The Tool Desk
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What a feasibility proof needs to establish
Feasibility is a decision exercise with evidence behind it, not a general claim that “AI works.” Define what the system is intended to do, for whom, and under what conditions. Then test the complete use—not just model output—including data permissions, privacy, security, error severity, effects on people, human review, auditability, integration, and the ability to stop or reverse the workflow.
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Separate assistance from autonomous action. An AI tool that drafts a summary for a professional to check has a different risk profile from one that triggers an operational action or supports a consequential decision. Record whether the output is advisory, operational, or used to make or support a consequential decision, and identify who is accountable for acting on it.
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A practical proof-of-feasibility sequence
1. Define a narrow decision
Write a short decision brief before choosing test cases. It should name:
- The intended users, task, system boundary, and expected benefit.
- The people or groups affected, including who bears the consequences of an error.
- The existing workflow that will serve as a baseline.
- The decision the pilot is meant to inform, such as whether to redesign, advance to a controlled stage, or stop.
- The human role: who reviews, escalates, overrides, and remains responsible for the outcome.
“See what AI can do” is not a useful pilot objective: it gives no stable basis for interpreting results or deciding what follows.
2. Set the data and vendor boundary before using data
Map the data sources and fields, identifiers, permissions, retention, access, model or provider processing, logging, subprocessors, and deletion process. Decide whether synthetic, public, or de-identified data can answer the pilot question. Those labels do not automatically eliminate every privacy, security, contractual, or legal obligation.
For ePHI, establish the parties’ HIPAA roles and whether a business associate agreement (BAA) is required. HHS says cloud services can create, receive, maintain, or transmit ePHI when regulated parties comply with HIPAA; an appropriate BAA and the organization’s own understanding of the cloud environment, risk analysis, and risk-management policies are central. A vendor’s marketing label or a contract alone cannot determine whether a particular use is appropriate. See HHS guidance on HIPAA and cloud computing.
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For insurer use, record the use case, input data, third-party model and data dependencies, governance owner, risk mitigations, and what evidence may be needed for regulatory examination. NAIC says decisions or actions made or supported by AI remain subject to applicable insurance laws and regulations; its Model Bulletin on insurer use of AI was adopted in December 2023. See the NAIC artificial-intelligence topic page.
For legal work, set out confidentiality limits, the lawyer’s review and supervision duties, how outputs will be verified, client communication or consent considerations, and billing treatment. The ABA’s Formal Opinion 512 discusses professional obligations for lawyers using generative AI; it does not replace rules that govern in a particular jurisdiction. See ABA Formal Opinion 512.
3. Pre-register the evaluation
Choose measures and decision thresholds before reviewing results. Compare the AI-assisted workflow with the current baseline on representative cases, edge cases, and realistic operating conditions. Specify error categories and their severity; average quality alone can conceal a small number of unacceptable failures.
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NIST ARIA offers a useful evaluation pattern: model testing probes performance, red-teaming probes risks, and field testing examines operation in context. ARIA is sector- and task-agnostic, and supplies evaluation methods and metrics rather than sector-specific approval or regulator certification. See NIST ARIA.
4. Exercise controls as well as output quality
Test whether the system can be used safely inside the intended environment. Exercise authentication and least-privilege access, data segregation, logging, retention and deletion, incident response, vendor access, and human escalation. For generative systems, include probes for hallucination, prompt injection, sensitive-data exposure, unauthorized tool actions, and brittle behavior under distribution shifts.
Preserve the evidence needed to reproduce and review the test: system and model versions, configuration, data provenance, test cases, results, exceptions, and approvals. Use the NIST AI RMF and Generative AI Profile as cross-sector references for organizing those risk decisions, not as substitutes for applicable law or the organization’s own assessment.
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Before the pilot, state what results would support progression, require redesign, or stop the test. Treat unacceptable harm and privacy or security events as stop conditions, not merely as performance targets. Assign owners to residual risks, mitigations, and monitoring obligations.
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Advance in controlled stages: sandbox or retrospective validation, controlled shadow use, limited human-reviewed use, and broader deployment only after the required governance decisions. A favorable result at one stage is evidence for the next decision—not a pass for every population, workflow, or future model version.
How the feasibility boundary changes by sector
| Sector | Boundary to establish | Evidence to preserve | Human accountability |
|---|---|---|---|
| Healthcare | HIPAA roles, permitted ePHI use, cloud-provider arrangement, safeguards, and organization-specific risk analysis. | Data-flow and role map, BAA where required, risk analysis, control evidence, validation cases, and errors. | Define review, escalation, and override responsibilities appropriate to the task. |
| Insurance | Applicable insurance law, governance, third-party data and models, consumer impact, and examination expectations. | Model and data inventory, governance approvals, mitigations, decision-support records, testing, and monitoring. | The insurer remains accountable for decisions or actions made or supported by AI. |
| Legal | Professional duties concerning competence, confidentiality, communication, supervision, candor, meritorious advocacy, and reasonable fees. | Tool and vendor data practices, review protocol, factual and legal verification, and client communications or approvals as applicable. | Lawyers retain professional judgment and responsibility for AI-assisted work. |
The healthcare summary reflects HHS cloud guidance, not a complete HIPAA checklist. The insurance summary reflects NAIC materials; state law and adoption of guidance vary. The legal summary reflects ABA Formal Opinion 512; obligations depend on governing jurisdiction and facts. Read the relevant HHS, NAIC, and ABA materials for the applicable context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What insurer adoption figures do—and do not—show
NAIC survey results illustrate interest in AI/ML among responding insurer groups, but use, planned use, and exploration are combined in these figures. They are not evidence that a system is effective, compliant, or ready for deployment, and they should not be treated as population-wide adoption rates.
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| Respondent group | Reported use, plan, or exploration | Survey reporting date |
|---|---|---|
| 93 responding health insurers | 92% | May 2025 |
| 193 responding auto insurers | 88% | December 2022 |
| 194 responding home insurers | 70% | August 2023 |
| 161 responding life companies | 58% | December 2023 |
These are respondent survey findings reported by the NAIC; the percentages describe organizations that said they currently use, plan to use, or plan to explore AI/ML, not only active deployment.
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How to compare two AI options fairly
Run candidate systems against the same task, test set, operating assumptions, and decision rules. Compare the whole workflow, not just a model’s best-case output. A useful comparison records:
- Task quality and the severity of errors.
- Robustness on edge cases and in realistic field conditions.
- Data minimization, permitted use, retention, and vendor access.
- Security controls and the evidence available for review.
- Bias, disparate impacts, and affected-group performance where relevant.
- Explainability, traceability, and audit evidence.
- Human review, escalation, override, and accountability.
- Integration burden, operational resilience, cost, and reversibility.
This comparison framework synthesizes the risk-management and evaluation approaches in NIST AI RMF and ARIA with the sector responsibilities described by HHS, NAIC, and the ABA. It is a practical synthesis, not a checklist mandated verbatim by one authority.
What a successful pilot actually proves
A successful pilot supports only a bounded conclusion: under the conditions tested, a defined system may be suitable to advance to a specified next stage, subject to named controls and human responsibilities. It does not certify compliance, establish production readiness, or justify extrapolating results to an untested task, population, model version, or workflow. Keep the decision tied to the evidence, record unresolved risks, and require a fresh review when the system or its operating context changes.
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