The promise of AI agents to extend automation into complex knowledge work is too good for enterprise leaders to ignore. Across every industry, organizations are experimenting with AI to automate complex tasks, finding real value in work that previously relied entirely on human judgment. Yet for many, that promise remains unfulfilled.
Despite widespread experimentation, almost three-quarters (73%) of organizations admit there’s a significant gap between their agentic AI vision and the current reality, according to Camunda's State of Agentic Orchestration and Automation 2026 report. While many report using AI agents, only one in ten projects reached production last year.
The result is a growing sense of frustration: plenty of pilots and experimentation, but limited impact and results. Unless organizations close the vision-reality gap, they will realize only a fraction of AI’s potential value.
The challenge of trust in agentic AI
Organizations understand that AI can bring substantial value by augmenting human work but must trust the output of agents before rolling out the technology at scale. For many, that trust has yet to be established, making it the biggest barrier to moving agents from pilot to production.
Many organizations worry about the business risk of AI systems in day-to-day operations when IT teams lack adequate controls. Others point to a lack of transparency around how AI is used within business processes, as well as compliance concerns, or lacking internal skills to effectively manage AI. These issues pose a significant barrier to AI adoption in highly regulated industries — such as banking, healthcare or insurance — where transparency, auditability and traceability are non-negotiable.
AI adoption stuck in pilot mode
These anxieties shape AI adoption today. Organizations are comfortable using agents for low-risk tasks or internal copilots, with 80% reporting that most AI agents are chatbots or assistants that summarize or answer questions. However, organizations remain cautious about deploying agentic AI in end-to-end, mission-critical, or highly regulated processes. In fact, 50% of leaders warn that untamed agentic AI risks “fanning the flames” of poorly implemented processes and automations.
This cautious approach is understandable but carries its own risk. If AI agents remain stuck in pilot territory, organizations will leave efficiency gains untapped and struggle to justify growing AI investments. Embedding agents into orchestrated processes allows teams to redesign customer journeys and optimize internal operations with far greater consistency, control and impact.
To achieve these benefits, enterprises need a way to make AI agents trustworthy participants in mission-critical processes.
Orchestration: The missing link to scale AI
As organizations strive to close the AI vision-reality gap, the majority of IT leaders (88%) recognize that AI must be orchestrated across business processes to get the maximum benefit from AI investments. A further 90% agree that AI must be orchestrated like any other endpoint within automated processes to ensure regulatory compliance. However, despite these intentions, most organizations have not reached the process maturity required to implement agentic orchestration.
For years, deterministic process models have helped organizations bring order to increasingly complex automation landscapes. Deterministic processes allow teams to define responsibilities, integrate systems, manage exceptions and maintain a clear audit trail of what happened and why.
Agentic orchestration applies that same discipline to AI by combining deterministic guardrails with dynamic reasoning. It provides a control layer that governs how and where agents are allowed to act inside a process. It can specify which decisions must involve a human in the loop, when escalation is required and how outcomes are logged for review. Every action becomes part of a transparent, traceable workflow rather than a siloed experiment.
At the same time, agentic orchestration allows AI agents to adapt to new information in real time, while remaining within clearly defined guardrails. Blending deterministic control with dynamic logic allows organizations to balance agility with oversight and scale AI with confidence.
With this foundation, AI agents can finally move into the heart of the business. Claims processing can be accelerated without compromising compliance. Fraud investigations can adapt to real-time signals while preserving auditability. Customer onboarding can become faster and more personalized without introducing operational risk.
Building trust, unlocking value
Agentic orchestration, not standalone agents, is the key to closing the AI vision-reality gap. With the right framework in place, organizations can embed AI agents into governed, transparent, mission-critical processes. Only then can AI agents move beyond isolated pilots to drive efficiency, enhance customer journeys and deliver measurable business impact.
