Assess the operating problem before assessing the AI. A heavy-industry use case deserves a pilot only when you can state the decision or action that will change, measure the current baseline, show that reliable signals and records exist, estimate value against full operating cost and risk, and define how people will supervise and evaluate the system. If any of those links is missing, revise the idea or stop rather than treating a model demonstration as a business case.
The sequence below adapts NIST’s system-level condition-monitoring procedure for manufacturing and combines it with OECD due-diligence guidance for responsible AI. NIST’s procedure is illustrated with manufacturing cases, so other heavy-industry sectors require an additional check of their process hazards, laws, standards and operating context.
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Start with a decision, not a model
Write the use case in operational terms before discussing algorithms, vendors or sensors. Identify:
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- the decision or action that is currently difficult, late, expensive or inconsistent;
- the person or team responsible for acting;
- the event or outcome you want to change; and
- the time window in which a useful intervention is possible.
“Use AI to improve maintenance” is too broad. “Give the maintenance planner an advance indication of a pump condition that can be checked during the next planned window” identifies an asset, a user, a possible action and a timing requirement. The same discipline applies to quality inspection, energy management, safety monitoring, production scheduling and supply-chain decisions.
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List the existing workflow as it operates today. Include manual inspections, control-room alarms, work orders, laboratory results, operator knowledge, approval steps and shutdown rules. The pilot must be compared with that real baseline, not with an imaginary process in which no one notices problems.
Define the intended use
State what the system is allowed to do and what it is not allowed to do. An output might support a planner’s prioritization, recommend a test, flag an abnormal condition for human review or provide information for a process engineer. It should not silently become an automatic control action merely because an interface makes that possible.
Record the intended users, operating locations, shifts, languages, required response time, acceptable data delay and the consequences of ignoring or acting on an output. These requirements become the reference for pilot evaluation. NIST’s 2025 ARIA pilot report describes validity as the degree to which an application’s output meets the requirements for its intended use; the report concerns ARIA 0.1, not an industrial field trial. NIST’s report record quotes the principle directly: “NIST chose to focus on validity as the primary metric for the pilot, the degree to which application output met the requirements for the intended use.”
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Check whether the process is monitorable and actionable
Not every process benefits from monitoring or AI. NIST’s condition-monitoring procedure first asks whether a system or process is suitable for monitoring, whether monitoring can detect relevant problems and whether those risks can be mitigated. Use that as an early gate rather than assuming that more data will create value. NIST’s evaluation procedure explains the approach.
Test observability
For the proposed event or decision, identify the signals that would be available before, during and after it. Depending on the use case, these may include process measurements, equipment states, inspection findings, maintenance records, quality results, production context or energy readings. For each input, document its owner, sampling interval, time stamp, units, calibration status, retention period and access restrictions.
Then ask whether the outcome itself can be observed reliably. A model cannot be evaluated if failures are recorded inconsistently, labels are created only after a long delay or the “normal” operating range changes without being documented. Separate a genuine absence of events from an absence of records; both affect what a pilot can establish.
Test actionability
An accurate alert has no operational value if nobody can respond in time or if the recommended action conflicts with safety, production or maintenance constraints. Map each possible output to:
- the responsible role;
- the verification step the person performs;
- the action that may follow;
- the authority needed to take that action; and
- the record that proves what happened.
Include cases in which the user rejects an alert, cannot access the asset, is already handling a higher-priority event or receives contradictory information from an established instrument. A pilot should measure these workflow conditions, not just whether a score crossed a threshold.
Identify conditions that make a use case unsuitable
Pause the proposal when the target event has no dependable signal, when the response window is shorter than data collection and review, when no one owns the decision, or when the process cannot be safely changed on the basis of an uncertain output. A technology demonstration may still be useful for learning, but it should not be presented as a pilot with an operational success claim.
Establish the baseline and a value hypothesis
Before collecting new data, describe current performance and risk. For a condition-monitoring case, NIST calls for considering baseline risk without monitoring, the cost and risk of installing and operating the monitoring system, estimated value and a risk-based investment analysis. The NIST publication record describes this system-level procedure.
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Choose baseline measures
Use measures that represent the operational outcome, not only model behavior. Depending on the use case, a baseline may include:
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- frequency, duration and consequence of unplanned interruptions;
- planned-versus-unplanned maintenance work;
- scrap, rework or release delays;
- energy use for a defined production output;
- inspection findings and time to disposition;
- response time to an abnormal condition; or
- exposure to a defined safety hazard.
Specify the population, time period, production mix and exclusions used for each measure. A line with frequent changeovers should not be compared with a steady-state period without accounting for that difference. If the baseline is unstable, explain why and decide whether the pilot will stratify results by operating condition or first improve measurement.
Write the value hypothesis
Describe the causal path from output to outcome: “If the system identifies condition X early enough for role Y to perform action Z, outcome Q should change.” Identify the counterfactual—what would have happened without the system—and how it will be estimated. Possible designs include a comparison with a similar asset, a phased introduction, or a before-and-after analysis with documented operating changes. The design must fit the site; there is no universal industrial experiment pattern.
