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An AI pilot in a regulated organization proves feasibility only when it shows that a clearly defined system can do a clearly defined task on data the organization is allowed to use, meet agreed safety and quality thresholds under realistic conditions, and fit the organization’s security, governance, and human-review controls. A successful pilot is evidence for the next decision—not proof of legal compliance, production readiness, or suitability for every workflow or population.
What a feasibility test must establish
Feasibility is broader than whether a model produces plausible answers. Evaluate the whole proposed use: the task, data, people affected, operating environment, controls, and consequences of failure. The NIST AI Risk Management Framework (AI RMF) is a voluntary cross-sector framework for organizing risk work; its Generative AI Profile, released July 26, 2024, addresses distinctive generative-AI risks and actions. NIST’s current framework page says AI RMF 1.0 is being revised. Neither resource is a sector regulator’s certification or a blanket determination that a use is compliant.
- Task performance: Can the system perform the intended task, and how severe are its errors?
- Robustness: Does it hold up on edge cases and in the conditions where people would actually use it?
- Data and vendor boundary: Are data permissions, processing, access, retention, logging, subprocessors, and deletion understood?
- Impact and fairness: Could quality or error severity vary across relevant groups or situations?
- Security and governance: Are access, monitoring, incident response, audit evidence, and decision ownership defined?
- Human accountability: Can authorized people review, escalate, correct, or override output?
- Operational fit: Can the system be integrated, monitored, and withdrawn if risks or performance change?
NIST’s ARIA program describes three useful evaluation levels: model testing, red-teaming, and field testing. It is sector- and task-agnostic, so it offers an evaluation pattern rather than approval for a particular use.
A practical sequence for proving feasibility
1. Define the decision, not just the demo
Write down who will use the system, the specific task, the system boundary, the intended benefit, who may be affected, the current workflow baseline, and the decision the pilot is meant to inform. State whether the output is advisory, operational, or used to make or support a consequential decision. Distinguish assistance from autonomous action. A goal such as “see what AI can do” leaves no clear basis for interpreting results.
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2. Set the data and vendor boundary before providing data
Map data sources and fields, identifiers, permissions, retention, user and vendor access, model-provider processing, logging, subprocessors, and deletion. Decide whether synthetic, public, or de-identified data can answer the pilot question; those labels alone do not establish that all risks or obligations disappear.
For ePHI, first establish the parties’ HIPAA roles and whether an appropriate business associate agreement (BAA) is required. HHS says cloud services may create, receive, maintain, or transmit ePHI when the regulated parties comply with HIPAA. The organization still needs to understand the actual cloud environment and conduct its own risk analysis and risk management. A vendor’s marketing label or a contract by itself does not determine whether a specific use is appropriate. See HHS guidance on HIPAA and cloud computing.
For insurance, record the use, input data, third-party model and data dependencies, governance owner, mitigations, and evidence that may be needed in an examination. For legal work, define confidentiality limits, lawyer review and supervision, output verification, client communication and consent considerations, and billing treatment.
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3. Pre-register the evaluation
Choose measures and thresholds before reviewing results. Compare with the existing workflow using representative cases, edge cases, and defined failure categories. Measure average quality as well as serious-error rates, relevant subgroup or distributional differences, and whether the system appropriately abstains or escalates. Specify who reviews or labels outputs and how disagreements will be resolved.
Record the tested task, population, model and system version, configuration, data provenance, and operating conditions. A result does not automatically generalize beyond those tested conditions. The ARIA pattern—model testing, red-teaming, then field testing—can help separate basic capability from adversarial risks and real-world behavior.
4. Test controls as well as output quality
Exercise authentication, least-privilege access, data segregation, logging, retention and deletion, incident response, prompt and output handling, vendor access, and human escalation. For generative systems, probe hallucination, prompt injection, sensitive-data exposure, unauthorized tool actions, and brittle behavior when inputs differ from the expected pattern. Preserve test cases, outcomes, exceptions, approvals, and system configurations so reviewers can reconstruct what was evaluated.
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The NIST AI RMF and Generative AI Profile can help organize these risks, but they do not replace applicable law, sector rules, or the organization’s own decisions about acceptable residual risk.
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5. Set stop, redesign, and progression criteria in advance
Define what results support progression, require redesign, or stop the test. Treat unacceptable harm, privacy events, or security events as stop conditions—not merely as performance metrics. Assign owners to residual risks, mitigations, and monitoring. Progress in stages, such as retrospective or sandbox validation, controlled shadow use, limited human-reviewed use, and only then any broader deployment after required governance decisions.
