AI Integrity Management
Circle of The Integral Management Society
Tegrity.AI is an international working group advancing AI Integrity Management across safety, governance, compliance, ethics and operational integrity.
Our vision is broad. Our work is concrete.
Our current initiative advances Structural Awareness for Operational Integrity in Adaptive Systems as a foundational capability for AI Integrity Management.
The circle focuses on practical frameworks, independent validation and real-world initiatives for trustworthy AI adoption.
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Tegrity.AI Members & Roadmap
Hosted by The Integral Management Society, Tegrity.AI aims to bring together researchers, architects, engineers, operators, academics and governance specialists working across complexity, adaptive systems, AI integrity, computational thermodynamics, systemic risk, operational resilience and mission-critical environments.
The circle welcomes senior professionals with strong field experience or research depth, including contributors from mathematics, finance, quantitative systems, enterprise architecture, cybersecurity, compliance, public policy, industrial operations and AI governance. However, it is not an open public forum, but a working circle for serious contribution.
Our current core workstream focuses on validating a framework for Structural Awareness with regime change detection in Adaptive Systems: a regime-awareness capability derived from real industrial and operational experience, now being re-examined and formalised as a domain-agnostic framework.
The roadmap moves from independent validation to industry and academic pilots, and then toward the publication of an open-source reference framework for broader research, benchmarking and operational adoption.
1 Independent Validation
Initial validation with independent experts, specialists and institutions to challenge assum-ptions, refine the theoretical model and confirm its relevance across different domains.
2 Co-Development and Pilot Projects
Joint work with industry, researchers and public-sector stakeholders to adapt the frame-work to concrete environments, build pilot implementations and test real operational value.
3 Open-Source Publication
Publication of the framework as an open-source and openly documented reference model, enabling wider adoption, contribution, benchmarking and further development by the broader community.
Tegrity.AI Additional Workstreams
Foundation of AI Integrity Management Discipline: Definition & Boundaries
Tegrity.AI introduces and define the concept of «AI Integrity Management»
This workstream explores whether AI Integrity Management can emerge as a formal enterprise discipline: not replacing AI governance, ethics, cybersecurity, compliance or MLOps, but creating the coordination layer where these domains converge.
As AI moves into mission-critical operations, real failures often cross these boundaries at once.
The approach examines where orchestrating AI Integrity Management adds value or just increase governance overhead.
AI Integrity Management Architecture
This workstream examines the architecture of enterprise AI systems under real operational pressure: statistical AI cores surrounded by deterministic guardrails, rules and compliance checks.
It explores what happens when the environment changes, when protective envelopes become insufficient or even fragile, and when systems move from manageable complexity into emergent failure.
The focus is architectural: how to design AI integrity layers that remain useful under regime change, operational stress and mission-critical conditions.
Human Intelligence Debt
Tegrity.AI introduces and define the concept of «Human Intelligence Debt»
This workstream examines if and how organisations systematically waste human intelligence when people are forced to reconcile fragmented systems, re-enter data, validate outputs and compensate for architectural gaps.
We define Human Intelligence Gap as the distance between what people could contribute — judgment, strategy, design and knowledge creation — and what they actually do when technology, data and operating models remain poorly integrated.
The workstream asks a central question for the AI era: is artificial intelligence closing this gap, or widening it further?
New Workstreams Exploration
Tegrity.AI consider potential new workstreams across the broader AI Integrity Management landscape.
The aim is to share hard-earned knowledge, challenge assumptions, and develop practical frameworks, methods and outcomes for responsible AI adoption.
Applied Research & Field Practice
Tegrity.AI builds on decades of practical architecture work across expert systems, decision-support systems, business intelligence, cybersecurity, compliance, logistics intelligence, machine learning, process mining and adaptive control architectures.
The technologies have changed, but the core problem has remained stable: designing systems that preserve reliability, explainability and operational integrity when complexity, uncertainty and cascading effects increase.
Core Workstream: Structural & Regime Awareness for Operational Integrity in Adaptive Systems
Our core workstream develops early-warning mechanisms that identify when current operating conditions are losing stability.
Combined with guardrails, explainable AI and dynamic buffer control, this enables organisations to reinforce capacity, preserve critical redundancy and act before local disruptions become systemic failures.
AI Integrity Management. Our Definition
AI Integrity Management is the discipline of ensuring that AI-enabled systems remain explainable at their core, secure by design, integrable with legacy and third-party environments, resilient under failure, flexible in deployment, disciplined in cost, and accountable for delivering real operational and human value.
Tegrity.AI Governance Structure
The Integral Management Society (IMSV.org) is a Swiss scientific and technological society based in Geneva. Here there is the specialised circles structured propossal (in formation).
- Frontier Operators
Companies, engineering groups and operational actors contributing implementation capability, field experience, frameworks, code and real-world validation environments. - Academic & Research Support
Universities, researchers and scientific collaborators supporting theoretical review, methodological refinement and long-cycle validation. - Observatories & Institutional Stakeholders
Observers, associations, regulators, policy specialists and institutional actors contributing external review, governance perspectives and societal alignment. - Patronage Layer
Individuals, institutions and strategic supporters contributing access, relationships, influence, knowledge, sponsorship and financial capital to support pilots and long-cycle continuity. - Inner Circle
Core working layer composed of senior validators, scientists, engineers, architects and domain specialists responsible for framework structuring, technical coherence and validation.
This structure is designed to preserve serious contribution, operational realism and long-cycle continuity across industry, academia and institutional environments.
The Integral Management Society Ecosystem
The Integral Management Society is a Swiss non-profit scientific and technological society. It is not a consulting platform, a visibility network or a networking club.
Its purpose is to contribute to the public interest and the benefit of humanity through specialised circles working on complex systems, deep tech, governance, operational integrity and capability preservation.
