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Friction Courage¶
Removing structural obstacles rather than just "supporting" change.
The Argument¶
Most leadership models treat change support as an attitudinal matter: leaders should "champion" adoption, "communicate the vision," and "empower" their people. These are necessary but radically insufficient. The primary barriers to AI adoption in most organizations are not motivational — they are structural. Procurement processes that take nine months. Legal reviews designed for software contracts applied to AI services with entirely different risk profiles. Data governance policies written before large language models existed. IT architectures that prevent experimentation. Performance metrics that reward activity over outcomes.
Friction Courage is the willingness and ability to identify and dismantle the structural barriers that prevent AI adoption — even when those barriers are defended by powerful constituencies, embedded in established processes, or protected by organizational inertia. The word "courage" is deliberate. Structural barriers exist because someone created them, someone maintains them, and someone benefits from their continuation. Removing them is a political act, not merely an operational one.
This dimension draws on institutional theory (DiMaggio & Powell, 1983) and the concept of organizational drag. Every organization accumulates procedural sediment — processes that made sense when created but now impede adaptation. AI adoption accelerates the rate at which existing structures become obstacles, because AI changes what is possible faster than bureaucracies change what is permitted.
Three Levels¶
| Level | What This Looks Like | Red Flags |
|---|---|---|
| Leading Self | Willing to challenge established processes when they impede AI adoption. Does not hide behind "that's just how we do things." Takes personal risk to remove obstacles. | Defers to existing process even when it visibly blocks progress. Avoids conflict with peer functions. Frames structural barriers as unchangeable constraints. |
| Leading Teams | Actively identifies structural friction points that slow the team's AI work. Escalates barriers that require organizational authority to resolve. Shields teams from unnecessary bureaucratic load. | Acknowledges friction but takes no action to resolve it. Asks teams to "work around" structural barriers. Treats every obstacle as someone else's problem. |
| Leading Systems | Redesigns organizational processes — procurement, legal review, data access, performance management — to accommodate AI's pace and characteristics. Builds fast-track mechanisms for AI experimentation. | Applies legacy governance frameworks to AI without adaptation. No expedited pathways for experimentation. Structural barriers are documented but never addressed. |
Observable Behaviors¶
- Maintains a visible "friction log" — a documented list of structural barriers to AI adoption, with owners and resolution timelines.
- Has personally intervened to change or bypass at least one organizational process that was blocking AI experimentation.
- Engages peer functions (legal, procurement, IT, HR) as partners in redesigning processes, rather than adversaries to be circumvented.
- Distinguishes between friction that protects (legitimate governance) and friction that merely impedes (procedural inertia), and acts differently on each.
- Tracks time-to-experiment and time-to-deploy as organizational health metrics, and acts when they exceed acceptable thresholds.
Development Pathways¶
Map the friction. Spend a week documenting every structural barrier your team encounters in AI work. Not complaints — specific process steps, approval chains, and policy requirements that add time without adding value. The map itself is a leadership tool.
Pick one battle. Select the single highest-impact friction point and commit to resolving it within 90 days. Document the effort. The first successful removal creates precedent and builds the political capital for subsequent ones.
Build cross-functional alliances. The most consequential friction lives at functional boundaries. Invest in relationships with legal, procurement, IT security, and HR leaders. Understand their constraints. Co-design solutions that address their concerns while reducing drag.
Reframe governance as enablement. Work with governance functions to redesign AI-specific processes. The goal is not to eliminate oversight but to create pathways proportionate to risk. A sandbox experiment and a production deployment do not require the same review.
Measure friction systematically. Establish metrics for structural friction — days from idea to experiment, approval steps per AI project, percentage of AI initiatives delayed by process rather than technical challenges. What gets measured gets managed.