What changes across healthcare, insurance, and legal work
| Sector | Feasibility boundary | Evidence to preserve | Human accountability |
|---|---|---|---|
| Healthcare | HIPAA roles, permitted handling of ePHI, cloud arrangement, safeguards, and the organization’s risk analysis. | Data-flow and role map; BAA where required; risk analysis and control evidence; validation set and observed errors. | Define appropriate clinician and operator review, escalation, and override responsibilities for the task. |
| Insurance | Applicable insurance laws, insurer governance, third-party data and models, consumer impact, and examination expectations. | Model and data inventory; governance approvals; mitigations; decision-support records; testing and monitoring evidence. | The insurer remains accountable for decisions or actions made or supported by AI. |
| Legal | Competence, confidentiality, communication, supervision, candor, meritorious advocacy, and reasonable fees. | Tool and vendor data practices; review protocol; factual and legal verification; applicable client communications and approvals. | Lawyers retain professional judgment and responsibility for AI-assisted work. |
The healthcare summary reflects HHS cloud guidance, not a complete HIPAA checklist. Insurance requirements depend on applicable law and state adoption of guidance. The legal summary reflects ABA guidance; governing jurisdiction and case facts matter. Sources: HHS, NAIC, and ABA Formal Opinion 512.
Healthcare: evaluate the actual ePHI arrangement
Whether a cloud service can handle ePHI depends on the regulated parties’ compliance and the specific arrangement; it is not settled by a vendor’s general assurance. Assess the real data path, responsibilities, safeguards, risk analysis, and policies before introducing ePHI. The HHS cloud guidance addresses HIPAA responsibilities for cloud services, but does not resolve every other applicable rule, contract, or clinical policy.
Insurance: keep governance and examination in view
The NAIC Model Bulletin on insurer use of AI was adopted in December 2023. NAIC describes insurer decisions and actions made or supported by AI as subject to applicable insurance laws and regulations, and notes that information may be requested in examinations or investigations. The bulletin is a model, not a substitute for checking applicable state requirements.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchAdoption survey findings are useful context, not proof of safe deployment. NAIC reported that 92% of 93 responding health insurers said they currently use, plan to use, or plan to explore AI/ML. The response category includes plans and exploration, not just active use, and the figure describes respondents rather than all health insurers.
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Legal work: verification and professional duties remain with lawyers
ABA Standing Committee on Ethics and Professional Responsibility Formal Opinion 512, dated July 29, 2024, identifies duties implicated by generative-AI use, including competence, confidentiality, communication, supervision, meritorious advocacy, candor toward tribunals, and reasonable fees. The opinion states: “To ensure clients are protected, lawyers using generative artificial intelligence tools must fully consider their applicable ethical obligations, including their duties to provide competent legal representation, to protect client information, to communicate with clients, to supervise their employees and agents, to advance only meritorious claims and contentions, to ensure candor toward the tribunal, and to charge reasonable fees.” It is ABA committee guidance, not a replacement for the rules and later guidance that govern a lawyer’s jurisdiction.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare AI options fairly
If two or more systems are candidates, run them against the same task and test set, with the same acceptance criteria. A strong score on one dimension does not compensate automatically for a weak control or unacceptable error. Record trade-offs across:
- Quality on the intended task and severity of errors.
- Robustness on edge cases and under realistic operating conditions.
- Data minimization, permitted use, retention, and vendor access.
- Security controls and evidence available for review.
- Bias, disparate impacts, and relevant affected-group performance.
- Explainability, traceability, and audit evidence.
- Human review, escalation, override, and accountability.
- Integration effort, operational resilience, cost, and reversibility.
This is a practical synthesis of NIST’s risk-management and evaluation approach and the responsibilities described by HHS, NAIC, and ABA; it is not a single regulator’s prescribed checklist.
What a favorable pilot does—and does not—show
A favorable result can justify moving to the next controlled stage if the system met pre-set thresholds, controls worked in the tested setting, and accountable owners accept documented residual risks. It does not establish compliance across all laws, production readiness, or reliable performance on a different population, task, model version, or operating condition. Keep the evidence bounded to what was actually tested and make any broader deployment a separate governance decision.
Other insurance survey results are also respondent findings
NAIC’s other reported figures illustrate why adoption percentages need their denominators and wording attached:
| Respondent group | Reported status | Survey result |
|---|---|---|
| Auto insurers | Currently use, plan to use, or plan to explore AI/ML; results reported December 2022. | 88% of 193 responding auto insurers. |
| Home insurers | Currently use, plan to use, or plan to explore AI/ML; results reported August 2023. | 70% of 194 responding home insurers. |
| Life companies | Currently use, plan to use, or plan to explore AI/ML; results reported December 2023. | 58% of 161 responding life companies. |
These are survey responses, not causal evidence or estimates of active deployment across each entire insurance sector. See the NAIC AI topic page.
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