Beyond Compliance: Towards a Values-Driven Framework for AI Governance

Why compliance alone is not enough in high-stakes organisational environments, and the research case for a values-driven framework for AI governance.
Picture of Dr. Yassin Miheisi

Dr. Yassin Miheisi

Published March 2026 · 7 min read

Six controls, one missing: a stream of AI decisions flows through six gates, and one gate is missing
The paper in brief

Governance that satisfies audits is not the same as governance that works

Ethical principles for AI are abundant; their translation into organisational practice is not. The paper argues that compliance-oriented governance, built around documentation and conformity checks, is valuable but structurally insufficient where AI shapes decisions about people’s access to credit, work, care or public services.

The reason is decoupling: formal structures that satisfy auditors while leaving consequential decisions unchanged. Drawing on institutional theory, business ethics research, stakeholder theory and the risk- and rights-based traditions of AI governance, the paper derives six design requirements and proposes the Ethical Governance Integration Model (EGIM). It maps the model to ISO/IEC 42001, illustrates it with a composite retail bank, and sets out implications for auditors, certification bodies and standard-setters.

The problem

The audit readiness paradox

Institutional theory distinguishes two ways formal governance drifts from practice. In policy–practice decoupling, policies are adopted but not implemented. In means–ends decoupling, they are implemented, but the activities they mandate are only loosely connected to the outcomes they exist to secure. Mature AI governance is most exposed to the second: every risk assessment can be completed and filed without any of them altering a design, delaying a deployment or changing how a system treats people.

The paper calls this the audit readiness paradox: the more completely governance is organised around demonstrable conformity, the easier it becomes to achieve conformity without achieving its purpose. This is not a claim that audits are futile. ISO/IEC 42001 already requires organisations to evaluate the effectiveness of their AI management system. The question is what counts as evidence of effectiveness.

What the documents show
What the decisions show
A board-approved AI policy
No use case has ever been declined or delayed on ethical grounds
An AI governance committee with terms of reference
It has never conditioned or halted a deployment
Impact assessments on file for high-risk systems
They are risk registers with no group-level impacts

Illustrative examples from the paper’s composite case.

From theory to design

Six design requirements

The paper translates each theoretical lens into a requirement that any governance model must meet if it is to resist decoupling.

R1

Evidence of effect

Every mechanism must generate evidence that it changes decisions, not only that it exists.

Lens: Institutional theory (decoupling)
R2

Build on compliance

Values-driven mechanisms sit on, not instead of, the compliance infrastructure.

Lens: Compliance and values research
R3

A voice for those affected

Give affected parties influence proportionate to risk, and record its effect.

Lens: Stakeholder theory
R4

Incentives and speaking up

Align incentives with commitments and protect those who raise concerns.

Lens: Culture and incentives
R5

Non-negotiables

Combine risk-proportionate controls with explicit constraints that are not traded against benefits.

Lens: Risk- and rights-based governance
R6

Beyond the firm

Extend governance to suppliers and make decisions reviewable.

Lens: Algorithmic supply chains
The model

The Ethical Governance Integration Model

The EGIM is a five-dimension framework for embedding ethical reasoning in an AI management system. It complements standards and regulation rather than replacing them, and it is not itself a certifiable standard. Its distinguishing feature is that every dimension is paired with effectiveness evidence: observable signs that the mechanism is changing decisions.

Effectiveness evidence: does governance change decisions?Decisions delayed, conditioned or refused · designs changed by impact assessment · remedies granted · reviews triggered by events
D1
Ethical Policy Anchoring
  • Ethics Charter and non-negotiable uses
  • Incentives aligned with commitments
D2
Risk-Proportionate Controls
  • Use × population vulnerability tiers
  • Triggered reclassification
D3
Stakeholder Inclusion Mechanisms
  • Ethics Board with authority to halt
  • Participation with recorded response
D4
Transparent Accountability Structures
  • Impact assessment and named owners
  • Reviewable records and redress
D5
Continuous Ethical Review Cycles
  • Scheduled and event-triggered review
  • Protected concern-raising
ISO/IEC 42001 AI management system: Clauses 4–10 and Annex A controlsThe compliance infrastructure the EGIM builds on, supported by ISO/IEC 42005 (impact assessment) and ISO/IEC 42006 (certification)
Review findings feed back into management review and improvement.
D1

Ethical Policy Anchoring

Establish what the organisation will and will not do with AI, in terms that bind future decisions.

Core mechanisms

An Ethics Charter naming values and non-negotiable uses, developed with stakeholder input; sponsor incentives aligned with its commitments.

