| Current FPF A.15.8, A.2.2, E.23.CDI, C.38, and C.11 | Configuration inputs and recovery probes; holder-specific capability and development; same-result comparison; and separately governed choice. | Use these patterns to qualify configuration, comparison, and choice; generate organization-specific candidates here and obtain local authority and realization evidence from their owners. |
| Naikar et al., distributed and joint human–AI design | Distributed teams, artifacts, networked technologies, communication, adaptation, and self-organization replace a dyadic human-versus-machine frame. | Treat the conceptual synthesis and illustrative application as design input; qualify the local allocation Method and institutional authority separately. |
| Waterson et al., function allocation for responsible AI | Interdependence, joint operation, decision and responsibility points, outcomes, authority, dynamic trust, failure, and recovery extend static allocation. | The evidence comes from an early framework and small experiments. Select local responsibility predicates, legal rules, and organization-design Methods for the actual setting. |
| Vaccaro, Almaatouq, and Malone, human–AI meta-analysis | Human-only, AI-only, and combined comparison remains visible when synergy matters; task type and the best solo alternative can reverse the answer. | The evidence covers heterogeneous experiments through June 2023. Test local comparative performance and obtain authority, safety, and provider conditions separately. |
| NASA, Objective Function Allocation Method | Several human, automation, and robotic allocations can be compared through task and performance trade spaces rather than stereotypes. | Qualify transfer from the completed deep-space project to the local organization, provider, authority, and affected-person conditions. |
| Lagomarsino et al., adaptive task planning and dynamic role allocation | Communication, skill transfer, adaptive planning, control, feedback, and reallocation matter when robotic roles change during Work. | Robotics algorithms, safety thresholds, and execution-time control remain with direct engineering and Operations Methods. |
| ISO 6385:2016 and ISO 10218-1/-2:2025 | Human, social, technical, equipment, workspace, environment, skill, well-being, lifecycle, and current industrial-robot safety conditions belong in applicable comparisons. | Check which edition and requirements apply to the arrangement. Use the relevant requirements in the comparison, then qualify performers, capability, authority, and safe use for the actual arrangement. |
| NIST AI RMF 1.0 Core and human–AI interaction appendix | Explicit roles and oversight where applicable, third-party risk, deployment-representative evaluation, monitoring, incident response, recovery, override, and change management. | AI RMF 1.0 is voluntary and under revision in 2026. It supplies risk-management outcomes, not OCE authority, a universal human-in-the-loop rule, or provider adequacy. |
| Aksin and Masini, shared-service organization configurations | Shared-service configurations and their effectiveness are contingent rather than one universal best practice. | Qualify transfer from administrative and business services before choosing an internal, external, engineering, clinical, robotic, AI, or mixed arrangement. |
| Goth et al., shared-service administrative cost evidence | Objective cost-reduction evidence for administrative shared services is often weak or methodologically under-specified. | Require local evidence of cost gain, capability, provision, and arrangement adequacy before relying on the proposed shared service or outsourcing. |