Measuring Progress: Altitude Gained, Not Just Distance Covered

Opening Scene

Two climbers can cover the exact same horizontal distance along a long switchback trail, yet only one of them has actually gained meaningful altitude toward the summit — distance walked is easy to log, but real elevation gained is what actually matters, and it takes more careful measurement to see. An organization tracking “number of logins” to a new AI tool as its headline success metric is logging distance walked, while the real question — whether the tool is genuinely changing how work gets done — goes largely unmeasured.

In Plain English

Measuring AI adoption well means tracking genuine behavior change and delivered value, not just surface activity like login counts or licenses distributed. Genuine altitude — actual changed behavior and outcomes — is a different, harder measurement than distance covered — raw activity counts that look like progress without necessarily being it.

The Old Way

Before organizations distinguished real adoption from surface activity, the easiest numbers tended to become the only numbers:

Success was measured almost entirely by adoption-adjacent activity: licenses purchased, accounts created, training sessions scheduled.
Whether the tool was actually changing daily work, or just sitting unused after the first login, went largely untracked.
Vanity metrics made rollouts look successful right up until the moment that illusion became impossible to sustain.

Tracking real elevation, not just steps taken, is the discipline this kind of measurement was missing.

What’s Changing (and Why AI Is the Reason)

Organizations are shifting to metrics that capture genuine behavior change: sustained usage, task completion changes, and quality outcomes, not just initial activity.
This connects to the precision covered in this content library’s dedicated data governance frameworks series, since measuring AI adoption rigorously draws on the same discipline of defining metrics precisely that governance work depends on.
Because AI tools are easy to “use” superficially without real behavior change — a login without genuine reliance — organizations increasingly need adoption metrics specific enough to catch that exact gap.

The Metaphor, Fully Extended

The Expedition	Change Management Concept
Distance walked along a switchback trail	Surface activity like logins or licenses distributed
Actual altitude gained toward the summit	Genuine behavior change and value delivered
A guide tracking real elevation, not just steps taken	Leadership tracking real outcomes, not just usage counts
Two climbers who covered equal distance but unequal altitude	Two teams with equal login counts but very different real adoption

For Beginners: What to Actually Do

Ask what specific metric is being used to judge whether an AI rollout is working, and whether it measures real behavior change.
Notice the difference between logging into a tool once and actually relying on it in your daily work.
Report honestly whether you’re genuinely using a new tool, not just technically having access to it.

For Practitioners and Leaders: The Deeper Layer

Define adoption metrics around sustained usage and real outcome change, not initial activity counts.
Build in a way to distinguish genuine reliance on a tool from a single login that never repeats.
Apply the same rigor to defining adoption metrics that data governance work applies to defining any other metric.

Quick Recap

Real adoption progress looks like altitude gained, not distance covered.
Login counts and license numbers are easy to log but don’t measure genuine change.
Organizations are shifting toward metrics that track sustained usage and real outcomes.
Precise metric definition matters as much here as it does in any other governance discipline.

Where This Fits in the Series

Article 10 covered recognizing change fatigue as a genuine risk to the climb. Article 11 covers how to actually measure whether the climb is working despite that risk — by tracking real altitude, not just activity. Article 12 turns to a related decision leaders often face partway up: whether to bring in outside guides, and when hiring consultants genuinely helps versus when it doesn’t.