Here’s a paradox for the AI era: organizations are obsessed with the promise of AI as the key to unlocking productivity and enterprise transformation, and IT teams are all-in on the advantages AI and automation offer — yet those same organizations are the ones holding that transformation back.  

While the majority of IT workers advocate for AI adoption, operational, cultural and budgetary barriers stand in the way of enterprises implementing AI at scale. The result: most companies today have yet to operationalize breakthrough AI and automation use cases that deliver true business value. 

That’s not to say; most companies aren’t using AI — but mainly for completing lower-level tasks and boosting individual productivity. While this is an important step, enterprises will need to think bigger and longer-term to see significant ROI and digital transformation from their AI investments. 

Automation and outputs vs. agentic AI autonomy  

IT teams are ahead of the curve when it comes to generative AI adoption. 84% of IT professionals use generative AI tools at work, according to a 2025 Ivanti research report. That same research shows that IT professionals are also overwhelmingly positive toward AI and automation:  

  • 83% expect AI to boost productivity in the next year. 
  • 70% say it will make their work more satisfying.

Yet, for all that progress, companies aren’t tapping into the deeper potential of AI-driven technology. Many teams have mastered task-level automation (ticket routing, password resets, log summarization), but few have embraced agentic AI, which goes beyond execution to autonomous reasoning and optimization.  

That gap between automation and transformation mirrors a larger problem occurring across industries. While most organizations are already using AI in some capacity, only a few have seen measurable business impact from their AI tools. McKinsey has referred to this situation as “the gen AI paradox.”  

This disconnect between AI applications and ROI is further validated by an MIT report published in August 2025, which finds that a staggering 95% of organizations found zero return on generative AI despite $30-40 billion worth of investment in it. 

The reason for the problem is straightforward: generative AI creates. It produces content, automates tasks, and accelerates workflows. However, it doesn’t learn, reason, or adapt on its own. The next phase of enterprise AI will be driven by systems that can interpret, predict and act dynamically: what’s now being defined as agentic AI. 

Ivanti’s research further underscores the fact that most companies today have yet to integrate more complex AI use cases into their IT workflows.   

While 67% of organizations automate ticket routing, fewer than one-third apply AI to root-cause analysis or other predictive use cases. This signals that most teams are still prioritizing standard automation for tasks rather than enabling systems to think and act autonomously via agentic AI. 

Standard automation and generative AI tools are frequently employed by IT teams to improve individual productivity and handle low-level, repetitive tasks at greater speeds than human intervention alone. The ability to boost efficiency with less time and resources is a critical advantage of AI — particularly for strained IT teams who are always being asked to accomplish more with less. But efficiency gains are only one piece of the puzzle. 

Ivanti data also reveals a deeper organizational issue: nearly half of IT teams say their organizations lack resilience — the ability to adapt quickly to change, recover from disruption and maintain business continuity without excessive manual intervention. 

True enterprise agility demands autonomous and adaptive AI solutions that can anticipate problems, reason through potential solutions and continuously learn how to deliver the most optimal outcomes. 

This is where many leading organizations are turning to agentic AI. Autonomous AI agents offer teams the opportunity to reshape traditional IT operations from reactive automation to proactive, goal-driven collaboration. Realizing that transforming ITOps with AI means moving from isolated automation wins to system-wide intelligence. 

High-impact agentic AI use cases in ITSM and ITOps

Agentic AI is already moving the needle by reducing downtime, cutting costs and improving organizational agility.  

Five high-impact use cases for agentic AI in IT follow: 

1. Autonomous incident remediation 

Autonomous remediation is where AI begins to move from support to strategy. In traditional IT operations, identifying and fixing an issue can take hours of human triage and escalation.  

With agentic AI, those same incidents can be detected, diagnosed and resolved in real time — often before users notice any disruption.  

Agentic AI doesn’t not only detect anomalies within that IT space and diagnose the root cause — it intelligently devises solutions and then executes fixes without human intervention. Moreover, machine learning enables the AI to learn from previous incidents and use this experience to continually refine and improve response efforts going forward. 

2. Proactive problem prevention

Proactive problem prevention happens when AI can anticipate rather than just react to potential tech problems. Instead of solving a known issue, agentic AI continuously monitors data patterns and detects early warning signals in the form of small deviations that might evolve into service disruptions or security issues.  

In other words, it moves ITOps toward proactive prevention, monitoring leading indicators and addressing issues before they become significant. 

3. End-to-end lifecycle management

Autonomous AI agents will provide a more comprehensive and effective approach to asset lifecycle management. 

This lifecycle view of automation extends beyond individual tickets or incidents to touch every stage of IT operations.  

From onboarding new devices to patching and decommissioning outdated infrastructure, agentic AI ensures systems remain secure, compliant and cost-effective.  

