An AI feature passes evaluation, reaches production, and receives a model update a month later.
At that point, several questions become important: Who approved the change? Which evaluation results supported it? Did the provider’s data-handling terms change? Who can disable the feature if its behavior deteriorates?
These questions connect AI governance directly to engineering work.
ISO/IEC 42001 provides a framework for establishing, maintaining, and improving an Artificial Intelligence Management System, or AIMS. It applies to organizations developing, providing, or using AI. Independent certification bodies assess certification; purchasing a governance platform or hiring an implementation partner does not itself confer certification.
For teams comparing external support, the useful distinction is between governance advice, technical implementation, and the tools used to maintain evidence.
What does ISO 42001 preparation involve?
The GeekyAnts ISO 42001 implementation guide outlines a sequence covering executive ownership, scope and AI inventory, gap and impact assessments, implemented controls, operating evidence, internal audit, management review, and independent certification assessment.
Two points are especially relevant to developers:
- Governance evidence should emerge from normal approval, evaluation, deployment, monitoring, and change workflows.
- Certification concerns a defined management-system scope, rather than blanket approval of every model or product.
The guide also treats supplier reviews, incident handling, and ongoing improvement as continuing responsibilities.
The engineering implication is practical: governance requirements need identifiable implementation tasks and maintainable records.
Five companies to evaluate for AI governance support
The following shortlist compares published capabilities. It is not a ranking based on independently measured project outcomes, and the companies’ offerings are not interchangeable.
1. GeekyAnts: Engineering and operational implementation
GeekyAnts describes support for translating readiness gaps into changes across AI inventories, lifecycle workflows, monitoring, supplier processes, and evidence-producing systems.
Its published scope includes remediation planning and implementation across engineering functions. It distinguishes that work from the independent certification decision.
Evaluation focus: Whether the proposed engagement identifies concrete system changes, acceptance criteria, responsible owners, and evidence handover.
A useful question is whether a requirement such as “maintain model-change evidence” results in an implemented workflow that the internal team can continue operating.
2. IBM: Governance tooling and lifecycle records
IBM’s watsonx.governance offering combines capabilities for AI governance across the lifecycle. Its documentation describes AI Factsheets, evaluation and monitoring capabilities through Watson OpenScale, and model risk governance features associated with OpenPages.
These capabilities make IBM relevant to a tooling comparison, particularly where teams need to organize governance information across multiple AI assets.
Evaluation focus: Integration with existing model infrastructure, the evaluations supported for the intended use case, and the ability to export evidence.
A platform can help maintain records, but teams still need to define who reviews those records and what action follows a failed evaluation.
3. Deloitte: Governance structure and readiness assessment
Deloitte’s ISO 42001 guidance emphasizes building on existing risk, security, privacy, and audit capabilities. It also addresses cross-functional ownership and evidence of operational effectiveness, including monitoring logs, data audit trails, and launch approvals.
This suggests relevance where fragmented responsibilities are a substantial obstacle to implementation.
Evaluation focus: How assessment findings become owned remediation tasks, and how advisory work connects to technical delivery.
A gap assessment becomes actionable when each finding has an accountable owner, a completion condition, and an agreed method of verification.
4. Accenture: Responsible AI governance programs
Accenture publishes responsible AI consulting services focused on establishing governance frameworks and implementing responsible AI practices. Its accompanying material discusses moving organizational principles into operational approaches.
This suggests relevance for organizations coordinating responsible AI work across multiple teams or business functions.
Evaluation focus: The exact boundary between governance design, technical implementation, and ongoing operation.
Teams should request explicit ISO 42001 deliverables where certification readiness is the objective. A general responsible AI engagement should not be assumed to include them.
5. Tata Consultancy Services: Responsible AI lifecycle services
TCS describes responsible AI services across the adoption lifecycle. Its published framework discusses security, accountability, fairness, transparency, and identity protection as guiding principles.
This makes TCS relevant to a comparison of lifecycle implementation approaches.
Evaluation focus: How those principles translate into tests and controls for the specific application.
For example, a customer-support assistant and an automated financial recommendation system require different evaluation criteria. Buyers should examine the proposed methods rather than rely on the presence of a framework alone.
What evidence could an engineering team maintain?
The following is an illustrative implementation checklist, not a prescribed ISO template or a complete certification checklist.
| Engineering event | Example evidence | Practical purpose |
|---|---|---|
| New AI feature proposed | Intended use, system owner, dependency record | Establish what is being introduced |
| Model or prompt changed | Version reference, evaluation report, approval | Trace the basis for a release |
| Retrieval source updated | Dataset version, access review, quality checks | Investigate changes in output behavior |
| Production threshold breached | Alert, investigation record, response decision | Demonstrate how issues are handled |
| AI supplier changed | Supplier review and affected-system assessment | Revisit dependency assumptions |
| Feature retired | Access removal and retention decisions | Close out operational responsibilities |
These records can live in existing repositories, issue trackers, registries, and monitoring systems. Their usefulness depends on traceability: a reviewer should be able to connect a release to its evaluated configuration and approval.
Sensitive prompts, customer data, and credentials should not be copied indiscriminately into evidence stores. Evidence design also needs appropriate access and retention controls.
How should teams compare proposals?
A practical comparison should examine three areas.
Delivery boundaries: Does the engagement cover assessment, implementation, tooling, or a defined combination?
Evidence quality: Can the supplier demonstrate how a control produces records during normal operation?
Maintainability: Can the internal team update the process when models, dependencies, personnel, or product requirements change?
One useful evaluation exercise is to ask each provider to walk through the same hypothetical model update—from proposed change to evaluation, approval, deployment, monitoring, and recovery.
That exercise makes differences in ownership and implementation detail easier to identify than a generic capabilities presentation.
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