Because many initiatives involve sensitive capabilities, advanced architectures and high-trust environments, discretion is structurally important. Participation is based on responsibility, demonstrated contribution and appropriate confidentiality.
The society has a clear vocation for economic self-sustainability. It may support pilots, remunerate contributors for their time when projects are funded, and enable economic growth through its Frontier Operations Circle.
This economic dimension is instrumental, not the purpose of the society. In practice, participation can strongly contribute to the professional trajectory of contributors, but the core aim remains serious work, long-cycle capability and useful outcomes.
Standards, Frameworks & Forums
Tegrity.AI uses selected standards, frameworks and public forums as a reference landscape for AI Integrity Management. They are references, not endorsements, certifications or formal affiliations unless explicitly stated.
Governance, Risk & Audit
- CISA / CISM: Disciplines for auditability, information security and global governance
- ISO 31000: Principles for enterprise-wide and operational risk management
- TBM (Technology Business Management): Principles for cost transparency, investment discipline and technology value creation
Enterprise Architecture & Technology
- TOGAF: Principles for structural architecture, portfolio rationalisation and complex systems integration
- Cloud Architecture: Practices informed by AWS professional-level design principles and vendor-neutral enterprise cloud architecture
- Enterprise Platforms: Disciplines shaped by SAP, IBM and large-scale systems integration experience
Quality, Delivery & Operational Excellence
- ISO 9001: Frameworks for quality management and disciplined project delivery
- ITIL: Standards for service management and operational control
- Lean Portfolio Management (SAFe): Methodologies for prioritisation, governance and transformation flow
- Six Sigma / Operational Excellence: Data-driven approaches for process improvement, performance and defect reduction
Integrated Management Systems
- Compliance & Audit: Expertise in ISO 9001, ISO 14001 and OHSAS 18001 standards, specifically applied to industrial and high-stakes operational environments
AI Governance & Assurance
EU AI Act
NIST AI RMF
ISO/IEC 42001
AI Standards Bodies
ISO/IEC JTC 1/SC 42
CEN-CENELEC JTC 21
IEEE AI standards initiatives
Public AI Forums
AI for Good / ITU
OECD.AI Policy Observatory
Partnership on AI
Architecture & Governance
TOGAF
ArchiMate
COBIT
Cybersecurity & Risk
ISO/IEC 27001
NIST Cybersecurity Framework
ISO 31000
Operational Intelligence
Process Mining
Application Portfolio Management
Operational observability
Operational Excellence
ITIL
Lean Six Sigma
Integrated Management Systems
Regime Awareness
Early Warning Systems
Dynamic Guardrails
Structural Self-Awareness
Research Foundations
Computational Thermodynamics
Complex Adaptive Systems
Systemic Resilience
These references help frame AI Integrity Management across standardisation, governance, architecture, cybersecurity, assurance, operational control and adaptive systems research.
Articles and Notes
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About Cats, Boxes and Enterprise Management Decisions
Three Ways a Fact Can Go Missing – Tegrity.AI Three Ways a Fact Can Go Missing Between the fact you can find and the fact no one has decided, there is a third — and a cat that tells the…
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Structural Awareness
Tegrity.AI · The Integral Management Society Synthesis & Index Synthesis & Reader’s Index The Whole Program The whole program in one read — every idea explained, every series and paper linked, so this page works as both a synthesis and…
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The Outlook
What the program forecasts about the near future — anchored on its main prediction: that automation, applied to the systems we actually run, produces more inefficiency and more work before it produces fewer jobs. Document Status — Companion Paper (The…
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Why Selection Cannot See It
Insolvency, positive deviation, and a case (Nokia) in which market and evolutionary predictions pointed confidently in the wrong direction — the case that motivates the claim that the two instruments are complementary, not rival. Why selection does not refute this…
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Optimization, Coherence, and the Frame
Why a system optimizing on its own map cannot see the map; why coherence — not rationality — is the mode that sees it; and why a purely maximizing agent is structurally blind to exactly the capabilities that keep a…
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The Map and The Flow
Informational friction as a systems problem — why a representation that governs a flow it cannot fully observe tends to deform that flow, under conditions this paper makes explicit. Stated for systems in general, human or not. Document Status —…
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Attribution Gap Meassurement Protocol
Where Human Intelligence Debt and the Attribution Gap arrive at the same place — offered as an observation, not a proof. Document Status — Field Note · Series: The Attribution Gap and Capability Loss Tegrity.AI · The Integral Management Society…
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Bridge: The Attribution Gap and Capability Loss
Where Human Intelligence Debt and the Attribution Gap arrive at the same place — offered as an observation, not a proof. Document Status — Field Note · Series: The Attribution Gap and Capability Loss Tegrity.AI · The Integral Management Society…
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The Attribution Gap and Capability Loss
An architecture theory of why enterprise rationalization destroys the capabilities a firm most depends on — with a fenced conjecture toward the wider economy. Document Status — Foundational Paper · Anchor Article · Working Paper v8 · Series: The Attribution…
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The Minimal Information a Transformation Needs
A field account of the basic information every transformation requires before it can begin — why it is rarely treated as a project cost and risk, how that omission distorts the business case, and how estimating its price may help…
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What Goes Unpriced Is Paid in Human Intelligence
Cross-series bridge — why the unpriced cost of producing clarity does not vanish but is paid as misallocated human intelligence, and why that misallocation is the Debt the Gap series measures Document Status — Field Notes (Cross-Series Bridge) · Series:…
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Is Clarity Getting More Expensive?
A conjecture, now testable: that rising system entanglement is driving up both the cost and the risk of knowing how your own work runs — wave after technological wave Document Status — Paper 6 · Series: The Cost of Clarity.…