Reported IT savings from AI agents are concentrated in tier 1 service-desk work, software-development workflows, and cloud or infrastructure operations. The strongest examples are tied to specific companies or modeled studies—not independently audited proof of what a typical organization will save. The practical test is whether an agent resolves work safely and reduces net cost after review, rework, and implementation expenses.
Where organizations are applying AI agents
Agents are being used where work is repeatable, documented, and connected to systems they can query or operate. Three areas account for the clearest reported IT savings, although the evidence behind each is different.
Tier 1 service desk and IT support
Password resets, access questions, routine troubleshooting, and other high-volume requests are plausible starting points. McKinsey describes one multinational enterprise handling approximately 450,000 tickets a year, automating up to 80% of requests, redeploying 50% of service-agent capacity, and reporting customer satisfaction of 4.8 out of 5. These are results for that enterprise example, not a general service-desk benchmark. McKinsey’s 2026 analysis also estimates 5–15% savings from continuous agentic cost optimization; that is consultancy analysis, not a universal realized result.
In a separate example reported by CIO in October 2026, LaunchDarkly CIO Rhonda Baldwin cited about $50,000 in annualized tier 1 support savings. That company-reported figure should not be treated as a forecast for another organization. Ticket closure alone is a weak success measure: reopened requests, human checks, and unresolved underlying problems can reduce the apparent benefit.
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Software development workflows
Reported agent use spans coding, developer onboarding, migration, planning, code review, testing, and security remediation. A Forrester Consulting study commissioned by GitLab in 2026 modeled a composite organization using interviews with four GitLab customers. GitLab’s announcement reports 400% ROI, $7.5 million net present value over three years, and payback in under six months for that model. These are modeled findings, not a typical industry result or an independently measured outcome across all GitLab customers. GitLab’s announcement describes the study and its scope.
The same modeled organization attributed its results to a range of workflow improvements, including:
- Developer onboarding: 80% faster, with $582,000 in three-year savings reported by GitLab for the model.
- Code migration: 75% faster; the modeled migration fell from eight months to two, with $157,000 in reported savings.
- Quality assurance and security remediation: 40% time savings for engineers in those roles.
- Individual developer productivity: 20% gain, which GitLab attributed $7.4 million in three-year gains to in the modeled organization.
These figures are tied to the commissioned model and should be evaluated against a team’s own throughput, quality, and review burden. Faster code production does not automatically mean lower costs if it increases defects or the work required to validate changes.
Cloud and infrastructure operations
Reported uses include monitoring cloud deployments, routing budget approvals, identifying or shutting down unauthorized spend, rightsizing resources, reclaiming licenses, and handling repetitive capacity or hosting work. In the CIO report, LaunchDarkly’s Baldwin attributed $1 million in avoided spending across two optimization projects and $120,000 saved by building an internal asset-management solution. West Monroe CIO Kevin Rooney cited a 40% reduction in yearly managed-service-provider costs and an estimated 2,700 operational hours saved annually. KamiwazaAI reported a 70% immediate cloud-spend reduction in the first round of optimization agents deployed in its own environment. These are individually attributed company or executive reports, not directly comparable trials.
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Which savings claims are comparable—and which are not?
Reported figures use different methods and measure different things. A modeled return, estimated capacity released, avoided spend, and a company’s own deployment result are not interchangeable. Keep attribution attached to each number when using these examples to set expectations.
