10+1™ Organizational AI Readiness Checklist
  • Version: v1.0
  • Last updated: 2026-02-08
  • Status: Canonical
For any team using, deploying, or relying on AI systems
Based on the 10+1 AI Ethics and Governance Framework. For more information about the 10+1 click HERE.
1
Accountability & Ownership
Who owns the outcome?
Named Human Accountability
A named human is accountable for all AI outputs and decisions.
Clear Escalation Path
Clear escalation path defined for ambiguous or high-impact cases.
Decision Logs
Decision logs are created and stored.
2
Safety Boundaries & Red Lines
Is this safe to deploy?
  • Harm boundaries, PII limits, and red lines are documented.
  • Misuse scenarios are identified and mitigated.
  • System includes fail-safes or shutdown triggers for unsafe behavior.
3
Transparency & Disclosure
Have we been clear about AI involvement?
User Understanding
Users understand when AI is being used.
Content Labeling
AI-generated content is labeled internally and externally as required.
Transparent UX
UX includes transparent explanations of how AI influences decisions.
4
Data Integrity & Truthfulness
Is the data trustworthy?
Documentation
Data sources are documented with known limitations.
Quality Checks
Checks for outdated, incomplete, or biased data have been performed.
Compliance
Sensitive data complies with privacy and confidentiality requirements.
5
Responsible Design & Development
Have we built this system with intention?
01
Risk Assessments
Risk assessments and harm checks were completed prior to launch.
02
Human-in-the-Loop
Human-in-the-loop review is applied where it matters most.
03
Comprehensive Evaluation
Evaluation includes accuracy, fairness, edge cases, and misuse risks.
6
Human Oversight & Judgment
Where does the human step in?
Key decision points requiring human review are defined.
Humans can override or contest AI recommendations.
The team knows who has authority to intervene.
7
Values & Ethical Alignment
Does this align with our values and the 10+1™?
  • AI system behavior reflects human-centered values.
  • No manipulation, coercion, or deceptive interactions.
  • Ethical dilemmas have been considered and documented.
8
Future-Readiness & Capability Growth
Are we prepared for the system to evolve?
Understanding Growth
The team understands potential capability increases.
Active Monitoring
Monitoring is in place to detect unanticipated behavior.
Regular Testing
Guardrails are tested regularly.
9
Compliance & Regulatory Fit
Does this meet internal and external requirements?
Internal Standards
Aligns with company security, risk, and governance standards.
Legal Compliance
Meets all applicable legal, regulatory, and industry expectations.
Complete Documentation
Audit logs and documentation are complete.
10
Documentation & Traceability
Can we explain what happened and why?
Recorded Decisions
Decisions, changes, and reviews are recorded.
Centralized Storage
AI model cards, risk worksheets, and SOPs are stored in one location.
Traceable Records
A traceable record exists for model versions and training data sets.
+1
Stewardship
Are we improving the system responsibly over time?
Feedback Loops
Feedback loops exist for users and stakeholders.
Continuous Monitoring
Continuous monitoring and retraining processes are defined.
Ongoing Commitment
The team is committed to ongoing responsibility, not one-and-done fixes.

FINAL CHECK — If you can't answer "YES" to all items, pause deployment.
This checklist is the minimum viable governance standard for safe, responsible, and aligned AI use across the organization.
10+1™ ROLE-BASED CHECKLISTS
Organized by the Four Pillars of Responsible AI Leadership
EXECUTIVE LEADERS (CEO, CAIO, CTO, CISO, GC)
01
Accountability & Transparency
  • Assign an accountable owner for every AI system or workflow.
  • Communicate non-negotiable transparency expectations.
  • Approve disclosure policies for internal/external use.
02
Safety & Limits
  • Define the organization's AI red lines, safety posture, and escalation model.
  • Approve harm-prevention protocols and high-risk review gates.
  • Ensure AI is never used for conflict, manipulation, or retaliation.
03
Data Integrity & Improvement
  • Fund data quality, governance, and monitoring infrastructure.
  • Ensure legal + compliance teams validate data usage policies.
  • Approve retraining and iteration standards.
04
Human Advantage & Future-Proofing
  • Maintain policies ensuring humans have final authority.
  • Incentivize AI augmentation (not replacement) of human judgment.
  • Set expectations for future-readiness and capability management.
+1 — Stewardship
  • Sponsor continuous improvement cycles.
  • Review AI Impact Reports quarterly.
  • Champion cultural adoption across the enterprise.
PRODUCT MANAGERS
1
Accountability & Transparency
  • Confirm project-level AI owner(s).
  • Document what users must know and when.
  • Capture decision logs in product requirements.
2
Safety & Limits
  • Build harm scenarios, misuse cases, and safety test plans.
  • Identify where AI must stop or defer to humans.
  • Verify compliance with PII and regulatory boundaries.
3
Data Integrity & Improvement
  • Validate sources feeding the model.
  • Include evaluation metrics, bias checks, and data limitations in specs.
  • Plan controlled retraining with data/engineering teams.
4
Human Advantage & Future-Proofing
  • Design user journeys with explainability and human override points.
  • Prioritize features that enhance human judgment, creativity, or safety.
  • Create a roadmap that anticipates capability expansion.
+1 — Stewardship
  • Maintain product-level monitoring dashboards.
  • Ensure user feedback informs model improvements.
