A team can compare an AI idea with a simpler non-AI solution by defining the same decision or workflow, establishing a credible baseline, and measuring both against the same outcomes, failure modes, operating demands, and deployment context. NIST describes quantitative, qualitative, and mixed-method approaches for measuring AI risk, and connects the choice of measurement approach to deployment context.
Define the decision before comparing solutions
The comparison should begin with the work, not with the technology. The team needs to state:
- What decision or workflow is being supported?
- What information is available when the decision is made?
- What output is required?
- Which errors matter most?
- How should exceptions be handled?
- What happens when the system is unavailable or produces an unreliable result?
Without these conditions, an AI proposal can appear valuable simply because it is more sophisticated, while a simpler process may already meet the actual requirement.
A useful comparison starts with shared criteria:
| Question | Simpler non-AI solution | AI idea |
|---|---|---|
| What part of the work does it perform? | Rules, search, analytics, a defined process, or human review | A model-assisted or model-generated output |
| Which inputs can it handle reliably? | Inputs that fit defined rules or available fields | Inputs requiring interpretation, variation, or generated content |
| How does it fail? | Missed cases, rule gaps, processing errors, or unavailable data | Incorrect, inconsistent, biased, or hard-to-explain outputs |
| What work follows the output? | A fixed review or escalation path | Additional checking, interpretation, or monitoring |
| What does it require to operate? | Maintenance of rules, data, or the existing process | Model operations, input monitoring, review, and risk controls |
The table is not a scoring formula. It helps the team identify where the two approaches differ in practice.
Make the simpler baseline credible
A fair comparison does not use an obviously inadequate non-AI option as the baseline. The alternative should be plausible for the same task and operating conditions.
Depending on the workflow, a baseline might be a current manual process, deterministic rules, conventional search, standard analytics, a spreadsheet, or a defined human-review procedure. The appropriate choice depends on the task; none is automatically superior.
The baseline and the AI idea should be tested with comparable cases, similar information, and the same decision requirements. If the non-AI option is given incomplete data or unrealistic time constraints while the AI option receives carefully selected examples, the resulting comparison will not be useful.
The team should also record what the baseline already does well. A simpler solution may already handle the common cases adequately, leaving the AI proposal responsible for demonstrating what additional capability is necessary.
Measure outcomes and risk together
Results matter, but so do the consequences of incorrect results. NIST describes quantitative, qualitative, and mixed-method tools, techniques, and methodologies for measuring AI risk.
A team can apply those approaches in different ways:
- Quantitative measures can examine observable patterns such as error distribution, missed cases, consistency, processing volume, or resource use.
- Qualitative measures can examine failure modes, user feedback, review notes, edge cases, and the reasons behind an incorrect result.
- Mixed methods can combine numerical results with structured review of cases that metrics alone may not explain.
The methods should be selected for the decision rather than applied because they are available. A quantitative average, for example, may hide a serious failure affecting a smaller but important group of cases. Qualitative review may identify a problem that a count does not capture, but it may also require a consistent review process to make comparisons reliable.
NIST also states that measurement approaches for identifying AI risks are connected to deployment context. That means the evaluation should reflect the conditions in which the solution is intended to operate, including the relevant data sources, users, operating constraints, and consequences of failure. A result from a demonstration or a limited test should not be treated as equivalent to evidence from the intended deployment without further confirmation.
Compare the full operating burden
The choice should not be based only on output quality or apparent capability. The team should compare the work required before, during, and after use.
Relevant questions include:
- What has to be built or configured?
- How will inputs be prepared and checked?
- Who reviews the output?
- How will errors be detected and escalated?
- What happens when the input distribution changes?
- Who maintains rules, data, workflows, or system settings?
- What monitoring and documentation are required?
- How will unexpected behavior be handled?
These questions apply to both sides of the comparison. A manual process may have substantial review and training costs; an AI system may introduce additional monitoring, evaluation, and maintenance work. The point is not to assume that either option is simpler in every respect, but to identify where the complexity actually sits.
Make the decision rule explicit
The team can set a decision rule before reviewing the results. For example:
- Define the requirements that the solution must meet.
- Define which failures are acceptable and which are not.
- Test the non-AI baseline against the same requirements.
- Identify any material requirement the baseline cannot satisfy.
- Assess whether the AI idea’s additional capability justifies its added complexity and risks.
- Document the reason for choosing the baseline, the AI idea, or a redesign of the workflow.
Under this rule, the simpler solution should remain the default when it meets the required conditions within the team’s risk tolerance. The AI idea should need to show a specific gap that matters in the intended context, along with a credible way to manage the additional failure modes and operating requirements.
A comparison based only on a successful demonstration is incomplete. A demonstration may show that an output can be produced under selected conditions, but it does not by itself establish how the solution will behave across the full workflow.
What the team must still confirm
Before making the decision, the team must still confirm several matters that cannot be settled by a general measurement method alone:
- Whether the non-AI baseline is realistic for the actual workflow.
- Whether the evaluation cases represent the intended deployment context.
- Which errors are tolerable and who decides that boundary.
- How edge cases, exceptions, and failures will be handled.
- Who owns review, monitoring, maintenance, and escalation.
- Whether the evidence covers ordinary cases as well as unusual ones.
- Whether the proposed solution’s benefits are material enough to justify the added work and risk.
The practical comparison is therefore straightforward: require the AI idea to explain what the simpler baseline cannot do, show why that difference matters in the intended context, and account for the risks and operating work created by the difference. If the evidence does not meet those conditions, the simpler solution remains the more defensible choice.