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06/06/2026 2:27 am
TL;DR: Dialpad CTO Brian Peterson argues that AI is becoming an operating mandate across engineering and customer operations, with teams required to use AI in code, performance reviews, and customer service workflows according to WorkOS. The governance question is no longer whether AI is useful, but which identity controls can survive non-deterministic, human-plus-machine execution at scale.
NHIMG editorial — based on content published by WorkOS: From Google Voice to AI-first communication: Dialpad's Brian Peterson on leading AI adoption
By the numbers:
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years.
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security.
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems.
Questions worth separating out
Q: How should organisations govern AI-assisted work in engineering and operations?
A: Treat AI-assisted work as an identity and accountability problem, not just a productivity upgrade.
Q: Why do AI-enabled workflows complicate least privilege?
A: Least privilege assumes the needed access can be scoped before execution begins.
Q: What breaks when humans verify AI output but do not own the workflow?
A: Responsibility becomes ambiguous.
Practitioner guidance
- Map AI-assisted workflows to entitlement boundaries Inventory where AI produces code, summaries, recommendations, or customer responses, then tie each workflow to the underlying repository, data, and action permissions that make it possible.
- Separate machine synthesis from human accountability Require clear ownership for every AI-assisted decision that affects customers, employees, or production systems, and document where the human is verifying versus independently deciding.
- Review privilege assumptions for non-deterministic workflows Reassess roles that now include AI drafting, classification, or orchestration so the permitted action set reflects runtime variation rather than the original job description.
What's in the full article
WorkOS's full article covers the operational detail this post intentionally leaves for the source:
- How Dialpad embedded AI across engineering, sales, customer service, and internal review processes
- What Brian Peterson said about leadership behaviour, team adoption, and cultural momentum
- The practical examples behind AI-assisted performance reviews and personal productivity workflows
- The HumanX 2026 interview context and Peterson's view of where customer service is headed
👉 Read WorkOS's interview with Dialpad CTO Brian Peterson on AI adoption →
AI adoption as a mandate: what does it mean for IAM teams?
Explore further
06/06/2026 4:07 am
AI mandate creates an identity governance problem before it creates a productivity gain. When engineering teams are required to use AI, the control question shifts from adoption to authority. The organisation is no longer managing isolated tool usage, but a new pattern where human identity, machine assistance, and workflow output are intertwined. That makes access review, approval, and accountability harder to map to a single actor. The practitioner takeaway is that AI policy is now an identity design problem, not just a training problem.
A few things that frame the scale:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, which shows the control model is already under pressure.
A question worth separating out:
Q: How do security teams assess AI adoption without creating compliance theatre?
A: Measure whether AI actually changes operating decisions, not just whether people say they use it. Look for evidence of traceable outputs, clear accountability, and bounded permissions. If adoption metrics rise but ownership, approval, and logging remain vague, the programme is creating activity without governance.
👉 Read our full editorial: AI adoption as mandate: what Dialpad’s model means for IAM
