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Technology executives are more confident than IT practitioners that AI agents will become central to business operations soon. A 2025 PagerDuty survey reported by CIO found a 24-percentage-point gap between the two groups. That divide reflects more than resistance: the people responsible for deploying and running agents see the testing, integration, security, and maintenance work that executive forecasts can overlook.
How wide is the gap between executives and IT practitioners?
In its February 4, 2025 report, CIO described findings from a PagerDuty survey about expectations for AI agents over the next two years. The results measure what respondents expect, not how many organizations already have fully autonomous agents in production.
| Respondent group | Expect agents to be core to business operations within two years | Expect agents to be peripheral within two years |
|---|---|---|
| Technology executives | 53% (PagerDuty survey, as reported by CIO, February 4, 2025) | Not stated for this group in the CIO report |
| IT practitioners | 29% (PagerDuty survey, as reported by CIO, February 4, 2025) | Not stated for this group in the CIO report |
| All respondents | 38% (PagerDuty survey, as reported by CIO, February 4, 2025) | 50% (PagerDuty survey, as reported by CIO, February 4, 2025) |
The 53% and 29% figures put technology executives 24 percentage points ahead of IT practitioners on the expectation that agents will be core. The all-respondent figures add context: more respondents expected agents to be peripheral than core within that period. Those are forecasts about the next two years, not evidence that the systems are already ready for every enterprise workflow.
Why are IT practitioners skeptical of AI agents?
Practitioners are close to the operational details: whether an agent gives a reliable answer, connects safely to existing systems, and remains manageable after launch. PagerDuty CTO Tim Armandpour described IT teams as “closer to the action and closer to the madness of what it takes to operate and manage things at scale.”
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That proximity can make skepticism a form of implementation knowledge. An executive may see a promising use case; the team responsible for it must account for the data, permissions, exceptions, and failure paths that determine whether it works in practice.
Many “agents” still rely on prescribed workflows
The label does not guarantee a system that independently chooses its own processes and tools. Savvi AI CEO Maya Mikhailov said many products marketed as agents use “highly deterministic and prescribed” workflows. In her description of a more mature agent, the system would choose processes and tools and learn from those decisions; current systems still need substantial human effort.
Mikhailov said: “Frontline AI practitioners have likely seen the amount of customization, quality assurance, and maintenance required to make a somewhat functional agentic workflow.” That work can include configuring a workflow, testing its outputs and edge cases, monitoring behavior, and maintaining it as connected systems or business needs change.
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Errors can become operational and compliance problems
Accuracy is not an abstract concern when an agent is connected to business data or processes. Pull Logic co-founder and CTO Rahul Chahar cited a test in which inconsistent manufacturer catalog descriptions led an agent to hallucinate product numbers and features. The example shows how messy source information can undermine an apparently straightforward workflow.
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Chahar said: “IT practitioners are cautious due to concerns around accuracy, transparency, security, and integration complexities.” He also described agentic systems as “often unpredictable, tough to troubleshoot, and challenging to mesh with older infrastructure, not to mention the compliance and security headaches they can create.” These risks make it important to decide in advance what an agent may access, what it may change, and when a person must review its work.
Are AI agents ready for enterprise deployment?
Readiness depends on the task and the controls around it; the survey figures do not establish that agents are universally ready or universally unready. The maturity concerns reported by CIO argue against treating the term “agent” as proof of autonomy or reliability. A bounded, testable workflow may be a reasonable pilot, while a workflow with consequential decisions, sensitive access, or hard-to-reverse actions calls for tighter human review and safeguards.
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Before putting an agent into a live process, teams should be able to answer practical questions:
- What specific problem is it meant to solve, and how will the team judge whether it works?
- What data, applications, and actions can it access, and are those permissions limited to what the task requires?
- How will people test accuracy, exceptions, and failure cases before launch?
- Who reviews consequential outputs, handles incidents, and owns ongoing maintenance?
- How will the team monitor performance and identify security, compliance, or integration problems?
If these questions have no clear answers, the organization has not yet established the operating conditions for a dependable deployment. A pilot should be small enough to contain risk and useful enough to produce evidence about whether the workflow merits expansion.
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Adoption is more credible when it begins with a real operational problem rather than a mandate to use AI. Chahar put the principle plainly: “CIOs and CTOs need to align agentic AI initiatives with their teams’ real challenges.” A pilot aimed at a specific bottleneck gives practitioners a concrete outcome to assess instead of asking them to endorse an abstract promise.
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Start small, make the work visible, and keep people accountable
Rob Kim, CTO at Presidio, recommends small, high-impact projects and regular opportunities for practitioners to tell leadership what is working and what is not. As Kim put it: “Create regular forums for practitioners to share insights and challenges with leadership.” Those forums are useful only if feedback can change the rollout: teams need clarity about who owns decisions, how problems are escalated, and what training people will receive.
Transparency matters throughout the pilot. Explain what the agent does, where its limits are, what information it can use, and when a human is expected to intervene. Give the people operating the workflow a way to report inaccurate outputs, security concerns, and maintenance burdens. That makes practitioner input part of deployment governance rather than an after-the-fact complaint.
Kim also offered a reason to keep learning rather than assume the first version is final: “This will be the worst version of agents and agentic AI that you will ever use — it only gets better.” Improvement is a possibility, not a substitute for validation: each new version still needs appropriate testing and review before its role expands.
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What does it take to manage AI agents in production?
Small experiments can become a control problem as the number of agents grows. In a March 4, 2025 companion report, CIO described the management needs organizations face as agents proliferate: a unified view of agents, alerts for performance or security concerns, asset and version management, cost tracking, authentication controls, and governance. The report named AgentOps and OneReach.ai among vendors entering this emerging market; naming them does not establish that any one product is suitable for a particular organization.
Production operations should make it possible to answer, at minimum, which agents exist, who owns them, what versions are running, what they can access, and whether they are behaving as intended. Cost visibility helps teams understand the resources an agent consumes; authentication and governance controls help constrain access and clarify responsibility. Without that inventory and oversight, it becomes harder to investigate a failure or ensure that an agent remains within its approved role.
The gap in confidence is therefore a useful signal for leadership: strategic intent alone does not establish operational readiness. The people closest to deployment need a meaningful role in defining the problem, setting controls, testing the system, and deciding whether results justify scaling.
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