Topic starter
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.
Editorial analysis by NHI Mgmt Group, based on content published by WorkOS: “From Google voice to AI-first communication: Dialpad's Brian Peterson on leading AI adoption”.
Key questions
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 agents make non-human identity governance harder?
A: AI agents make governance harder because they can request tools, act autonomously, and change behaviour across sessions while still relying on machine credentials.
Q: What breaks when AI-generated content is used in performance reviews or customer workflows?
A: The review process can fail if humans treat AI output as authoritative without enough scrutiny.
Practitioner guidance
- Define AI-assisted workflow boundaries Document which business processes may use AI, where human approval remains mandatory, and which downstream systems can never accept model-generated output without review.
- Separate tool access from execution rights Grant users permission to use AI tools without automatically granting authority to commit the resulting output into systems of record, customer workflows, or employment decisions.
- Create reviewer accountability rules Assign named human owners for AI-assisted outputs in performance management, customer service, and engineering so verification is a controlled step rather than an informal habit.
Bottom line: AI adoption becomes an identity governance issue once organisations require it as part of normal work rather than treating it as an optional assistant.
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06/06/2026 4:07 am
AI mandate turns governance from adoption management into execution governance: Once an organisation requires AI use across functions, the identity problem is no longer optional adoption. The programme has to govern how people, systems, and AI outputs combine inside routine work. That is a different control challenge from granting software access, because the action path itself becomes variable. Practitioners should treat mandatory AI use as a workflow governance issue that sits alongside IAM, not beneath it.
A few things that frame the scale:
- Organisations that describe themselves as confident in their AI deployment actually experience a 72% security incident rate, compared to 33% for those who remain cautious, according to the 2026 Infrastructure Identity Survey.
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What is the difference between user access and approval authority in AI-assisted processes?
A: User access lets someone operate the tool, while approval authority lets them accept, publish, or act on the AI output. Those are not the same control. In mature governance, a user may be allowed to draft with AI but still need separate permission and oversight to commit the result into a business process.
👉 Read our full editorial: AI adoption as mandate: what Dialpad’s model means for IAM