ENGINEERING & MLOPS
1 — Accountability & Transparency
  • Tag outputs as AI-generated where relevant.
  • Implement versioning + traceability for all models.
  • Log decisions, training runs, and system changes.
2 — Safety & Limits
  • Run harm, risk, and adversarial testing in CI/CD pipelines.
  • Implement kill switches, throttles, and boundary checks.
  • Enforce secure handling of sensitive inputs.
3 — Data Integrity & Improvement
  • Validate data quality, completeness, and bias before use.
  • Track data provenance with lineage tools.
  • Monitor drift, hallucinations, and system anomalies.
4 — Human Advantage & Future-Proofing
  • Build human-in-the-loop checkpoints into workflows.
  • Implement transparent model behaviors (e.g., explainability APIs).
  • Flag unexpected capability growth immediately.
+1 — Stewardship
  • Maintain continuous monitoring.
  • Produce monthly model stability reports.
DATA TEAMS (Data Science, Data Engineering, Analytics)
1 — Accountability & Transparency
  • Document source systems and data assumptions.
  • Flag limitations & uncertainties early.
  • Maintain data documentation accessible to all teams.
2 — Safety & Limits
  • Enforce privacy, consent, PII, and minimization standards.
  • Evaluate potential harms derived from data misuse.
  • Validate datasets for security and integrity.
3 — Data Integrity & Improvement
  • Run quality checks, bias detection, and validation protocols.
  • Manage retraining datasets with strict version control.
  • Ensure updates incorporate monitoring feedback.
4 — Human Advantage & Future-Proofing
  • Communicate data limitations that impact downstream decisions.
  • Build dashboards that help humans understand AI behavior.
  • Flag indicators of capability expansion the moment they appear.
+1 — Stewardship
  • Support quarterly data/AI governance audits.
  • Maintain pipelines that keep models honest and accurate.
SECURITY, RISK & COMPLIANCE
1
Accountability & Transparency
  • Ensure accountability chains exist for all high-risk AI uses.
  • Maintain audit trails accessible to regulators and investigators.
  • Approve disclosure and transparency standards.
2
Safety & Limits
  • Assign risk scores to each AI system and use case.
  • Conduct red-team tests; validate threat models.
  • Define and enforce escalation triggers for harms.
3
Data Integrity & Improvement
  • Validate compliance with privacy laws (GDPR, CCPA, etc).
  • Confirm lawful data use and data minimization practices.
  • Review retraining processes for compliance risk.
4
Human Advantage & Future-Proofing
  • Ensure humans can override AI in any regulated domain.
  • Assess future capability risks and vendor risk profiles.
  • Ensure explainability meets regulatory standards.
+1 — Stewardship
  • Manage incident response plans involving AI.
  • Conduct annual or biannual AI governance maturity reviews.
HR / PEOPLE / L&D
1 — Accountability & Transparency
  • Define acceptable use policies for all employees.
  • Communicate disclosure requirements company-wide.
  • Track completion of AI literacy & governance training.
2 — Safety & Limits
  • Train employees on red lines and sensitive use cases.
  • Monitor for misuse or shadow AI activity.
  • Add AI risk awareness to onboarding.
3 — Data Integrity & Improvement
  • Ensure employee training datasets (e.g., internal text) respect privacy.
  • Vet LLM-based HR tools for fairness and compliance.
  • Include accuracy & bias checks in HR workflows.
4 — Human Advantage & Future-Proofing
  • Train employees to work with AI, not around it.
  • Promote human creativity, judgment, and leadership.
  • Prepare workforce development plans anticipating AI capability growth.
+1 — Stewardship
  • Maintain ongoing training cycles.
  • Support cultural reinforcement of ethical AI use.
ALL EMPLOYEES
1 — Accountability & Transparency
  • Disclose when using AI to generate work.
  • Do not manipulate, deceive, or mislead with AI.
  • Follow documented approval workflows.
2 — Safety & Limits
  • Stay within red lines and PII boundaries.
  • Escalate any concerning or unsafe AI behavior immediately.
  • Only use approved tools.
3 — Data Integrity & Improvement
  • Use truthful, high-quality data when prompting or training.
  • Do not expose sensitive or regulated information.
  • Report errors, drift, or incorrect outputs.
4 — Human Advantage & Future-Proofing
  • Apply human judgment before taking action on AI output.
  • Ask for clarification when AI results seem incorrect.
  • Offer feedback to improve system behavior.
+1 — Stewardship
  • Treat AI as part of the system — contribute to its responsible evolution.
  • Provide feedback through designated channels.
Meet the Creator of 10+1
Cristina DiGiacomo is a philosopher of systems who builds ethical infrastructure for the age of AI. She is the founder of 10P1 Inc. and creator of the unknown link, a decision-making tool used by CEOs, CISOs, CAIOs, and compliance leaders navigating high-stakes AI environments. Her work bridges governance and execution, helping organizations embed moral clarity into complex systems. Cristina is the author of the #1 bestselling book Wise Up! At Work, and has received multiple awards for both her strategic and philosophical work, including from The New York Times, Cannes Cyber Lions, and IAOTP. She currently leads the C-Suite Network AI Council and speaks regularly on Responsible AI, Systems Ethics, and moral leadership.
© 10P1 Inc — The 10+1™ Commandments of Human AI Co-Existence
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