| Example | Reported figure | Evidence type and scope |
|---|---|---|
| GitLab Duo Agent Platform | 400% ROI; $7.5 million NPV over three years; payback under six months | Forrester Consulting study commissioned by GitLab, 2026; modeled composite based on interviews with four customers. |
| Multinational enterprise service desk | Approximately 450,000 annual tickets; up to 80% of requests automated; 50% of agent capacity redeployed; customer satisfaction 4.8/5 | One anonymized enterprise example in McKinsey’s 2026 analysis. |
| Continuous agentic cost optimization | 5–15% savings | McKinsey consultancy analysis, 2026; not a universal realized result. |
| LaunchDarkly and other CIO-reported examples | $1 million avoided across two optimization projects; $120,000 saved on an internal asset-management solution; about $50,000 annualized tier 1 support savings | Figures attributed to LaunchDarkly CIO Rhonda Baldwin by CIO in October 2026. |
| West Monroe | 40% reduction in yearly managed service provider costs; estimated 2,700 operational hours saved annually | Figures attributed to CIO Kevin Rooney by CIO in October 2026. |
| KamiwazaAI | 70% immediate cloud-spend reduction in its first round of cloud optimization agents | Company-reported result from KamiwazaAI’s own deployment, as reported by CIO in October 2026. |
| West Monroe support engagement | 14% lower support ticket resolution time; 45% faster documentation; more than $26 million in annualized cost savings | West Monroe case page, accessed October 3, 2026; unnamed infrastructure software company, more than 10,000 tickets analyzed, generative AI and retrieval augmented generation. The case does not establish that the work used agents. |
For that last case, see West Monroe’s case page. It is relevant to AI-assisted support economics, but it should not be represented as an agent deployment without evidence from the client source.
ServiceNow’s March 2025 infographic reports internal results including 76% of IT support requests self-served, 20% developer productivity, and 53% productivity with its server patch management process. Its headline value conflicts: the page title says $355 million or more while the infographic text labels the total $325 million or more. Because those figures disagree, the total is not a reliable comparison point. The infographic and its accompanying page are company-reported internal evidence.
How to tell whether the savings are real for your IT team
Start with a baseline and track the full cost of getting work done—not just how many tasks an agent touches. A useful evaluation separates cash savings from capacity that has been freed for other work.
- Choose a bounded workflow. Record current task volume, cycle time, operating expense, staffing effort, service levels, and exception rates. Prefer work that is high-volume, well-documented, and inexpensive to reverse if an agent makes a mistake.
- Define successful resolution. For support, count a request as resolved only if it stays resolved; track reopens, escalations, response time, SLA adherence, errors, and user satisfaction alongside closure rates.
- Include all agent costs. Account for software and implementation, licenses, inference, integration, monitoring, human review, and rework. Measure these over the same period as the claimed savings.
- Separate avoided spend from released capacity. An hour returned to an engineer may enable higher-value work without reducing payroll or vendor expense. Report that as capacity or productivity unless it actually avoids spending.
- Compare quality as well as speed. Check whether faster delivery changes defect rates, security findings, customer outcomes, or the amount of review needed.
- Scale only after repeatable results. Compare the pilot with its baseline and check that benefits persist across normal demand and edge cases before broadening access or autonomy.
A CIO article’s illustrative service-desk calculation shows why this accounting matters: an apparent €60,000 monthly saving fell to roughly €36,000 after reopened work and human checking were included. It is an expert’s worked example, not a measured result that applies to all service desks. The article also raises a workforce consideration: automating all tier 1 work may remove some of the tasks through which junior staff build experience.
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Where to draw the line on autonomy
Begin with documented, reversible tasks and make escalation part of the workflow. Actions affecting permissions, security, production systems, or infrastructure can have disproportionate consequences if an agent misunderstands context or acts on stale information.
- Give agents only the data and system permissions needed for the assigned task.
- Require human approval for high-impact or hard-to-reverse changes.
- Keep an audit trail of inputs, recommendations, approvals, and actions.
- Set explicit boundaries for exceptions and route uncertain cases to an accountable human owner.
- Maintain reliable knowledge, telemetry, APIs, and runbooks; poor source data makes confident automation unsafe.
As Jeet Pattanaik, founder and CTO of Glokal AI, put it in CIO’s October 2026 report: “The sweet spot is work that’s high volume, well documented, and cheap to undo if the agent gets it wrong.” That is a useful starting filter, not a substitute for controls.
Adoption is not proof of savings
PwC’s May 2025 AI Agent Survey found that 53% of U.S. businesses deploying AI agents reported use in IT and cybersecurity. The survey included 290 respondents currently using or planning agent use. It indicates where organizations are applying agents; it does not establish that those deployments reduced costs or improved outcomes.
The evidence for savings remains a mix of commissioned modeling, company-reported deployments, consultancy analysis, and named executives’ accounts. Treat those examples as useful hypotheses for a local pilot, then judge the case on measured net cost, service quality, risk, and whether released capacity produces a benefit the organization values.
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