Companies confess their agentic AI goals aren't really working out - and a lack of trust could be why

• Business risks, transparency, and compliance are the biggest hurdles for agentic AI deployment
• Nearly half of organizations are running AI agents in silos
• Allowing AI to adapt to workload variables is key

Three in four (73%) organizations admit there's a gap between their ambitions and reality when it comes to deploying agentic AI tools, and it's because they lack trust.

Just to drive that message home, a report from Camunda report reveals that despite 71% of organizations using AI agents, only 11% of use cases reached production last year.

Business risks (84%), transparency (80%) and regulatory/compliance concerns (66%) are the main hurdles – but businesses are still going all-in on investments, leading to appallingly low ROI.

Agentic AI isn't being used to its full potential

Four in five were found to be using AI agents as chatbots or assistants only, with nearly half (48%) admitting that their agentic systems work in silos, lacking full context.

Many AI agent applications also require human approval, preventing them from being as effective as the technology suggests.

"Right now, exercising caution with agentic AI means many organizations can’t move beyond pilots or isolated use cases," Camunda Customer Success SVP Kurt Petersen wrote. "Once a foundation of trust is in place, agents can become powerful multipliers inside governed processes instead of siloed copilots or chatbots."

However, those who did use agentic AI's full capabilities saw strong results – 95% saw business growth from automation, and nearly four in five (79%) plan to increase automation spend as a result. With tech stacks becoming far more distributed (76% agree), agentic AI could hold the key to tying multiple systems together.

Camunda says the answer lies in agentic orchestration, which means the combination of deterministic orchestration (fixed rules and workflows) and dynamic orchestration (agentic AI being able to respond and adapt to variables).

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