The Judgment Bottleneck: Leading Strategy at AI Speed

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Executive Brief | Teguh Anantawikrama – Based on McKinsey & Company’s October 2026 Report

Executive assessment

AI is rapidly accelerating research, financial analysis, scenario modeling, and strategic monitoring. However, the ability to interpret evidence, challenge assumptions, build organizational agreement, and take responsibility remains constrained by human capacity. As a result, executive judgment is becoming the critical bottleneck in strategic decision-making. Companies must redesign decision processes and develop their people to translate analytical speed into business value. (McKinsey & Company, 2026)

 

What the report reveals

The productivity gains can be substantial. One executive reported completing five weeks of financial analysis in under two days. At another company, teams produced analytical outputs 10–20 times faster and explored three to five times as many scenarios, yet executives needed more time to review the results because assumptions and sources were unclear. These examples illustrate the central challenge: faster production can increase the burden of verification and delay action.

 

AI adds particular value in identifying emerging opportunities and threats, testing alternative scenarios, and monitoring predefined triggers for action. Despite so, leaders still remain essential for assessing uncertainty, recognizing missing considerations, resolving competing priorities, and mobilizing people and capital.

 

Three strategic risks

Risk

Business consequence

Speed outruns scrutiny

Unverified assumptions and automation bias can turn analytical errors into consequential decisions.

Decision overload

Excessive reports and scenarios consume leadership attention and obscure the choices that matter most.

Judgment development weakens

Junior employees lose the practical analytical experience needed to become capable future leaders.
 

Recommended decision architecture

McKinsey proposes allocating human attention according to uncertainty, stakes, and accountability.

Decision category

Appropriate operating model

Routine, with clear parameters

AI may execute within defined limits, supported by monitoring and escalation.

Uncertain or involving significant change

Humans lead; AI supports analysis, scenarios, and challenge.

High stakes or major consequences

Accountable leaders retain ownership, supported by rigorous scrutiny.
 

For example, routine inventory adjustments may be suitable for bounded automation. Entering a new market requires human-led assessment. Major acquisitions, restructuring, and safety-sensitive decisions require explicit leadership responsibility.

 

Proposed leadership agenda

The following actions translate the report into an implementation agenda for Indonesian enterprises:


  • Redesign executive submissions. Require concise decision papers identifying the proposed action, evidence, critical assumptions, uncertainties, alternatives, and accountable owner, making supporting analysis straightforward while still being accessible
  • Establish decision rights. Define which decisions AI may support or execute, the limits of that authority, and when escalation is mandatory.
  • Build judgment through practice. Require staff to explain and validate AI-generated calculations and recommendations. Combine AI training with mentoring, scenario exercises, and constructive challenge.
  • Protect accountability. Assign a named human owner to every consequential recommendation. Independent AI checks can support review; responsibility must remain clear.
  • Measure business outcomes. Track decision quality, execution, rework, and realized benefits alongside analytical speed and cost savings.
 

Implications for KADIN

KADIN can help enterprises and MSMEs adopt practical decision-governance templates and training that combine AI proficiency with business judgment. Smaller businesses particularly need simple procedures for checking sources, understanding assumptions, protecting sensitive information, and escalating consequential choices to their owners.

Leadership priority: Invest in the organization’s ability to evaluate and act on AI output at the same time as investing in the technology itself. The competitive advantage will come from faster insight, sound judgment, and accountable execution.