Since the World Economic Forum Annual Meeting in Davos, there has been a noticeable change in tone around AI.

Conversations have moved beyond bold predictions and breakthroughs towards understanding what it really takes to embed agentic AI into the fabric of an organization, and most importantly, making it work securely at scale.

Frustrated with isolated pilots sitting stagnant within IT operations, leaders are now turning their attention to implementing agentic AI across core business functions, ensuring those systems can move from experimentation to production without introducing new vulnerabilities.

Our 2026 report on agentic AI highlights that the questions being asked are more practical, such as how do we build autonomous systems that can be trusted? How do we maintain oversight without undermining agility? And how do we create the governance structures that allow innovation to accelerate rather than stall?

Encouragingly, the investment appetite suggests organizations are serious about expansion, with nearly three-quarters planning to increase spending on agentic AI over the next year.

However, investment alone will not guarantee business impact, and without addressing the barriers that prevent scale, larger budgets risk producing more pilots rather than enabling significant transformation.

For organizations across EMEA, those barriers are clear. The core challenge is not a shortage of talent, but security and data privacy concerns, with more than half of organizations in the region citing both as the biggest obstacles to scaling.

Why pilots aren’t enough

It’s clear that agentic AI projects won’t deliver substantial value if they remain stuck in the pilot phase.

So far, most deployments have concentrated on IT operations, but we’re beginning to see that change. More organizations are applying agentic AI in repeatable, customer-facing areas like customer support, where the impact is even more visible.

Even in fields like legal services - currently one of the slowest sectors to deploy AI - automation is expected to grow significantly over the next few years, showing that confidence in more complicated use cases is building.

Interestingly, the same capabilities that can streamline IT workflows, can also reshape how businesses serve their customers and drive commercial growth. Yet, as AI moves closer to customers and those core decision-making processes, the stakes naturally get higher, requiring greater oversight and guardrails to be put in place.

Security and privacy as gatekeepers: why human-in-the-loop remains essential

For today’s organizations, safeguarding security and privacy are top criteria for moving projects from pilot to production. However, for many leaders, the real barrier is not the complexity of the technology itself, but trust.

Establishing trust and confidence means identifying clear boundaries for when an AI agent can act autonomously but also guaranteeing human oversight at critical decision points. It’s increasingly clear that in the era of agentic AI, trust has become the ultimate control mechanism.

As it stands, nearly 70% of agentic AI decisions are currently verified by humans, and almost half of organizations conduct a human-led review of AI outputs as a verification measure. This signals a deliberate balance: the pendulum is not swinging entirely towards automation, nor is it reverting back to purely human control.

Instead, we’re seeing a productive equilibrium. Human judgement and agentic AI are complementing one another - AI executes with speed, while humans provide direction and guardrails. Agentic AI is emerging not as a replacement for human capability, but as a powerful partner in amplifying it.

As organizations integrate agentic AI more deeply into workflows, leaders must understand and apply this division of responsibility. AI may perform the execution, but humans must continue to define goals, set boundaries, and critically, retain accountability.

Building the control panel for trust

Business observability is fundamentally what makes this human-AI partnership sustainable, ensuring traceability and confidence at the human-AI interface.

As agentic systems grow more autonomous and interconnected, their complexity also increases. A small error in one model component - a hallucinated output or a misinterpreted prompt - can quickly cascade across applications and environments.

With a significant number of teams still manually reviewing agentic AI communication flows, this reveals a critical gap in real-time, context-aware automation.

Without comprehensive visibility, organizations are forced into a reactive stance - diagnosing issues only after they’ve already introduced risk. Simply logging events or flagging anomalies after the fact is no longer sufficient.

Today’s organizations need systems that can detect hallucinations and anticipate downstream impacts in real time, before they escalate into material problems. In increasingly complex multi-model and multi-agent ecosystems, observability is therefore the backbone of scalable, trustworthy autonomous operations.

From pilot to production: engineering scale through trust

To move beyond pilot purgatory, organizations must build the infrastructure of trust that allows agentic AI to operate securely at scale. Trust cannot be assumed, and it cannot be retrofitted. Instead, it must be designed into systems from the outset through robust observability.

The future of agentic AI will not be defined by the volume of experimentation, but by how effectively organizations operationalize what they build. When trust is engineered into the system, pilots do not stall, but progress and generate sustained business value.

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RVP of Solutions Engineering for UK&I at Dynatrace.