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06/09/2026 5:18 pm
TL;DR: Digital.ai’s 18th State of Agile Report says AI use in development rose from 68% to 84% in under two years, and Arxan Technologies notes that only about half of those organisations report proper guardrails, showing that delivery speed is now outrunning governance and operating-model maturity. The real issue is not adoption, but whether AI-assisted workflows remain accountable, measurable, and bounded by trusted controls.
NHIMG editorial — based on content published by Arxan Technologies: The Fourth Wave is Already Here: What 18 Years of Agile Data Tells Us About What’s Next
By the numbers:
- AI tools in development processes increased from 68% to 84% in just under two years.
- 74% of organisations say measuring business outcomes remains challenging.
Questions worth separating out
Q: How should security teams govern agentic AI as it moves into production?
A: Security teams should govern agentic AI as a class of non-human identity, not as a generic application feature.
Q: Why do AI-driven delivery programmes need stronger auditability?
A: Because once AI can trigger or coordinate work, teams need to know not only what changed but why it changed and under whose authority.
Q: What breaks when delivery metrics focus only on speed?
A: Speed-only metrics hide whether AI-assisted work is actually improving business outcomes.
Practitioner guidance
- Define AI action boundaries Map every AI-assisted workflow to a specific set of permitted actions, escalation conditions, and approval requirements before expanding usage beyond simple assistance.
- Add auditability to delegated work Require logs that show which agent proposed or triggered each action, what data it used, and which human or policy approved the final outcome.
- Measure outcomes, not just throughput Tie delivery metrics to business-value indicators so AI-driven acceleration can be assessed for quality, not only speed or volume.
What's in the full article
Arxan Technologies' full article covers the operational detail this post intentionally leaves for the source:
- The full report’s survey breakdown shows how AI adoption varies across delivery functions and organisational maturity levels.
- The source article includes the phased model for moving from AI assistance to agentic orchestration inside Agile workflows.
- It also outlines the specific governance guardrails Digital.ai says it uses for traceable, contextual AI recommendations.
- Readers will find the article’s full discussion of how teams are redefining value measurement as delivery automation expands.
👉 Read Arxan Technologies' analysis of the fourth wave of software delivery and AI governance →
Agentic AI in agile delivery: what governance teams must catch up on?
Explore further
06/09/2026 5:19 pm
Agentic AI in delivery pipelines should be treated as a governance and identity problem, not just a productivity upgrade. Once an AI system can sequence tasks, call tools, or trigger workflows, it begins to behave like a delegated actor with runtime authority. That introduces the same questions identity teams already face with service accounts and workloads: what can it do, how is it authenticated, and where is the audit trail. Practitioners should assume the control model must expand with the autonomy model.
A question worth separating out:
Q: Should AI planning tools be treated like privileged non-human identities?
A: Yes, when they can initiate actions, call tools, or move work through a delivery workflow. In that case, they deserve the same scrutiny applied to other privileged non-human identities: scoped permissions, separation of duties, logging, and periodic review. That framing prevents governance gaps from hiding inside convenience features.
👉 Read our full editorial: AI is reshaping agile delivery faster than governance models
