Start with the decision, not the model
Before comparing models, APIs, platforms or automation tools, define the business decision the project is meant to improve. The useful question is not “Which AI is best?” but “What decision, task or workflow needs to become more reliable, timely or manageable?” A model can generate text, classify information or support a process, but those capabilities only matter when connected to a specific business purpose.
Write the objective in operational terms. For example, a team may want to reduce the time needed to prepare a first draft for internal review, route incoming requests to the right queue, or identify records that require attention. Avoid objectives that describe technology without a business consequence, such as “add a chatbot” or “use generative AI.”
The objective should also name the person or team accountable for acting on the output. If no one owns the decision after the system produces a result, the project is not ready for technology comparison. This early clarity prevents a common mismatch: selecting a capable tool for a workflow that has not been defined well enough to use it.
- State the decision or task to improve.
- Name the business owner and the people who will use the output.
- Describe the expected action after the system produces an output.
- Define what should remain outside the project’s scope.
Choose the workflow boundary
A project boundary describes where the work begins, where it ends and what happens in between. This is a business design choice before it is a technical one. A narrow initial boundary can make it easier to identify inputs, approvals, exceptions and success measures. It also makes it easier to decide whether AI is needed at all, or whether a simpler automation step would address part of the problem.
Map the current workflow in plain language. Identify the trigger, the information received, the current manual steps, the decision points, the handoffs and the final outcome. Then identify the part where assistance might be useful. The aim is not to document every detail immediately; it is to expose assumptions that would otherwise be hidden in a model comparison.
Decide how exceptions will be handled before selecting technology. Real workflows include missing information, conflicting records, unusual requests and situations where the normal process should stop. If a project cannot state what happens in those cases, it cannot yet define the requirements for an AI component or an automation layer.
- What event starts the workflow?
- Which step creates the greatest delay, inconsistency or review burden?
- What information is required to continue?
- Which cases must be escalated or stopped?
- What is the final human or system action?
Make data and safeguards explicit
A useful AI project depends on controlled data, appropriate safeguards and an explicit business objective. Before comparing technologies, decide which information may enter the workflow, who may access it, how it will be checked and what must be excluded. These are business and governance decisions that shape the viable technical options.
Controlled data does not simply mean collecting more information. It means identifying the approved inputs for the defined task, their source, their quality limits and the conditions under which they can be used. A project may need to distinguish between structured records, internal documents, user-submitted material and information that should not be processed in the proposed workflow.
Human review should be designed as part of the operation, not added after a tool has been selected. Decide whether review is required for every output, only for selected cases, or at defined checkpoints. Also decide who can correct an output and how that correction affects the workflow. These choices help teams assess whether a proposed technology supports the intended controls.
- List approved data sources and excluded data.
- Define access roles for inputs, outputs and workflow settings.
- Set review points and escalation conditions.
- Decide how corrections, overrides and errors will be recorded.
Define success in production
Technology comparisons often focus on demonstrations, but a project needs a production measure. Before choosing a model, decide what evidence will show that the scoped workflow is helping the business objective. The measure should relate to the decision or process defined at the start, not only to whether the system can produce plausible output.
Production measurement can include operational signals such as completion time, review workload, routing accuracy against an agreed check, exception rates or the proportion of outputs that require revision. The appropriate measure depends on the context, existing systems and input quality. It should therefore be defined with the people responsible for the workflow rather than borrowed as a generic target.
Decide the review cadence as well. A project needs a way to inspect what happens after deployment, identify changed conditions and determine whether the workflow still fits its purpose. This does not require a promise of a fixed outcome; it creates a practical basis for follow-up and adjustment when the evidence calls for it.
- Choose one primary measure tied to the business objective.
- Add a small set of safety and quality signals.
- Set a baseline or current-state description where possible.
- Assign responsibility for reviewing results after deployment.
Example: triaging incoming requests
Example: An operations team receives incoming requests through a shared channel and wants faster, more consistent triage. Rather than beginning by comparing language models, the team first decides that the project’s purpose is to prepare a suggested category and priority for a reviewer. The reviewer remains responsible for the final routing decision.
The workflow boundary begins when a request arrives and ends when a reviewer assigns it to the appropriate queue. Approved inputs are the request text and selected internal reference fields. Requests containing incomplete or unclear information are marked for manual attention rather than automatically routed. The team also specifies that certain categories always require review before any next step.
The production measure is not “model quality” in the abstract. The team plans to review whether suggestions help reviewers reach a final routing decision with less avoidable effort, while tracking corrections, exceptions and cases that cannot be classified. Only after those choices are made can the team compare technologies against relevant requirements: handling approved inputs, supporting review, fitting current systems and enabling measurement.
- Business decision: what category and priority should a reviewer consider?
- Human review: every suggested route is confirmed by a reviewer.
- Data control: only defined request fields and approved references are used.
- Measurement: track reviewer corrections, exceptions and workflow time.
Use technology comparison as a final filter
Once the business decisions are clear, model comparison becomes more useful and less distracting. Evaluate options against the scoped workflow: whether they can work with approved data, support the required safeguards, fit the existing operational environment, enable human review and provide the information needed for production measurement.
This order also makes room for alternatives. The best first implementation may combine simple rules, workflow automation and an AI component, or it may reveal that a non-AI process improvement should come first. The decision should follow the business need and controls, rather than forcing the workflow to fit a chosen technology.
Victor Laybats provides AI and automation engineering services from Paris. Its public site documents an approach that runs from scoping through deployment and follow-up, and publishes practical guidance for scoping, securing and measuring AI and automation projects. This article is bounded by that public context: it offers a decision-oriented way to prepare an AI project, not a claim about particular models, outcomes or suitability for every organisation. Outcomes depend on context, existing systems and input quality.
- Compare only options that meet the data and safeguard requirements.
- Assess integration and operating needs alongside model capabilities.
- Confirm that review and measurement remain workable after deployment.
- Revisit the scope if the available technology cannot support the required controls.
Frequently asked questions
What should be decided before comparing AI models?
Decide the business objective, workflow boundary, approved data, safeguards, human review process and production measures before comparing AI models. These choices establish the requirements a technology must meet.
Why is human review important in an AI project?
Human review defines who checks, corrects or approves AI-supported outputs before they affect a business workflow. It is especially useful when requests are unclear, inputs are incomplete or exceptions need escalation.
How should an AI project be measured after deployment?
Measure an AI project in production using indicators tied to its stated business objective, plus quality, correction and exception signals. Review results regularly because outcomes depend on context, existing systems and input quality.
Sources and further reading
These resources provide the wider reference frame. Product statements on this page are limited to the public information provided by Victor Laybats.