The cost of an enterprise AI process can be significantly influenced by which model is assigned to which task. If we use the same high-performance large language model for every minor decision, we may expend significant resources on steps that could be resolved more simply. A consciously designed decision layer can enable more cost-effective, faster, and more traceable operations.
In an AI workflow, numerous decisions are made even before the actual response is generated. Are internal documents needed? Which AI agent or tool should perform the task? Is a smaller model sufficient, or is more complex reasoning required? When should human verification be requested?
These decisions determine the subsequent path of the process. If we delegate all of them to a powerful, general-purpose language model, it can increase costs and response times even when the decision itself is relatively simple.
Fast Decisions and Complex Reasoning
Daniel Kahneman's System 1 / System 2 framework can serve as a useful analogy for designing AI processes. Here, the comparison helps distinguish between two types of tasks: fast, structured classification and problem-solving that requires deeper deliberation.
A fast decision layer can provide answers to questions such as:
Is document retrieval required for the answer?
Which model should the request be routed to?
Which AI agent or tool needs to be involved?
Is human verification warranted?
For these steps, the possible outcomes can be predefined. In such cases, the system's task is to classify the request, evaluate the potential routes, and determine the appropriate next step.
Meanwhile, a so-called reasoning model capable of more complex deduction can be brought in for tasks that genuinely require this capability.
What Does This Mean for a Specific Question?
Consider a seemingly simple request:
“When does the vendor contract expire, and what is the notice period?”
The system must first determine whether accessing the contract is necessary to answer. Then, it needs to decide what level of processing is sufficient to extract the relevant information.
If the document clearly contains the requested data, the task can be handled by a simpler processing path. However, if multiple amendments, cross-referencing clauses, or contradictions complicate interpretation, more complex analysis or human review may be required.
Therefore, the appropriate path is influenced not only by the request itself, but also by the available documents and the potential consequences of an error.
The Decision Layer is More Than Just Model Selection
The role of the decision layer can extend beyond simply selecting which model to use. It can play a role at several points throughout the process:
Route selection: designates the appropriate model, AI agent, or tool.
Conditional gating: verifies whether the requirements for the next step are met.
Evaluation: scores potential outcomes based on specified criteria.
Escalation: routes the task to a more complex model or a human.
From a process design perspective, this logic is akin to decision points in classic workflows. The difference is that, alongside fixed rules, model-based assessments that inherently carry uncertainty can also support progression to the next step.
In these cases, it is especially important to define what should happen in the event of an uncertain result. A score provided by a model does not, in itself, guarantee correctness.
The Right Mechanism for the Right Decision Point
When designing an enterprise AI process, it is worth examining at every decision point which solution is sufficient to achieve the expected quality:
rule → decision model → reasoning model → human deliberation
This is not a mandatory sequence of steps to follow end-to-end. Rather, it represents options: where a clear rule is sufficient, we can use it; where interpretation is required, we can involve a model; and where uncertainty or risk warrants it, we can request a human decision.
To achieve this, the potential outcomes, the conditions for moving forward, and the scenarios requiring human intervention must be defined in advance.
Where Does the Business Value Lie?
A thoughtfully designed decision layer can deliver advantages in three areas:
Cost: more resource-intensive models can be reserved for tasks where their use is justified. Actual savings should be measured across the entire process, including the overhead cost of the decision layer itself.
Speed: well-defined decisions can be executed using simpler mechanisms, thereby reducing the number of unnecessary model calls.
Transparency: with proper logging and evaluation, it becomes possible to trace which path a request followed and based on what criteria the decision was made.
In Clarity's approach, the application of enterprise AI is closely tied to process design. Beyond the model's capabilities, it is essential to define how it fits into operations, what decisions it can make, and when human oversight is required.
With explicit decision points, the operation of systems built on AI agents can be better aligned with enterprise expectations: costs, response times, quality, and areas of responsibility.
