Two decisions in a World Cup match—one automated and uncontested, one judgment-based and disputed—illustrate a principle that applies directly to business AI adoption. Routine, measurable decisions benefit from automation, but harder calls requiring judgment and experience will remain dependent on humans. As AI handles more routine work, leaders face the opposite of what they often expect: the decisions left to humans become more important, more visible, and more dependent on expertise.
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Columbia Business School professors analyzed the Balogun red card from a July 1 World Cup match, where semi-automated offside technology correctly chalked off a goal (uncontested), while a VAR system flagged contact that led to a disputed red card decision requiring human judgment.
Why it matters
The two calls show that AI excels at straightforward, measurable, binary decisions (offside, goal-line calls), but the harder judgment calls—whether contact was reckless, proportionate, or intentional—remain dependent on human expertise. Business leaders face the same split: automating routine decisions (routing requests, ad serving, fraud flagging) frees human judgment for nuanced choices that data alone cannot resolve.
What to watch
As technology improves, the territory of easy calls shrinks and the remaining decisions become more contested and visible. Leaders must identify which decisions are settled once data arrives, then accept that the rest will require more expertise and judgment than ever—not less. The 2026 World Cup referees' role will be narrower but demand considerably more decision-making skill.
The article frames the Balogun incident—a goal correctly disallowed by semi-automated offside technology, followed by a controversial red card issued after VAR review—as a teachable moment about the proper role of AI in human decision-making. The two calls were separated by only half an hour yet illustrate fundamentally different kinds of questions: one binary and measurable (crossing a line), the other interpretive and subjective (assessing intent and proportionality).
The professors argue that business leaders often assume automation will shrink the domain where human judgment is needed. In practice, the opposite occurs. Automating routine decisions (request routing, ad selection, fraud detection) concentrates human decision-making authority in the harder, more ambiguous territory that data cannot settle. A survey cited in the article found that 44% of C-Suite executives would override an AI-recommended decision—a finding the authors suggest depends on what type of decision is being overridden. Overriding an offside call adds only friction and error; overriding a judgment-heavy call may reflect necessary expertise.
The article concludes that referees in 2026, like business leaders adopting AI, should expect the same trade-off: the job becomes narrower (fewer routine decisions to make), but the remaining decisions demand considerably more expertise, judgment, and decision-making skill. Leaders must design collaboration protocols that distinguish between decisions that data can settle and those that require human experience, while guarding against both over-reliance on systems (automation bias) and under-reliance on AI (algorithm aversion).
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