Computing Tool

Computing Maturity Diagnostic

Assess your organization's maturity across architecture, delivery, security, data, AI, sustainability, and operating discipline.

5 minutes 12 questions Anonymous

Computing maturity is not only a question of software quality or cloud usage. It is the combined capability to design systems, deliver change, protect trust, use data and AI responsibly, manage cost and sustainability, and make technology decisions through a clear operating model.

This diagnostic gives a directional view of that capability. It is not an audit, certification, or vendor assessment. It is a practical starting point for discussion and improvement.

Organization Profile (optional)

Scored Questions

Architecture and platform maturity

Question 1: How clearly are your systems divided into modules, services, or ownership boundaries?
Question 2: How well can your architecture absorb new features without creating excessive coupling?

Delivery and DevSecOps maturity

Question 3: How repeatable is your release process?
Question 4: How early are quality and security checks integrated into delivery?

Security and operational trust

Question 5: How clearly are sensitive data, roles, permissions, and trust boundaries managed?
Question 6: How prepared are you to detect, respond to, and learn from incidents?

Data and AI readiness

Question 7: How reliable and governed is the data used for decisions?
Question 8: How ready is the organization to use AI in workflows responsibly?

Sustainability and cost discipline

Question 9: How visible are infrastructure, cloud, software, and operational costs?
Question 10: How deliberately do you manage compute waste and operational sustainability?

Governance and operating model

Question 11: How are technology decisions connected to business, institutional, or mission outcomes?
Question 12: How consistently do leaders review digital capability, risk, and improvement priorities?

Limitations

This diagnostic is intentionally lightweight. It does not inspect systems, validate security controls, evaluate legal compliance, or measure AI model performance. Anonymous, aggregate completion data is recorded so the diagnostic can be improved over time. No raw answers, contact details, or identifying information are stored.

Back to Computing
Version: v0.1 - Last reviewed: 2026-05-09