Value can include avoided downtime, reduced waste, fewer emergency interventions, improved throughput, lower energy consumption, better planning or reduced exposure to hazardous work. State which benefits are expected to recur and which are one-time learning benefits. Do not count a theoretical benefit unless the organization can connect it to an action, a measurable result and an owner.
Account for the complete operating burden
A model metric alone is not evidence of facility-level value. Estimate the full change to the operation before approving a pilot.
Installation and integration
- Required sensors, gateways, connectivity and physical installation;
- interfaces with historians, maintenance systems, laboratory systems, quality systems or control platforms;
- data engineering, time synchronization and storage;
- cybersecurity review, identity management and network segmentation;
- commissioning, calibration and validation; and
- training, documentation and support during shift operation.
Separate costs that occur once from recurring costs. Include engineering time and production interruptions needed to install or test equipment. If the pilot depends on a data feed that is not yet governed, treat that dependency as a readiness issue rather than assuming it will be solved during deployment.
Ongoing operation
Plan for model monitoring, data-quality checks, recalibration, software updates, incident handling, access reviews and user support. Assign each activity to a role and define the record that demonstrates it happened. A system that needs daily expert interpretation may have a different business case from one that can be reviewed during a weekly planning meeting, even if both produce similar predictive metrics.
Risks created by the system
Assess false positives, missed events, stale data, sensor failure, concept drift, outages, insecure interfaces and misleading explanations. For each failure mode, describe the immediate operational consequence and the safe fallback. Examples include reverting to an established inspection routine, suppressing an alert when data quality is below a limit, requiring a second person’s confirmation or preventing an automated action.
Include the cost of investigating incorrect alerts. Alert fatigue can reduce attention to existing alarms and create a safety risk even when the model’s aggregate accuracy appears strong. The pilot plan should therefore measure workload and trust as operational outcomes, while preserving an independent safety process.
Screen responsible-use and regulatory readiness
OECD guidance recommends understanding the AI system and use case, business purpose, data inputs, physical extensions, human-in-the-loop arrangements, deployment context, scientific validity and foreseeable improper use or misuse. It also calls for human oversight, consultation with relevant experts and affected stakeholders, and review of robustness, security, privacy and sector-specific requirements. Read the OECD due-diligence guidance.
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Data governance
Document where each dataset came from, who may use it, how long it is retained and whether it contains personal, commercially sensitive or security-sensitive information. Operator comments, badge-linked events, video and voice recordings can create privacy or employment implications even when the original proposal concerns equipment.
Check label quality and provenance. Record changes to instrumentation, maintenance practices, recipes, operating modes and data pipelines. A pilot that cannot reproduce the data used for an output cannot support a reliable incident review.
Human oversight and workforce effects
Consult operators, maintainers, engineers, safety specialists, information-security staff, data owners and affected worker representatives before deployment. Ask how the system changes workload, authority, required skills, accountability and escalation. Provide a way to challenge an output and a clear owner for resolving disputes.
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Security, robustness and misuse
Threat-model data feeds, model interfaces, remote access, update channels and physical devices. Consider deliberate manipulation, accidental misconfiguration and plausible misuse by an authorized user. Test behavior under missing, delayed, out-of-range and contradictory inputs. Define who can change thresholds, retrain a model or release a new version, and keep an audit trail.
Sector and jurisdiction requirements
Applicable obligations depend on country, sector, process and the system’s role. OECD guidance points to context-specific laws, regulations and standards, but the sources do not resolve the requirements for an unspecified project. Obtain a site-specific legal, safety and compliance review before a system can influence regulated operations or consequential decisions.
Define the pilot boundary and evidence plan
A pilot is an evidence-gathering exercise with a controlled scope, not a commitment to production deployment. Write a pilot charter containing:
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- Intended use: the users, assets, operating modes and decisions covered.
- Exclusions: conditions, assets, actions and data sources outside the test.
- Success measures: operational outcomes, user requirements, data-quality requirements and safety conditions.
- Evaluation design: baseline period, comparison method, observation window and treatment of confounding changes.
- Human process: who reviews outputs, how actions are recorded and how overrides work.
- Failure handling: alert suppression, fallback procedures, escalation contacts and stop conditions.
- Change control: how model, sensor, workflow and threshold changes are approved and documented.
- Exit decision: the evidence needed to expand, redesign, pause or end the pilot.
Set requirements before looking at results. Otherwise the team may quietly lower the standard after seeing an attractive dashboard. Include measures of validity for the intended use, operational effect, false-alert workload, missed-event handling, data availability, security incidents and user response. A pilot can fail constructively if it shows that the use case, data or workflow is unsuitable.
Use risk-based escalation
Apply a simple gate for every candidate and a deeper review for high-consequence cases. Escalate when an output can affect physical safety, environmental compliance, product release, critical infrastructure, employment decisions, security controls or irreversible process changes. Escalation may require additional domain experts, a formal hazard analysis, legal review, independent validation or a restricted shadow mode in which outputs are observed but cannot trigger action.
