Modular Legal Personhood for AI Use Cases: An Enterprise Systems Engineering Framework for Digital Transformation

1. Introduction

1.1. Governance Problem and Research Gap

Digital transformation increasingly makes artificial intelligence (AI) a constitutive element of organizational action because AI capabilities are embedded not only in technical systems but also in workflows, data infrastructures, decision processes, vendor relationships, and oversight arrangements [1,2]. Enterprise Systems Engineering (ESE) provides an appropriate perspective on this transformation because it treats the enterprise as a purposeful system in which people, technologies, processes, information, resources, governance arrangements, and external environments must be designed in relation to organizational objectives [3,4]. Accordingly, enterprise AI governance concerns the architecture through which AI-enabled activities are authorized, integrated, monitored, revised, and held to account, rather than the technical performance of models considered in isolation.

Existing governance approaches address important parts of this architecture, although they frequently operate at boundaries that are either too narrow or too broad. Ethical principles articulate general commitments such as transparency, fairness, privacy, responsibility, and human oversight, whereas technical documentation describes models, datasets, intended uses, performance conditions, and limitations. Risk-management frameworks, impact assessments, audits, and regulatory requirements add organizational processes for documentation, monitoring, review, incident response, and lifecycle control. Because these mechanisms do not necessarily identify the concrete organizational activity to which authority, resources, records, risks, and corrective powers belong, their coexistence does not by itself produce an integrated governance unit.

Sociotechnical scholarship cautions that AI governance becomes incomplete when technical abstractions are separated from the institutional settings in which systems acquire organizational meaning and effect [5,6]. Internal auditing research further shows that accountability depends on organizational processes capable of connecting design decisions, deployment conditions, evidence, and corrective action across the lifecycle [7]. These limitations support the need for a use-case-level boundary through which technical, organizational, and institutional requirements can be governed together.

Model-centered governance is too narrow when the effects of AI depend on the purpose pursued, the workflow in which outputs are used, the persons exercising authority, the data and infrastructure supporting the deployment, and the decisions that follow. Enterprise-wide governance is correspondingly too broad when common policies cannot distinguish among deployments with different purposes, affected populations, risk levels, technical dependencies, operating environments, and lifecycle conditions. The resulting boundary deficit makes it difficult to determine what is being governed, which enterprise participants are responsible for particular decisions, what evidence must be preserved, and when a material change requires reassessment or intervention.

The problem is therefore legal-organizational as well as technical because an AI-enabled activity becomes governable only when its operational boundary is associated with decision authority, adequate resources, reviewable records, risk allocation, lifecycle control, and accountability relationships. A governance architecture must preserve enterprise-wide coordination while differentiating individual deployments sufficiently to support context-specific control. This article responds to that problem by treating the AI use case as an intermediate enterprise-system boundary between the technical AI system and the enterprise as a whole.

1.2. Research Question and Principal Claim

The article addresses the following research question: How can an enterprise establish a use-case-level governance boundary that remains legally identifiable, operationally aligned, reviewable, adaptable, and accountable throughout an AI deployment’s lifecycle? This question concerns the institutional design of the deployment rather than the attribution of autonomous legal status to the AI system itself.

The article develops modular legal personhood as a design-theoretic response. Under this framework, a defined AI use case is associated with a differentiated legal-organizational unit whose governance can be configured according to the deployment’s purpose, operating conditions, risks, interfaces, and lifecycle requirements. Protected series within a Series Limited Liability Company (Series LLC) provide the primary legal prototype. A protected series is a legally differentiated unit established within a broader LLC structure, with governance, assets, obligations, and records that may be associated with that series under applicable law. Its contractual flexibility, differentiated asset structure, liability segregation, and record separateness make the use-case boundary institutionally concrete.

