the conversations they enable. What are the numbers telling us about what's working and what isn't? What are we changing as a result?

Leah C. Jochim is Co-Founder & Partner at Convergence Technology Solutions. She has implemented OKR and measurement frameworks at Microsoft and a well-known Fortune 10 bank, and advises organizations on AI transformation measurement and governance. Connect at linkedin.com/in/leahac.

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