Compare several AI use cases consistently
When a site has more ideas than resources, score candidates against the same questions. The following axes synthesize NIST’s system-level risk-and-investment approach with OECD’s broader due diligence; they are a decision aid, not a validated universal scoring rubric. NIST’s procedure and the OECD guidance provide the underlying dimensions.
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| Comparison axis | Evidence to collect | Questions that can stop or defer a pilot |
|---|---|---|
| Operational value | Defined outcome, causal action, baseline and counterfactual | Is the expected improvement material, measurable and owned by an operating team? |
| Baseline and observability | Reliable event records, process context, labels and outcome data | Can the relevant event be detected and compared with what would otherwise occur? |
| Technical and data readiness | Input quality, time alignment, access, integration and operating-range coverage | Will missing, delayed or changing inputs make the output unusable? |
| Cost and ongoing burden | Installation, integration, operation, maintenance, review and training effort | Can the site sustain the work after the initial demonstration? |
| Risk and consequence | Severity and likelihood of incorrect or missed outputs, security, privacy and workforce effects | Are safeguards, fallback actions and required approvals defined? |
| Actionability and oversight | User role, response window, explanation, override and escalation path | Can an accountable person understand, challenge and safely act on the output? |
Use written evidence rather than enthusiasm as the basis for comparison. A lower-risk use case with a modest but observable benefit may be a better first pilot than a high-value idea whose outcome cannot be measured. Conversely, a technically easy project should not outrank a consequential project if its safeguards are inadequate.
Choose the application area without assuming a winner
The World Economic Forum’s manufacturing material identifies health and safety, quality, maintenance, production process, supply chains and energy management as application areas. The WEF material is a discovery checklist, not a ranking or a guarantee of value at a particular site.
For each area, translate the category into a specific decision. “Energy AI” could mean identifying abnormal consumption for an engineer to investigate; “quality AI” could mean prioritizing samples for laboratory review; “supply-chain AI” could mean highlighting a replenishment risk for a planner. The assessment still depends on local signals, actionability, cost and consequence.
Common assessment failures and corrective actions
Starting with a technology demonstration
Failure: A team selects a model or sensor first and searches for a problem afterward.
Correction: Require a one-page problem statement naming the process, decision, user, action, baseline and intended-use requirements before technical selection.
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Failure: A favorable precision, recall or error metric is treated as proof of savings.
Correction: Trace each output to a documented action and measure the resulting operational outcome, workload and risk.
Ignoring the existing control system
Failure: The pilot duplicates alarms, inspections or planning work without defining precedence.
Correction: Map the current workflow and specify when the AI output supplements, replaces or defers an existing step.
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Failure: A stream of technically valid alerts overwhelms the people expected to review them.
Correction: Set an alert-handling capacity, measure investigation time and define suppression, prioritization and escalation rules.
Leaving safety and security until deployment
Failure: Physical, cyber or regulatory review begins after a prototype is connected to operations.
Correction: Include safety, security, privacy and compliance owners in the initial screening, and escalate high-consequence uses before live testing.
Presenting a dated success statistic as a forecast
Failure: Broad implementation figures are used to predict the outcome of one plant’s pilot.
Correction: Treat the World Economic Forum’s 2023 guidebook statement that “only one in six has been successful to date” as a dated, broad manufacturing implementation claim—not a current rate, a heavy-industry-specific statistic or a forecast for your organization. See the WEF guidebook page.
Make the proceed, revise or stop decision
Proceed when the operational hypothesis is testable, the baseline and outcomes are observable, the intended users can act, costs and risks have owners, and the pilot charter defines success and failure conditions.
Revise when value appears plausible but the data, workflow, safeguards or evaluation design is incomplete. A revised proposal might narrow the asset population, move to shadow mode, improve record-keeping or change the output from an automatic recommendation to a human-reviewed flag.
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Stop when no credible action or outcome can be measured, the response window is incompatible with review, the consequences of error cannot be mitigated, or the organization cannot provide accountable oversight. Stopping is a valid result of a risk-based investment assessment; NIST’s method is not a blanket recommendation to adopt AI.
Implementation difficulty is real, but it should not be converted into a site-specific prediction. The WEF 2023 guidebook’s “one in six” statement is evidence that broad industrial implementation has been difficult, not a substitute for assessing your process, data and controls.
Frequently Asked Questions
Does this method apply outside manufacturing?
The NIST condition-monitoring procedure is manufacturing-oriented. Other heavy-industry sectors can use its problem, baseline, value and risk logic, but must add sector-specific process hazards, regulations, standards and operating constraints.
When should a proposed pilot receive deeper review?
Escalate when outputs could affect physical safety, environmental compliance, product release, critical infrastructure, employment, security controls or irreversible process changes. Add the appropriate safety, legal, security and domain experts before live operation.
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Not by default. Define the intended use first. For consequential decisions, begin with human review or shadow mode unless the organization has separately demonstrated safe automation, authority, fallback controls and required approvals.
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