When we walk, our thoughts often loosen. The steady rhythm of our steps and the predictable flow of the environment allow our minds to drift toward the intuitive patterns psychologists call System 1. Long, simple stretches of road while driving can feel similar—automatic routines taking on much of the work while we remain lightly aware. 

But as complexity increases—dense traffic, sudden shifts, unpredictable motion—our cognition gradually leans toward the more effortful, analytical processes of System 2. Safety depends not on avoiding mind-wandering altogether, but—as our cognitive resources are limited—on whether our mode ofthinking fits the situation’s demands. Misalignment occurs when complexity rises faster than our attention adjusts. 

This interplay between intuition and effort offers a powerful metaphor for thinking about Artificial Intelligence. AI does not naturally rebalance its processing modes in response to context the way humans do. It moves through information at extraordinary speed, and that speed makes initial guidance profoundly consequential: unclear direction can produce errors that scale rapidly. Modern AI, therefore, depends on two layers of input that shape its behavior. 

The first layer is data. “Garbage in, garbage out” remains a core truth. Data is always a partial trace of the past—filtered by what was measured, who measured it, and what remained invisible. Outdated or biased patterns quietly distort the model’s internal map. In volatile settings, extending yesterday’s data into tomorrow requires continual updating and contextual awareness. 

The second layer is the directive. This input—whether shaped through system design, specific queries, or a prompt—is not just raw data; it functions as direction, context, constraint, and intent. Just as humans can misjudge situations when relying on automatic habits in environments that require deliberation, AI can misfire when processing vast information at high speed without clear framing. Ambiguous instructions—or those relying on implicit assumptions—do not merely produce weak answers; they can generate confident, coherent misjudgments. Designing effective directives is, in essence, about alignment, often supported by the guiding shells and guardrails built into the model itself. 

Still, AI is not the universal tool. Many advanced models are stochastic, opaque, and data-intensive. They excel at unstructured problems—language, imagery, open-ended reasoning—but can be difficult to govern in high-stakes settings requiring stability or transparent justification. In such contexts, deterministic approaches—classical statistical models or rule-based systems—are often more trustworthy and easier to audit, prioritizing explainability over predictive power. Selecting the tool that fits the problem is an act of responsible leadership. 

Models, by their nature, simplify the world into measurable parts. Yet much of what matters in organizations—trust, culture, meaning, leadership—is emergent. These qualities can be studied, but they cannot be fully captured by isolated variables. Turning data into wisdom requires more than computation: it calls for context, judgment, ethical reflection, and an understanding of people. 

In this broader, managerial sense of alignment, the goal is coherence across purpose, method, and impact. Alignment begins with objective clarity—what we are genuinely trying to achieve—and then expands outward to business constraints, stakeholder realities, and the human values that make progress sustainable. Businesses do not stand apart from society, and technology does not stand apart from people. 

As volatility increases, leaders must ensure that core competencies do not harden into core rigidities. In an era of algorithmic speed, adaptability and reflective awareness become essential. Machines will continue to operate at machine speed; humans will continue to think and decide at human rhythm. Wise leadership lies in bridging these tempos—aligning method with meaning, speed with judgment, and technological possibility with human purpose. 

November 23, 2025

Kyungchan Park

An abridged version of this essay, titled “Bridging the Tempos: Aligning Human Intent with Machine Speed,” was published as a Faculty Insights contribution in The CIAM Perspective, CIAM’s newsletter, in the December 1, 2025 issue on “Artificial Intelligence Is Now.”