It not only fixes issues but also detects, diagnoses, and optimizes IT systems from provisioning to retirement. It acts as a continuous improvement engine, learning from patterns across the IT ecosystem to proactively optimize resources, streamline updates and reduce the long-term burden on IT staff. 

4. Dynamic change and release management 

Dynamic change and release management is where agentic AI truly shows its orchestration capabilities. In most enterprises, change management remains a high-friction process, requiring coordination across multiple teams, tools and environments.  

Agentic automation transforms this by allowing AI agents to collaborate on complex workflows including cybersecurity incident response and software deployment, working together with minimal human oversight to resolve incidents, provision resources and ensure compliance.  

These agents act as intelligent coordinators: synchronizing updates across systems, validating configurations and rolling back changes automatically when anomalies occur. The result is faster, safer and more predictable change cycles that free IT teams to focus on innovation rather than resource-heavy firefighting. 

5. Autonomous resource and capacity management

Resource and capacity management are some of the most critical — and often overlooked — dimensions of IT performance. Using AI, companies can anticipate future resource needs by analyzing historical usage trends, workload fluctuations, and demand surges. Agentic systems can automatically allocate compute power, storage and bandwidth before bottlenecks appear, maintaining optimal performance without constant human oversight.  

Over time, these self-adjusting systems learn from operational data to continuously fine-tune capacity, reducing waste, minimizing costs and ensuring service continuity even during unexpected spikes. 

Research from IBM’s report, Agentic AI’s strategic ascent, underscores this pace of change: by 2027, twice as many executives expect AI agents will make autonomous decisions in workflows. Today, only 24% of executives report that level of autonomy; within two years; 67% expect this to be the norm. 

Overcoming barriers and delivering enterprise impact

Yet progress often stalls; not for lack of intent, but because of structural barriers. IT leaders must first overcome the obstacles that stand in the way of their evolution to value-focused work. These barriers are multifold: technical, cultural and operational.  

Even AI-forward IT organizations can lack the structural readiness for deep automation. For example, Ivanti’s “2025 Technology at Work Report” found that: 

  • 38% of IT professionals point to complex tech stacks as an issue in effective IT operations. 
  • And 72% say their IT and security data is siloed within their organization.  

Building a sustainable AI strategy requires more than a positive attitude – it demands alignment between IT and the broader business. Successful organizations are the ones aligning technology goals with tangible outcomes, supported by clear data structures, unified processes and teams equipped to manage new AI-driven workflows. Without that alignment, even the best tools struggle to deliver enterprise-wide impact. 

IBM research reveals another layer: 45% of executives cite a lack of visibility into AI decision-making as a major barrier. This AI “black box” problem isn’t just a technical one. It’s also about trust, clear communications and AI guardrails. Scaling agentic AI requires governance frameworks where automated decisions can be understood, audited and explained. 

Such transformation must keep humans in focus: design for augmentation — not replacement. 

The agentic AI operating model

Think of agentic ITOps as much more than an efficiency upgrade, but as a total reshaping of traditional IT workflows. Organizations leading the next phase of transformation are the ones rethinking how their systems make decisions, collaborate and adapt autonomously across the enterprise. 

That level of digital transformation requires leadership from the top. CIOs and executive stakeholders must pivot from experimentation to execution. They must look at embedding agentic AI not as a side project, but as a core operating model that aligns technology, data and people toward shared outcomes. 

That pivot marks the real test ahead. 

The future ROI of agentic AI initiatives

Even with the productivity boost enjoyed with generative AI tech, IT teams don’t need more tools. They need intelligence that delivers measurable outcomes. Teams should establish before-and-after metrics that resonate with IT and business leaders. Beyond efficiency gains (time saved, faster resolution, lower costs), measure labor savings, fewer resource-intensive outages and reduced tool sprawl. 

Transformational organizations see greater impact across every business metric, including productivity, efficiency, revenue growth, brand strength and customer loyalty, than those that limit AI to incremental and even superficial gains.  

In fact, organizations that excel in three key AI adoption areas are 32 times more likely to achieve top-tier business performance, according to that same IBM report referenced earlier.  

The bottom line

The next 12 to 24 months will test IT leaders’ ability to translate experimentation into sustained value. Those who embrace agentic AI early will build organizations that learn faster, adapt continuously, preempt potential issues and recover from disruption instinctively. 

IT has regularly shown that they're fully willing and committed to AI adoption. Now it must lead again, in depth. Agentic AI marks the next maturity stage: self-learning, self-healing and self-optimizing systems that enable greater agility and resilience across the enterprise.  

This isn’t “set and forget.” IT teams must build, train, monitor, measure and refine agentic AI to ensure value realization. 

To learn more about the role of AI as a transformative tool for IT operations and breakthrough use cases for agentic AI and automation in IT, see Ivanti’s research report: “AI: The Future of ITSM Automation.”