The principal claim is that modular legal personhood can translate an AI use case into a bounded enterprise subsystem when legal differentiation, contractual configuration, and operational practice remain aligned. Legal separation alone is insufficient because a nominal module that lacks corresponding personnel, resources, records, technical control, or corrective authority cannot perform the governance functions assigned to it. Consequently, the framework conditions modularization on operational correspondence, parent-organization responsibility, resource adequacy, accessible evidence, meaningful corrective powers, proportionality, and substance-over-form review.

1.3. Core Concepts and Scope

In this article, an enterprise is a mission-oriented organizational system rather than only a for-profit business firm, so the framework extends to public, private, nonprofit, scientific, infrastructural, and commercial organizations. The framework is particularly relevant where an AI product or service is deployed across organizational boundaries, because external clients, vendors, infrastructure providers, sectoral requirements, and contractual obligations increase the number of interfaces through which authority, evidence, risk, and corrective responsibility must be coordinated. Its scope is not limited to commercial deployment, however, because comparable governance problems arise when public, nonprofit, scientific, or infrastructural organizations rely on externally supplied AI capabilities or operate AI across differentiated internal units. An AI use case is the bounded socio-technical configuration through which AI capability is applied to a declared organizational purpose, including the relevant data and model dependencies, workflows, human roles, infrastructure, external relationships, decisions, resources, risks, records, oversight arrangements, and lifecycle controls. This boundary is broader than the technical model because it includes the organizational conditions that give the model practical effect, whereas it is narrower than the enterprise because it remains associated with a particular purpose and deployment.

Enterprise participants are the persons and entities that authorize, design, supply, finance, insure, operate, audit, oversee, or regulate an AI use case. Affected stakeholders are the persons or groups whose rights, interests, opportunities, or welfare may be materially affected by the deployment. The distinction matters because a person may be affected by a use case without participating in its design or operation, while governance must account both for the allocation of responsibility among enterprise participants and for consequences experienced by affected stakeholders.

The contractual architecture combines a common contractual core with one or more deployment-specific riders. The common contractual core is the stable body of terms that preserves the module’s institutional identity, baseline governance, relationship with the associated organization, recordkeeping obligations, amendment procedures, escalation structure, and conditions for suspension or termination. A deployment-specific rider is a functionally delimited contractual component that supplements or modifies the common core in response to a defined deployment condition, such as an applicable jurisdiction, sectoral requirement, risk classification, data or model dependency, vendor relationship, human-oversight arrangement, incident-response duty, or lifecycle change. A modular operating agreement is the configurational method through which the complete body of terms governing a protected series is composed from that stable core and the riders required by the particular deployment.

The resulting AI use-case module is a bounded legal-organizational and enterprise-system unit through which a specific AI use case can be authorized, resourced, monitored, reviewed, modified, suspended, or retired. The framework combines structural modularity, which differentiates the module as an institutional unit, with configurational modularity, which permits its governance requirements to be assembled and revised through identifiable contractual components. Because the broader proposition is functional rather than form-dependent, organizations may reproduce these functions through subsidiaries, special-purpose entities, protected cell companies, internal charters, dedicated accounting arrangements, regulated operating units, procurement structures, or board-approved governance plans when protected-series law is unavailable or disproportionate.

1.4. Research Approach

The scope of the article is deliberately design-theoretic because it develops and operationalizes a conceptual and architectural framework rather than testing an already validated causal model. The study synthesizes requirements derived from ESE, digital-transformation research, socio-technical systems engineering, AI governance, organizational design, accountability scholarship, and organizational law. Legal sources are used functionally to identify institutional capabilities that may support the use-case boundary rather than to provide a comprehensive doctrinal survey of protected-series law.

Protected series provide the primary legal prototype, although the broader proposition is functional rather than form-dependent. Where protected-series law is unavailable, inappropriate, or disproportionate, organizations may reproduce analogous governance functions through alternative legal or internal organizational arrangements. Empirical validation, sector-specific calibration, comparative effectiveness, and comprehensive doctrinal analysis remain tasks for subsequent research.

