The question is not whether a workflow uses AI. The better question is what the workflow needs in the first place.
Many business processes contain repeatable steps, known inputs, decision rules, and expected outputs. Those parts may not require an AI system. They may benefit more from being made clear, consistent, and observable through a deterministic system.
That does not make AI less useful. It gives AI a defined place to contribute. A strong workflow foundation can provide the structure around an AI tool, rather than asking the tool to define the entire process.
What is a deterministic system?
A deterministic system operates according to explicit rules or instructions. When the same relevant conditions are supplied, the system is designed to follow the same logic and produce an expected type of result.
A simple illustrative example is a routing rule: if a request contains a particular category and meets a defined condition, send it to a specified queue. The value is not novelty. It is clarity. Someone reviewing the workflow can inspect the rule, understand why the route occurred, and change the rule when the process changes.
Deterministic systems can include more than a single rule. They can define the sequence of steps, the information required at each stage, the permitted transitions, and the action that should occur when a condition is met. They can also make responsibilities clearer by showing where a person, system, or approval fits into the process.
The term “deterministic” does not mean that the wider business environment is perfectly predictable. Inputs can be incomplete, people can make mistakes, and exceptions can occur. It means the system’s intended behavior is defined rather than left entirely to interpretation.
How deterministic systems differ from AI
AI systems and deterministic systems solve different kinds of problems, although they can work together.
A deterministic system is generally suited to logic that can be stated in advance. If the business can define the conditions, decisions, and outputs clearly, rules-based automation may be an appropriate starting point.
AI is useful when a workflow involves information that is difficult to reduce to fixed rules. Examples might include interpreting natural language, summarizing material, generating a draft, or helping classify content. These tasks involve context and variation, so an AI component may add value where a purely rules-based approach would be limited.
This distinction is about fit, not fear. AI is a tool. It does not need to replace every existing process, and a workflow does not become more useful simply because AI has been inserted into it.
A helpful way to think about the difference is to separate the workflow’s structure from the work performed within that structure:
- Structure: the sequence, permissions, rules, handoffs, and required information.
- Interpretation: the work of understanding variable or unstructured input.
- Action: the next step, approval, update, or communication.
- Oversight: the checks that determine whether the result is acceptable.
The structure may be deterministic even when one step uses AI. For example, a process could define when an AI-generated summary is requested, who reviews it, what information it may use, and what happens after approval. The AI contributes to a bounded step instead of becoming the entire operating model.
Why the workflow foundation comes first
The interview behind this article emphasized a practical observation: workflows often already exist inside an organization, but their logic may be informal, inconsistent, or difficult to inspect. Before adding a new tool, it is useful to understand the workflow that tool would enter.
A workflow foundation can help answer basic questions:
- What starts the process?
- What information is required?
- Which decisions are governed by clear rules?
- Where do exceptions go?
- Who or what is responsible for each next step?
- What outcome indicates that the process is complete?
These questions are not an argument against AI. They are a way to prevent unclear process design from being mistaken for a technology problem. If the sequence, ownership, and decision criteria are undefined, adding AI may make the process harder to understand rather than more effective.
Starting with a deterministic foundation also creates a clearer boundary for experimentation. A team can identify the parts of the workflow that are stable and rule-based, then consider whether AI is appropriate for a particular variable task. That approach keeps the decision tied to the work instead of to the availability of a tool.
Where AI can add value
AI may be a useful component when the workflow must work with language, context, or material that is difficult to handle through fixed rules alone. It may help produce a first draft, organize information, identify possible categories, or support a person’s review.
Those uses should be defined in relation to the workflow. The relevant questions are not only “Can AI do this?” but also:
- What input will the AI receive?
- What kind of output is needed?
- Who checks the output?
- What happens when the result is incomplete or unsuitable?
- Which decisions must remain governed by explicit rules or human approval?
The answers will vary by workflow. In some cases, AI may be unnecessary. In others, it may be useful within a controlled step. The important design choice is to give it a role that can be understood and reviewed.
A practical way to choose the right approach
When reviewing a workflow, begin by mapping the process without assuming that AI belongs anywhere in it. Record the trigger, inputs, rules, handoffs, exceptions, and desired output. Then divide the work into two broad groups: tasks that can be described clearly in advance and tasks that require interpretation of variable information.
For the first group, consider whether deterministic logic can provide the required behavior. For the second, consider whether AI could assist, and define the boundaries around that assistance. Finally, identify the review and exception paths before deciding how the pieces should be connected.
This sequence produces a more grounded question than “Where can we add AI?” It asks: “Which parts of this workflow need predictable rules, and where could an AI capability help with the parts that require interpretation?”
That is the core idea: deterministic systems and AI are not competing definitions of automation. They can be complementary. A clear workflow foundation creates the structure; AI can be added where its particular capabilities fit the work.
Next step
Review one workflow in your organization. Identify its triggers, rules, handoffs, exceptions, and expected outputs. Then mark the steps that require interpretation rather than fixed logic. This will help you identify where clear, deterministic rules could provide a stronger foundation before adding AI.