AI-Ready Leader

About AI-Ready Leader

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AI-Ready Leader is an evidence-based diagnostic system developed to benchmark executive leadership in the era of generative AI and autonomous agents.

Who We Are

The AI-Ready Leader Research Team is a multidisciplinary group of technology executives, organizational design consultants, and behavioral assessment researchers. Our mission is to bridge the critical divide between fast-moving AI capability and traditional managerial decision-making, helping leaders avoid transition bottlenecks without succumbing to hype or skepticism.

Methodology & Situational Judgment Design

Unlike self-reported questionnaires that measure superficial adoption, our assessment uses 28 situational judgment test (SJT) scenarios. Each scenario simulates real-world executive dilemmas across five core capability dimensions (Task Delegation, Decision Quality, Quality Assurance, Team Enablement, and Workforce Capacity), mapping choices to validated maturity levels from M0 (Ad-hoc) to M3 (Autonomous System).

Foundational Theories & References

Peer-reviewed research, organizational behavior literature, and classical management frameworks grounding our assessment

Andrew S. Grove (Vintage / Random House)

High Output Management

Peter F. Drucker (Harper & Row)

The Practice of Management & MBO

Henry Mintzberg (Berrett-Koehler)

Managing & Managerial Information Roles

Ikujiro Nonaka & Hirotaka Takeuchi (Oxford University Press)

The Knowledge-Creating Company & SECI Model

Amy Edmondson (Administrative Science Quarterly / JSTOR 2392745)

Psychological Safety and Learning Behavior in Work Teams

Eliyahu M. Goldratt & Jeff Cox (North River Press)

The Goal: A Process of Ongoing Improvement & TOC

Charles Goodhart (Bank of England / Papers in Monetary Economics)

Problems of Monetary Management (Goodhart's Law)

NIST Baldrige Performance Excellence Program

Total Quality Management (TQM) & Inspection Point Principles

National Institute of Standards and Technology (NIST)

Artificial Intelligence Risk Management Framework (AI RMF 1.0)

Ziwei Ji et al. (arXiv:2202.03629 / ACM Computing Surveys)

A Survey of Hallucination in Large Language Models

Data Minimalization & Privacy Protection

We adhere to a strict zero raw answer retention guarantee: all individual question choices are permanently deleted from database storage as soon as your composite score snapshot is generated. Sessions remain anonymous and your encrypted report dossier is accessible solely via your confidential report link.

Privacy

Contact Us

Have questions about the assessment, enterprise team inquiries, or need order assistance? We are here to help.