In today’s landscape of digital transformation, businesses face growing complexity in managing automated processes across disparate systems. Without proper coordination, organizations risk falling into what Daniel Meyer, CTO of Camunda, calls “Automation Armageddon” – where fragmented automation creates digital chaos instead of business value.
In this exclusive VMblog interview, Meyer explains how process orchestration provides the necessary framework for organizations to not only manage complex end-to-end processes but also to effectively integrate and scale AI capabilities, including the emerging field of agentic AI. He discusses how Camunda’s platform helps businesses maintain visibility, flexibility and governance while leveraging AI to transform operations without compromising compliance or control.
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VMblog: Why do businesses need process orchestration and what challenges do they face without it? How does Camunda help organizations resolve those challenges and orchestrate complex processes?
Daniel Meyer: Businesses need process orchestration to manage growing process complexity. As organizations pursue digital transformation, many have adopted a wide variety of tools and technologies, so called process endpoints, to automate every aspect of their business – these can be legacy systems, microservices and RPA tools, but also AI. However, if implemented in a disjointed way, processes can become fragmented and trap value in automation silos. These “local” automation silos stand in the way of reaching important internal and external business goals for automation, such as enhancing customer experience and team efficiency, and diminish the return on investment. Without a shift in approach, organizations risk falling into digital chaos, and potentially “Automation Armageddon”, if processes are left unchecked.
For organizations to keep pace with their digital transformation goals, they must integrate the various endpoints of a business process in a coherent and orchestrated way, supporting innovation without compromising control. Camunda helps organizations by coordinating complex processes across people, systems, and devices from end to end. Our platform provides the visibility, flexibility, and governance needed to not only orchestrate complex business processes but scale automation effectively. Recently, we expanded our capabilities to become both a process orchestration and automation platform, introducing Camunda RPA for robotic process automation and Camunda IDP for intelligent document processing. We’ve also launched agentic orchestration capabilities to help organizations seamlessly model, deploy, and manage AI agents within end-to-end processes.
VMblog: What sets Camunda apart from competitors, particularly in the evolving landscape of AI-driven automation?
Meyer: Process orchestration has always been our focus and is the core of our platform. While many automation tools focus on specific tasks or a particular sub-process, Camunda enables end-to-end automation, offering a unified approach to designing, automating, and optimizing entire processes. We have years of experience in helping organizations orchestrate?and improve their mission-critical business processes – no matter where they are and what they entail.
Today, we’re looking to AI to redefine what’s possible in process orchestration and automation. For instance, Camunda Copilot was developed to boost IT and business user productivity, enabling faster and more efficient process modeling through AI-powered suggestions. In addition, our AI connectors enable businesses to seamlessly integrate AI into their processes, orchestrating AI/ML tools and services just like any other process endpoint.
As conversations surrounding agentic AI continue to grow, Camunda now provides a wide range of out-of-the-box AI agents, along with the flexibility to build custom agents for specific tasks. These agents are integrated into BPMN-modeled processes, enhancing process automation and driving business efficiency while ensuring governance and compliance.
VMblog: What are the main challenges that organizations face when it comes to scaling and operationalizing AI?
Meyer: Despite most organizations looking to expand their AI capabilities in the next three years, 85% face challenges in effectively scaling and operationalizing AI. While AI has the power to transform business operations, many organizations only deploy point solutions to address individual tasks rather than to support a business-wide strategy. This fragmented approach makes it difficult for businesses to fully realize AI’s value, as disconnected AI initiatives often fail to work cohesively within larger business processes.
As AI and automation scales, many organizations lack the control to identify what is working, what is redundant, and where bottlenecks exist. This lack of visibility can increase operational risks, particularly related to compliance, as organizations struggle to ensure that all automated processes meet regulatory requirements – especially when AI decisions are involved.
VMblog: Why is orchestration essential for leveraging Agentic AI in business-critical processes?
Meyer: Process orchestration is essential to ensure that AI agents operate within controlled, transparent, and compliant environments. It allows organizations to see exactly what’s happening at each step. This creates an audit trail that simplifies compliance reporting and helps verify AI decisions, which is essential in highly regulated industries like for example financial services.
Depending on the level of regulation, agentic orchestration offers the flexibility needed to scale AI agents as needed. Organizations can adjust the level of AI agent involvement or implement human-in-the-loop checks to have more fine-grained control over AI outputs, helping to strike a balance between AI’s efficiency with human oversight.
This approach allows businesses to embed AI agents into their operations in a structured, traceable way – operationalizing AI while minimizing compliance risks, technical debt, and operational disruptions.
VMblog: How do deterministic guardrails help balance the autonomy of Agentic AI with control and compliance?
Meyer: Deterministic guardrails are essential for balancing the autonomy of agentic AI with the need for control and compliance. The promise of AI agents lies in their ability to act independently and make decisions based on complex reasoning.
With an AI-enhanced process, some steps may follow a pre-defined logic, making it easy to predict what will happen next, while others are executed using a large language model (LLM). LLM outputs are more difficult to predict but can be highly beneficial for dynamic, variable processes that don’t need to follow a specific structure.
To manage AI agent’s autonomy, businesses must implement “guardrails” that combine deterministic processes with the non-deterministic nature of AI capabilities. By clearly defining operational parameters, businesses can ensure AI operates independently while adhering to organizational policies and regulatory standards. This approach allows businesses to leverage AI’s adaptability and predictive power within structured, reliable processes without sacrificing compliance or control.
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