Evidence it is working

Use cases declined, narrowed or delayed because of the Charter.

D2

Risk-Proportionate Controls

Calibrate controls to the risk each system poses to the people it affects.

Core mechanisms

Tiers set by intended use and by the vulnerability of the affected population; justified fairness metrics; reclassification triggered by retraining, drift or supplier changes.

Evidence it is working

Reclassifications that followed triggers; group-level results that led to action.

D3

Stakeholder Inclusion Mechanisms

Give affected parties structured influence over systems that affect them.

Core mechanisms

An Ethics Board with defined authority to delay or halt deployments; proportionate external participation with recorded responses.

Evidence it is working

Deployments conditioned or stopped; stakeholder input traceable to changes.

D4

Transparent Accountability Structures

Make someone answerable for each system’s impacts, and give individuals recourse.

Core mechanisms

Impact assessment; named owners, including for supplier components; reviewable records; accessible redress.

Evidence it is working

Designs changed by assessments; remedies granted; decisions reconstructable on sampling.

D5

Continuous Ethical Review Cycles

Keep governance current as systems, contexts and expectations change.

Core mechanisms

Scheduled and event-triggered review; protected channels for raising concerns; findings fed to management review.

Evidence it is working

Reviews triggered by events, not only the audit calendar; concerns raised and resolved.

Standards and assurance

Working inside ISO/IEC 42001

The EGIM operates inside an ISO/IEC 42001 management system rather than alongside it. The full paper includes an indicative clause-by-clause mapping and relates the model to the companion standards ISO/IEC 42005 and 42006. Its practical consequence is a sharper test of effectiveness: sample decisions, not only documents. That test applies differently to each assurance role.

Internal audit

Has the most freedom. It can adopt EGIM criteria, triangulate documents against decision records and interviews, and report divergence between them as a finding.

Certification bodies

Audit against the standard’s requirements and cannot require mechanisms it does not. They can probe effectiveness more deeply within those requirements, supported by the competence requirements of ISO/IEC 42006.

Assurance beyond certification

Ethics-based audits and end-to-end algorithmic audits can adopt the EGIM’s criteria directly.

Illustrative application

What an EGIM assessment adds

The paper applies the model to Horizon Bank, a composite mid-sized retail bank preparing for ISO/IEC 42001 certification, whose documentation an internal review has judged sufficient. An EGIM assessment separates its gaps into two groups.

Would be nonconformities if detected

A high-risk credit model retrained twice without reassessment, despite an internal flag on residual fairness risk.

Satisfy the standard, change nothing

A governance committee that has never conditioned or halted a deployment. A policy that has never constrained a decision.

The second group is where the model adds most. The paper also sets out a sequenced response: close the high-stakes control gaps first, then give governance bodies real decision rights, and only then write the Ethics Charter. Mechanisms come first; the documents that codify them follow.

Implications

What it means for…

Organisational leaders

Treat ISO/IEC 42001 conformity as a foundation, not an end point.

Auditors

Sample decisions as well as documents.

Standard-setters

Articulate what evidence of effective AI governance looks like, especially for stakeholder engagement and impact assessment.

Regulators

Reward substantive governance, so that certification is treated as one indicator of good governance, not proof of it.

Scope and limitations

This is a conceptual paper. The EGIM has been designed and demonstrated, not yet empirically validated; the bank is a constructed composite, not a case study; and the model was developed from a practitioner perspective in AI governance and audit. The paper sets out an agenda for testing it, and also covers proportionality for smaller organisations, applicability across EU, UK and GCC contexts, and the pressures that generative and agentic AI place on system-by-system governance.

Request the full paper

The full paper, with its figures, tables and 54 references, is available free on request. Tell us where to send it.

How to cite

Miheisi, Y. (2026). Beyond compliance: Towards a values-driven framework for AI governance in high-stakes organisational environments [Working paper]. Enterprise Intelligence Center.

Picture of Dr. Yassin Miheisi

Dr. Yassin Miheisi

Founder of YM Consulting Group and the Enterprise Intelligence Institute. Advises enterprise organisations on the design and scaling of AI functions, with a focus on the operational discipline required to move AI from experimentation into durable, governed production. Author of the Enterprise Intelligence Series.

Picture of Dr. Yassin Miheisi

Dr. Yassin Miheisi

Founder of YM Consulting Group and the Enterprise Intelligence Institute. Advises enterprise organisations on the design and scaling of AI functions, with a focus on the operational discipline required to move AI from experimentation into durable, governed production. Author of the Enterprise Intelligence Series.

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