A new report from Dynatrace suggests that roughly half of all agentic AI projects are still stuck in the proof-of-concept or pilot phase, highlighting how many organizations are struggling to move beyond experimentation and into full-scale deployment. As a result, many businesses are falling short of the ROI they were hoping to achieve.
Importantly, the issue isn’t a lack of belief in AI’s potential. Instead, progress is being slowed by challenges around governance, safety, and operational readiness. On top of that, one in three organizations admits it still lacks a clearly defined business case for agentic AI, making it harder to justify wider rollout.
Despite these hurdles, confidence in the technology remains strong. Nearly three-quarters of businesses (74%) say they plan to increase spending on agentic AI next year, signaling that companies see these obstacles as temporary rather than deal-breakers.
The biggest barriers—and how to move past them
According to the report, agentic AI is currently being deployed most heavily in IT operations and DevOps (72%), followed by software engineering (56%) and customer support (51%). However, Dynatrace also found a disconnect between where companies are investing today and where they actually expect the strongest returns.
When it comes to ROI, organizations believe the biggest gains will come from IT operations and system monitoring (44%), cybersecurity (27%), and data processing and reporting (25%)—not necessarily the areas receiving the most attention right now.
The research highlights several major blockers slowing adoption:
- Security, privacy, and compliance concerns (52%)
- Difficulty managing and monitoring AI agents at scale (51%)
- Lack of skilled talent or adequate training (44%)
Human oversight also remains a critical part of the equation. Business leaders anticipate a roughly 50:50 balance between humans and AI agents for IT and routine support work. At present, about 69% of agentic AI decisions are still reviewed by humans, and 87% of organizations are building agents that require some level of human supervision. An additional 23% say they prefer to rely exclusively on human-supervised AI agents.
Looking ahead, Dynatrace advises organizations to rethink how they measure success, moving beyond traditional ROI metrics. Other recommendations include setting clear guardrails for human–AI collaboration, and scaling agentic AI deliberately, rather than pouring money into multiple initiatives without a clear path to value.
The message is clear: agentic AI has real potential, but realizing it will require stronger foundations, smarter scaling, and a more realistic approach to measuring impact.
