Govern AI across the whole organisation.
AI governance is not just a policy, risk register or compliance document.
It is an operating system for deciding how AI is selected, designed, procured, deployed, monitored and improved — with accountability, human oversight and service outcomes built in.
Discuss AI governance →Responsible AI needs to work in the real operating environment, not only in governance documentation.
AI creates opportunities. It also changes accountability.
Organisations increasingly use AI inside products, services, workflows and decision-making.
Governance needs to keep pace with how those systems are selected, developed, purchased, deployed and monitored.
DigiFixIT connects governance to the service itself — bringing together people, process, technology, risk, customer impact and organisational accountability.
Organisations do not always know where AI is used.
Teams may adopt embedded AI, generative tools or automated decision support without a shared organisational inventory.
Ownership can become fragmented.
Product, technology, data, compliance and operational teams may each own part of an AI system without clear end-to-end accountability.
Human review is often poorly defined.
Organisations need to determine when humans review, override, investigate or escalate AI-supported activity.
Governance cannot stop at deployment.
Performance, incidents, complaints, changes and emerging risks need to feed back into ongoing governance.
Governance across the AI lifecycle.
We help organisations translate governance principles into practical operating models, controls, responsibilities and service processes.
AI governance maturity
Understand current AI use, governance maturity, existing controls, organisational gaps and priority areas.
AI use-case visibility
Establish visibility of AI systems, embedded capabilities, suppliers, owners, purposes and affected services.
AI risk & impact assessment
Design proportionate assessment processes that consider intended use, affected people, operational risk and governance requirements.
Governance roles & decisions
Define decision rights, accountability, approval routes, escalation paths and cross-functional governance forums.
Human control & recourse
Design human review, override, escalation and recourse into AI-enabled services and operational workflows.
Monitoring & improvement
Connect AI performance, incidents, complaints, model changes and service evidence into continual governance review.
Move from AI principles to managed governance.
We can help organisations structure AI governance around an AI management-system approach and prepare the operating environment needed for ISO/IEC 42001-aligned governance.
Context · objectives · risk · responsibilities
Processes · controls · documentation · delivery
Measures · review · audit · evidence
Correct · learn · update · improve
ISO/IEC 42001 readiness
Review the current governance environment, identify gaps and establish a prioritised implementation roadmap.
AI management-system design
Define governance processes, responsibilities, decision routes and organisational controls.
Governance workflow design
Map the lifecycle from AI idea and risk assessment through approval, deployment, monitoring, change and retirement.
Governance review loops
Establish monitoring, review and improvement processes so governance evolves with the service and AI system.
Govern AI from idea to retirement.
AI governance is most effective when controls are embedded into normal delivery and operational processes rather than added after implementation.
Decision rights
Who can approve, reject, escalate or require additional assurance.
Assessment
How AI-related risk and impact are assessed proportionately to the use case.
Oversight
Where humans supervise, investigate, intervene or provide recourse.
Change control
How material changes to models, suppliers, data or use cases return through governance.
Governance integrated into service design.
AI governance strengthens the six existing DigiFixIT services rather than sitting beside them as a disconnected compliance activity.
AI Governance Discovery
AI inventory, maturity assessment, governance gaps, risk landscape and stakeholder mapping.
02 SERVICE BLUEPRINTINGAI Governance Blueprinting
Connect AI, users, operations, controls, ownership, data and human oversight.
03 JOURNEY MAPPINGResponsible AI Journeys
Identify AI-assisted interactions, transparency, human escalation and customer recourse.
04 PROCESS MAPPINGGovernance Operating Models
Design intake, assessment, approval, monitoring, change and incident-management processes.
05 TOUCHPOINT MAPPINGAI Touchpoint Mapping
Identify direct and indirect AI interactions, disclosures and human hand-off points.
06 FEEDBACK INTEGRATIONAI Monitoring & Improvement
Connect incidents, complaints, performance, model changes and governance review.
Practical governance teams can operate.
The focus is on usable governance artefacts, workflows and responsibilities that support everyday organisational decision-making.
AI system register
Structured visibility of systems, use cases, owners, suppliers, purposes and affected services.
Governance gap assessment
Evidence of current governance maturity, weaknesses and priority areas for improvement.
Governance operating model
Roles, decision rights, forums, ownership, escalation and accountability.
AI governance processes
Intake, assessment, approval, procurement, deployment, monitoring, change and retirement.
Human oversight model
Clear intervention, escalation, review, override and customer recourse points.
Governance implementation plan
Prioritised actions for moving from the current state towards a managed AI governance model.
Governance connected to the actual service.
Responsible AI requires more than technology controls.
It requires an understanding of the people affected, the service being delivered, the operational process, the decisions being made and the organisation accountable for the outcome.
Using AI but unsure how to govern it across the organisation?
We can help you understand the current landscape, establish practical governance and connect AI risk, accountability and oversight to real service delivery.
Discuss AI governance ↗