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20/08/2026 5:41 pm
TL;DR: Explainable AI depends on human oversight, business problem framing, organised data, and transparent workflows so organisations can trust automated decisions while meeting regulatory scrutiny, according to Fiddler. The governance challenge is not model performance alone, but whether decision logic, access, and accountability remain understandable enough to review, correct, and defend.
NHIMG editorial — based on content published by Fiddler: CIO Outlook 2020: Building an Explainable AI Strategy for Your Company
Questions worth separating out
Q: How should organisations govern explainable AI systems in regulated workflows?
A: Start by assigning accountable owners, defining review points, and documenting how decisions can be challenged or corrected.
Q: How do AI explainability and identity governance fit together?
A: Explainability tells you how a model reached an output, while identity governance tells you whether it should have been allowed to act at all.
Q: What do security teams get wrong about AI visibility?
A: They often assume licence data or static configuration data is enough to understand AI risk.
Practitioner guidance
- Define decision ownership for every AI use case Assign a named business owner, technical owner, and review authority for each model before it moves into production.
- Inventory data access paths into AI systems Map which datasets, service accounts, APIs, and pipelines feed each model, then restrict access to the minimum set required for training and inference.
- Build a human challenge and correction process Document how disputed or incorrect AI decisions are escalated, reviewed, and corrected, including what evidence operators need to see before overriding the model.
What's in the full article
Fiddler's full blog covers the operational detail this post intentionally leaves for the source:
- Concrete examples of explainability checks for business decision workflows, including who reviews and overrides AI outcomes
- Practical guidance on organising data for AI systems so that access and responsibility can be audited
- Questions teams can use to test whether an AI workflow is transparent enough for regulated use
- The article's full framing of how explainable AI supports trust, risk reduction, and policy review
👉 Read Fiddler's blog on building an explainable AI strategy →
Explainable AI strategy: what matters for governance teams now?
Explore further
20/08/2026 6:25 pm
Explainability is a governance control, not a communications layer. Organisations often treat explainability as a way to make AI easier to sell to users, but the real value is evidencing how decisions were made and whether they can be challenged. That shifts the control objective from persuasion to accountability, which is where AI governance meets identity governance. Practitioners should view explainability as part of the control plane for high-impact decisions.
A question worth separating out:
Q: How do you know if explainable AI is actually working?
A: It is working when analysts can resolve cases faster, false declines drop, customer complaints decrease and reviewers make more consistent decisions from the same evidence. If explanations are verbose but do not change thresholds, triage quality or audit outcomes, then the system is informational, not operational.
👉 Read our full editorial: Explainable AI strategy needs human oversight, data access and trust
