Without structured process orchestration, even the most innovative AI tools can become disconnected and ineffective. As companies seek to leverage artificial intelligence, one crucial question often remains unasked: what acts as the sinew that holds all these intelligent components together? In this post, I will explore three reasons why a process engine should be viewed as the backbone of any AI initiative. It provides structure for automation, context for data, and visibility into transactions—transforming isolated AI efforts into a coordinated, value-generating system.
Introduction: What is a Process Engine?
In simple terms, a process engine is the system where business processes are modelled and executed in the real world. Through its interfaces, organisations gain real-time visibility into ongoing processes, including bottlenecks, exceptions, and performance trends, which facilitates timely interventions.
Each step in a process can either be automated (handled by code) or manual (delegated to a person and awaiting confirmation). The automation “code” may consist of ready-made or custom-built integrations, scripts, robotic automation, or increasingly, AI agents.
Process engines can also integrate logic directly into workflows with conditional paths (e.g., “if the customer belongs to group A, then…”); rule tables (e.g., “if A and B, then Y”); and waiting conditions (e.g., “retry after 30 minutes”). Together, these features make the process engine a dynamic system capable of adapting to complex business realities.
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Reason 1: Personal Tools Aren’t Always So Personal
Generative AI tools—like ChatGPT used to assist with this post—are excellent for content creation. However, it’s crucial to recognise when these personal tools are actually part of a larger business process.
A clear indicator is the amount of copy-pasting involved. If you find yourself manually transferring content between systems, it might be worth reconsidering whether that step could be modelled and orchestrated through a process engine instead.
For example, this article was traditionally written, but AI is utilised for language refinement, illustrations and translation. A draft is shared for comments, and the final version is published via our content management system (CMS). Beneath this, you can actually see a content production process for marketing.
Since our blog publishing is infrequent, automation may not add value. However, if we aimed, for example, for consistent content, we could trigger this process weekly, bringing structure and transparency to our publishing workflow. Instead of copy-pasting text into ChatGPT and Grammarly and back to Google Docs, the raw material would move through dedicated agents for each purpose (translation, editing, etc.).
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Reason 2: AI Is Not a Silver Bullet
We often encounter pain points in system integrations or manual workflows, and there’s a common perception that AI serves as the “silver bullet” to solve all issues.
While this idea holds, simply applying AI to mask structural problems often creates new complications. We saw a similar situation with the introduction of Robotic Process Automation (RPA): it addressed short-term gaps, but as exceptions increased, they added complexity over time.
A more effective approach is to first model the process or workflow in which the issue occurs. Frequently, simply modelling and running the process through a process engine—with small, targeted automations—can resolve 80% of the problems without needing AI.
When AI is introduced, it’s preferable to integrate it within the process as a managed component rather than treating it as a hidden black box within your system architecture. The same concept applies to RPA: consider it a part of the process rather than a patchwork solution to maintain overall control.
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Reason 3: “Our System Already Has AI Features—Why Should We Use Separate Agents?”
This is a valid question that deserves careful evaluation on a case-by-case basis. However, it’s essential to approach this assessment cautiously to avoid significant pitfalls later.
Most leading business systems now come with built-in AI agents—for instance, CRM systems for prospecting and analytics, financial systems for handling invoices, and recruitment platforms for candidate matching and automated responses. However, there are three reasons to think critically before solely relying on these features:
1. Best-of-Breed Flexibility
Are those built-in agents truly the best fit for your current and future needs? By isolating your processes into a dedicated orchestration layer, you preserve the flexibility to swap components as technologies evolve. The pace of AI development is rapid, and large vendors may struggle to keep up with the latest language models. Additionally, built-in AI features can be surprisingly expensive compared to external alternatives.
2. Cross-System Processes
In many organisations, workflows extend beyond individual system boundaries. If data still needs to be transferred manually or through fragile integrations, it may be more sustainable to orchestrate those workflows using a process engine.
3. Vendor Lock-In Risk
One of the key advantages of having a dedicated process layer is increased independence, reducing the risk of being locked into a single vendor’s solutions.
Orchestrating Agents — Not Competing With Them
It’s worth mentioning that there are specialised tools for orchestrating AI agents, such as CrewAI. These multi-agent frameworks are not competitors to process engines.
A single process step may contain multiple chained agents, orchestrated via such a framework — while the process engine oversees the higher-level workflow. The process engine tracks the overall state, and the agent orchestrator reports status updates back to it. Together, they provide visibility and control from the top down.
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Conclusion – No Single Right Answer
There’s no universal solution here — only professional judgment and well-founded architectural choices. The goal is to build an IT landscape that best supports your business.
A process engine doesn’t replace AI or automation tools. Integrating a process engine into your AI strategy not only optimises performance but also fosters a more cohesive and controlled environment for leveraging artificial intelligence effectively — bringing clarity, structure, and long-term adaptability to your organisation’s digital backbone.
