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12/06/2026 12:02 am
TL;DR: AI adoption reached 99.6% among surveyed companies, up from 86.8%, while 68% report using AI for helpdesk and chatbots and 67% for security threat detection, according to JumpCloud’s Q3 IT Trends 2025 report. The governance problem is no longer adoption itself, but how AI is embedded into identity, access, and operational controls without expanding risk.
NHIMG editorial — based on content published by JumpCloud: Q3 IT Trends 2025 report on AI adoption
By the numbers:
- AI adoption has surged to 99.6% among the companies surveyed, up from 86.8% in the previous findings.
- Helpdesk and chatbots account for 68% of AI use cases in the report.
- Security threat detection accounts for 67% of AI use cases in the report.
Questions worth separating out
Q: How should security teams govern AI workflows that depend on service accounts and API keys?
A: Security teams should treat each AI workflow as a governed access path, not just an application feature.
Q: Why do AI deployments increase NHI governance pressure?
A: AI deployments increase NHI governance pressure because every integration typically introduces credentials, delegated access, and trust relationships that outlive the initial use case.
Q: How can organisations tell whether AI is creating access risk?
A: Organisations should look for AI systems that can reach production data, security tools, or administrative workflows through persistent credentials.
Practitioner guidance
- Map AI workflows to identity dependencies Inventory every AI-enabled workflow, then identify the human users, service accounts, API keys, and tokens that make it function.
- Require lifecycle ownership for AI-connected credentials Tie every AI integration to an explicit joiner, mover, leaver process for its associated credentials.
- Separate AI assistance from automatic enforcement Allow AI to assist with prioritisation or correlation before it can drive enforcement actions.
What's in the full report
JumpCloud's full report covers the operational detail this post intentionally leaves for the source:
- Survey segmentation that shows how AI use varies by company size and operating model.
- The full breakdown of AI use cases across support, security, maintenance, and analytics workflows.
- Additional findings on how respondents are prioritizing AI for efficiency and competitiveness.
- The report's broader IT trends context, which helps teams compare AI adoption against other infrastructure priorities.
👉 Read JumpCloud's Q3 IT Trends 2025 report on AI adoption and use cases →
AI adoption at 99.6%: what identity teams need to catch up on?
Explore further
12/06/2026 9:01 am
AI adoption at 99.6% means identity governance is now a baseline control issue. When AI is present in almost every surveyed organisation, the relevant question shifts from experimentation to control coverage. IAM and NHI programmes have to assume AI is already inside operational workflows, which makes entitlement review, secret handling, and approval boundaries part of standard governance. The practitioner conclusion is simple: AI can no longer be treated as an adjacent innovation track.
A few things that frame the scale:
- 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.
- 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.
A question worth separating out:
Q: What should IAM teams do when AI moves into operational decision-making?
A: IAM teams should classify AI-enabled decision paths as privileged workflows and require auditability, ownership, and review triggers before production use. If AI recommendations can affect access, containment, or service operations, the identity programme must govern the permissions behind those actions, not just the interface.
👉 Read our full editorial: AI adoption hits 99.6%, but identity governance is lagging
