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AI can help improve a process only when its inputs capture evidence relevant to the outcome. If important conditions go unmeasured, a model may produce confident predictions while missing the causes of defects. Before choosing software—or allowing it to act automatically—define what “better” means, measure the process accordingly, and test whether a model adds value.
Why measurement comes before useful predictions
A model can learn patterns only from information it receives. If the measurements omit a condition that drives variation, the model cannot directly account for that condition; it may instead rely on weaker correlations and give misleadingly precise outputs. This is one possible cause of model failure, not an explanation for every failure.
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In a manufacturing example, Aaron Bin Wang writes that shops may adopt monitoring, predictive maintenance, or automated quality tools and then find that dashboards miss important failures or trigger false alarms. He argues that the problem can begin with data that omit changing variables behind process variation. His account is an argument grounded in shop-floor experience, not evidence that measurement alone guarantees a successful AI project. Wang’s article in The AI Journal was published September 28, 2026.
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Before selecting sensors, software, or an algorithm, specify which process you are evaluating, where it begins and ends, and which outcome matters. Depending on the task, that outcome might be defect rate, dimensional consistency, delay, or risk. Define how it will be measured and what comparison will count as improvement.
#1 Best Overall
A baseline gives you a reference for judging whether performance changed. A benchmark can help assess an AI system against a suitable point of comparison. Neither is meaningful unless the measures fit the process and the intended decision. NIST’s voluntary AI Risk Management Framework (AI RMF 1.0) calls for assessing, benchmarking, and monitoring AI risks and impacts, with attention to metrics, uncertainty, and the system’s context. NIST says revision of the framework is in progress; it does not prescribe one universal set of metrics or require every process-improvement effort to follow a sensor-first sequence.
Choose measurements that reflect how the process works
Measurement quality depends on more than collecting a large volume of data. The variables need to be relevant to the outcome, collected reliably, and defined consistently enough to compare cases over time. A sensor in the wrong place, inconsistent readings, or a changing definition of “defect” can undermine analysis before model selection begins.
In a machining operation
Wang’s example points to temperature at relevant points, fixture repeatability, and in-process dimensional feedback as potentially important evidence. Those are examples, not a universal instrumentation specification: the variables worth measuring depend on the particular machine, operation, and failure modes.
Wang recounts that a predictive-quality trial failed when the line lacked reliable temperature and in-process measurements. After instrumentation and fixture improvements, he says, a model helped detect thermal drift. The manufacturer is not identified and no independent case data are supplied, so this is an attributed anecdote rather than a verified case study.
When workflow records can help
If a process already generates event records, process mining may help reveal how actual cases move through it. A ProcessMind explainer describes the basic ingredients as a case identifier, activity, and timestamp. That can support a baseline of process paths, but it depends on suitable records and consistent event definitions; buying software cannot supply missing evidence or resolve disagreement about what the process is. ProcessMind’s explanation is vendor-authored. Read its DMAIC and process-mining overview.
Decide whether AI is the right model
Once the measurements are credible enough for the task, compare plausible ways to analyze them. Wang notes that a physics-based or statistical approach may be easier to validate than machine learning in a stable operation. NIST’s guidance likewise emphasizes evaluating performance and uncertainty rather than assuming a technique works because it uses AI.
Rank #4
Use these questions to compare alternatives:
- Relevance: Does the measure correspond to the outcome or failure mode you need to address?
- Data quality: Are collection and definitions reliable and repeatable?
- Coverage and uncertainty: Which conditions are observed, which are missing, and how uncertain are the measurements?
- Performance: Does the proposed model outperform an appropriate baseline or benchmark on evidence suited to the task?
- Validation burden: Can your team explain and check the method well enough for its intended use?
- Operational risk: What could happen if a prediction triggers an action, and what review or escalation is needed?
These are practical comparison dimensions drawn from Wang’s discussion and NIST’s measurement guidance, not a formal NIST checklist. A simpler method can be the better choice when it is adequate and easier to validate.
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A baseline is not a one-time proof of success. Before deployment, test the system against suitable benchmarks and document the metrics, test data, processes, and uncertainty that support your assessment. After deployment, monitor performance regularly so you can detect errors, changing conditions, or emerging risks.
Best Value
NIST’s AI RMF Playbook discusses documenting measurement approaches, test sets, metrics, and processes, as well as instrumenting systems for tracking and regular monitoring under organizational governance. Its guidance is about AI risk management; it supports ongoing evaluation without turning every process improvement into a single mandatory sequence.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Automate only when the evidence and safeguards fit the decision
Allowing a model to trigger actions raises the stakes of measurement and validation errors. Before closing the loop, set acceptance criteria and decide which outputs can prompt automatic action, which require human review, and when to escalate an uncertain or out-of-range result. The right threshold and oversight depend on the workflow and the consequences of a wrong action.
Wang’s practical sequence is to measure relevant variables, choose and validate an appropriate model, then automate only when its outputs have been checked against observed conditions and operators trust them. Treat that as a useful manufacturing principle, not a universal rule that every AI project must use the same steps. NIST’s broader framework supports assessing and managing risks across development and operation; it does not mandate this exact sequence. NIST Director Laurie E. Locascio said the framework “can help companies and other organizations in any sector and any size to jump-start or enhance their AI risk management approaches,” in a January 26, 2023 NIST release.
Quick Recap
A practical decision path
- Define: Name the process boundary, outcome, and failure modes that matter.
- Measure: Select relevant variables and establish reliable, repeatable collection and definitions.
- Compare: Use a baseline or suitable benchmark to judge current performance and proposed methods.
- Choose: Select AI, a statistical method, a physics-based model, or no model according to evidence and validation needs.
- Test and monitor: Evaluate before deployment, document metrics and uncertainty, and keep checking performance in operation.
- Automate carefully: Set action criteria, human review, and escalation arrangements appropriate to the potential consequences.
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