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24/06/2026 8:55 pm
TL;DR: AI literacy is moving from a training preference to a governance requirement as organisations embed AI into workflows and customer journeys, with the EU AI Act making staff knowledge and training a formal obligation, according to Collibra. The real issue is not awareness alone but whether decision-makers can govern AI safely, consistently, and at scale.
NHIMG editorial — based on content published by Collibra: Why AI Literacy isn’t optional anymore and what to do about it
By the numbers:
- Gartner predicts that by 2027, organizations that prioritize AI literacy at the executive level will outperform their peers financially by 20%.
Questions worth separating out
Q: How should organisations operationalise AI literacy for governance teams?
A: Start by defining which roles must understand AI risk deeply enough to approve, monitor, or audit use cases.
Q: Why does AI literacy matter for identity governance programmes?
A: Because identity governance depends on people correctly understanding who or what is acting, what access it has, and who is accountable.
Q: What do organisations get wrong when they treat AI literacy as training only?
A: They assume completed courses equal operational readiness.
Practitioner guidance
- Define role-based AI literacy thresholds Set different minimum expectations for executives, approvers, risk owners, and operators.
- Map AI use cases to identity subjects Document whether a workflow is driven by a human, a service account, or an AI-enabled decision process.
- Build literacy evidence into governance records Record training completion only as a starting point.
What's in the full article
Collibra's full blog post covers the operational detail this post intentionally leaves for the source:
- Role-specific training examples for business, data, and governance teams
- Practical guidance on embedding AI literacy into onboarding and internal knowledge hubs
- Examples of how AI literacy links to policy review, leadership development, and applied learning
- Details of Collibra's AI Governance training course series and partner access terms
👉 Read Collibra’s analysis of why AI literacy now matters for governance →
AI literacy and governance: what should practitioners do now?
Explore further
25/06/2026 5:49 am
AI literacy is becoming a control-plane issue, not a learning-and-development issue. When teams do not understand how AI behaves or fails, they cannot govern approvals, exceptions, or escalation paths with confidence. That creates a programme-level weakness because the control owner and the control subject no longer share the same operating model. Practitioners should treat literacy as part of governance design, not as a post-deployment fix.
A few things that frame the scale:
- The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities, according to The State of Secrets in AppSec.
- 43% of security professionals are concerned about AI systems learning and reproducing sensitive information patterns from codebases.
A question worth separating out:
Q: How can security teams tell whether AI literacy is actually working?
A: Look for fewer conflicting interpretations of AI risk, clearer approval ownership, and better-quality exception decisions. If risk, legal, data, and security teams use different definitions, literacy has not translated into control. Good programmes produce consistent policy application and evidence that decision-makers understand the systems they govern.
👉 Read our full editorial: AI literacy is becoming a governance requirement for enterprise AI
