Evaluating AI workflows across six industries

Compare AI adoption by the work and evidence involved before comparing a percentage. Document review, care-record preparation, maintenance lookup and dispatch planning have different consequences, permissions and reviewers.

This article provides a six-industry evaluation map. Its tasks are planning examples, not completed Tenten client cases or promised savings and adoption rates. Evidence used in a buying decision should identify the existing process, assessment scope, measurement method and actual costs.

Six industries: define what acceptance means

The map does not set universal thresholds. Each team should agree acceptance criteria for its task, users and consequences of error.

Finance: expose evidence and omissions

For an illustrative clause-comparison draft, each difference could link to an approved source and version. Missing material should be marked for the reviewer. Assess whether differences are correct, significant items are omitted and review time is manageable.

Measuring only draft-generation speed excludes correction and approval effort. A clickable citation also does not establish a correct conclusion or satisfaction of applicable requirements. Record use, content quality and approval responsibility separately.

Healthcare and manufacturing: use relevant cases

Healthcare administrative summaries can be assessed for missing information, conflicting records and unsupported inferences. Responsible professionals determine permitted use. Summary performance does not establish diagnostic capability.

Equipment-knowledge retrieval needs the applicable machine and document version, handling for obsolete instructions and escalation when evidence is missing. Inspection requires assessment on the actual products, lighting, line speed and defect types. Results from another line cannot substitute for this evaluation.

Use cases within approved data scope. Include test preparation, data organization and knowledge updates in costs without assuming a fixed project share.

Retail, logistics and automotive: assess exceptions

Reply drafts, dispatch plans and maintenance explanations all need exception handling. Define which results may be used, which require review and which return to the original process. Assign actual owners and account for failure or delay.

A logistics plan must fit current fleet and order constraints. Aftersales information must match the vehicle and version. Retail replies must reflect current product and service policies. Integrating into an existing interface can reduce interaction effort, but it cannot replace these content and execution checks.

Keep comparable outcome records

Record the existing and trial processes using fields such as:

• Accepted work: completion definition, data scope, cases and exception types.
• Quality: errors, omissions, unsupported content and human revisions.
• Workload: preparation, waiting, review, rework and takeover time.
• Use: eligible users, reasons for use and reasons for bypassing or stopping.
• Full cost: development, integration, models, data maintenance, review, monitoring and incident handling.

Agree the assessment period and expansion criteria according to task frequency and risk. Released work time is not automatically cash savings. Login counts do not establish accuracy, safety or business value.

GAO's June 30, 2021 AI accountability framework considers governance, data, performance and monitoring together and provides an organizational assessment reference. NIST's AI Risk Management Framework is voluntary lifecycle risk guidance, not vendor certification or an outcome guarantee.

Choose one task with available data, an owner and checkable results. Document the existing process and trial evidence, then use it to decide whether to continue, narrow or stop.