- Agent AI implementation is slow, but the technology itself is not to blame
- Privacy, compliance, governance and lack of skills all create obstacles
- Dynatrace says the way forward is to redefine ROI and focus on human-machine collaboration
A new Dynatrace report has claimed that around half of agent AI initiatives are still in proof-of-concept or pilot stages, showing how organizations are struggling to move from experimentation to full implementation, holding them back from the ROI they are aiming for.
But AI’s value isn’t the factor being questioned—rather, it’s barriers like governance and security that cause delays. Furthermore, one in three cites a lack of a clear business case as a barrier to progression.
But companies are undeterred, with three-quarters (74%) expecting to increase agency AI budgets next year.
These are the biggest agentic AI barriers and how to navigate them
Today’s top implementation areas are IT operations and DevOps (72%), software engineering (56%) and customer support (51%), but Dynatrace’s report reveals a gap between investment focus and where companies expect to see the greatest ROI. Instead, the best returns are expected to come from IT operations and system monitoring (44%), cyber security (27%) and data processing and reporting (25%).
The survey details some of the top prevention barriers, including security, privacy and compliance concerns (shared by 52% of respondents), difficulty managing and monitoring agents at scale (51%) and lack of qualified staff or training (44%).
Business leaders also highlighted the importance of human workers in an agent world, predicting a 50:50 split for IT and routine support tasks. Currently, around two-thirds (69%) of agent AI decisions are still verified by humans, and 87% build AI agents that require human supervision.
Another quarter (23%) prefer to rely exclusively on human-supervised funds.
Looking ahead, Dynatrace’s recommendations include rethinking metrics and redefining ROI, establishing clear railings for human-machine collaboration, and scaling slowly with intent rather than throwing large amounts of cash with varying degrees of success.
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