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AI in business processes is often framed as a replacement for existing ways of working. In reality, it is better understood as an additional tool in the automation toolbox. For operations managers, business analysts, and innovation managers, the real question is not whether to use AI, but where it fits best alongside traditional automation and human decision-making.
Looking at business processes through a simple decision tree helps clarify when to rely on deterministic automation, when to introduce AI, and when human intelligence should remain in control.
Before large language models (LLMs) became available, business processes were typically split into two categories.
First, there were deterministic processes. These are processes with clear rules, predictable outcomes, and structured data. If X happens, the system does Y. Traditional automation, system integrations, and rule-based workflows work well here and have proven their value over many years.
Second, there were non-deterministic processes. As soon as unstructured data entered the picture (free text, documents, emails, or subjective evaluations) humans stepped in. Tasks such as reviewing motivation letters, interpreting written feedback, or assessing qualitative input were considered human-only work.
This division shaped how workflows were designed: automate what you can, and hand the rest to people.
LLMs change this picture, but they do not eliminate the need for structure or human oversight.
AI is not a replacement for deterministic automation. If a process can be fully automated using rules, integrations, and existing systems, that is still the preferred approach. It is reliable, explainable, and predictable.
The new opportunity appears when deterministic automation stops working. This usually happens when unstructured data is involved. Here, AI can act as a bridge by transforming unstructured input into structured data that can re-enter an automated workflow.
Examples include:
In this sense, AI extends what automation can handle. It does not replace traditional automation but feeds it.
Despite their capabilities, LLMs are not deterministic. Given the same input, they may produce slightly different outputs. In business-critical processes, this introduces risk.
For that reason, human-in-the-loop workflows remain essential. Humans provide the final check, approval, or decision, especially when outcomes can have legal, financial, or reputational impact.
This hybrid model balances efficiency and control:
The result is not autonomous AI, but faster and more focused human work.
When designing or optimizing a process, the following logic per step in your process can help:
This approach reduces risk while unlocking efficiency gains.
Recruitment is a clear example of where human intelligence remains critical, but AI can still add value.
In a typical recruitment workflow:
AI can be used to:
What AI should not do is make the hiring decision autonomously.
Instead, the workflow can be designed so that:
This approach delivers two clear benefits:
Even when human review is required, the time spent per candidate is significantly reduced.
The strength of AI in business process automation is not autonomy, but leverage. By handling repetitive analysis and preparation tasks, AI allows humans to focus on judgment, exceptions, and accountability.
For organizations, this means faster process execution, better use of skilled employees’ time, reduced operational bottlenecks, and controlled adoption of AI with limited risk.
AI should be treated like any other technology in business process management: chosen deliberately, applied where it fits, and combined with existing tools.
Traditional automation remains the foundation. AI expands what can be automated. Humans remain essential where decisions matter.
Using this decision tree helps organizations adopt AI in a controlled, practical way—unlocking efficiency gains without introducing unnecessary risk. These principles for integrating AI into business processes provide a useful framework for designing responsible, scalable AI-enabled workflows.