Framework construction proceeds through a sequence of analytical translations. The study first identifies the boundary deficit in prevailing governance arrangements and derives design requirements for identity, authority, interfaces, resources, risk capacity, evidence, corrective control, and lifecycle adaptation. It then examines protected-series and contractual features as a legal-organizational prototype, translates those features into ESE governance functions, organizes them within a layered AI use-case module architecture, and operationalizes the resulting framework through assessment dimensions and observable indicators.

The assessment framework is analytically demonstrated through an illustrative deployment scenario, although that application does not constitute empirical validation. Questions of inter-rater reliability, comparative effectiveness, predictive validity, sector-specific calibration, and implementation performance therefore remain part of the empirical research agenda developed later in the article.

1.5. Contributions and Article Structure

The article makes five related contributions. First, it identifies the AI use case as an intermediate systems boundary through which technical capability, organizational purpose, operational authority, affected stakeholders, resources, risks, evidence, and lifecycle change can be governed together. Second, it develops modular legal personhood as a legal-organizational substrate for institutionalizing that boundary without attributing independent moral or legal agency to the AI system. Third, it defines the modular operating agreement as a contractual configuration architecture that combines a stable common core with deployment-specific riders. Fourth, it translates this architecture into cumulative ESE governance functions and a layered AI use-case module. Fifth, it develops an assessment logic through which formal existence, operational correspondence, and governance performance can be distinguished and examined.

These contributions extend, while remaining distinct from, the authors’ prior and related work. The authors’ AI & Society article examined how business-entity structures can support liability protection, risk allocation, and AI service governance across jurisdictions [8], whereas the ISTAS-25 paper introduced modular legal personhood through protected-series design and deployment-specific contractual riders [9]. The present article instead constructs an ESE framework that connects the AI use-case boundary, legal-organizational differentiation, contractual configuration, layered governance functions, implementation indicators, and an empirical research agenda.

2. Literature Review and Conceptual Foundations

This section develops the conceptual foundations for treating the AI use case as an intermediate enterprise-system boundary. The literature identifies three connected problems: AI-enabled transformation distributes decision making across technical and organizational components, existing governance mechanisms do not necessarily converge on a common operational object, and a socio-technical boundary remains ineffective unless it acquires institutional capacity. The review therefore proceeds from ESE and digital transformation, through socio-technical AI governance and the boundary deficit in existing approaches, to modularity, organizational design, and legal personhood. It concludes by deriving the design requirements used to construct the framework in Section 3.

2.1. Enterprise Systems Engineering and Digital Transformation

AI-enabled digital transformation presents an enterprise-design problem because the introduction of AI can alter organizational processes, decision rights, information flows, capabilities, value creation, and relationships with external actors [1,2]. Digital technologies are also recombinable across organizational settings, which means that models, data services, platforms, and automated functions may migrate beyond the workflow or unit for which they were initially introduced [10]. Consequently, governance cannot be limited to the technical acquisition of an AI system because the deployment may reconfigure the enterprise through the activities, dependencies, and decisions that develop around it.

ESE responds to this problem by treating the enterprise as a purposeful and evolving system whose people, technologies, processes, information, resources, governance arrangements, and external relationships must remain aligned with mission-level objectives [3,11]. From this perspective, AI governance concerns the architecture through which an AI-enabled activity is authorized, integrated, supported, monitored, modified, and held to account. Because lifecycle integration, stakeholder concerns, configuration control, risk management, and organizational adaptation are already central ESE concerns, the field provides a basis for examining AI governance as an enterprise capability rather than as a model-level control [4].

The distributed structure of contemporary AI deployments nevertheless complicates enterprise-wide control because models, datasets, infrastructure providers, vendors, workflows, and oversight functions may remain operationally or managerially independent while contributing to the same organizational outcome. Such arrangements exhibit systems-of-systems characteristics when components develop under different authorities, incentives, contracts, locations, and timescales [12]. Although centralized policies may establish common expectations, they cannot by themselves ensure that authority, evidence, resources, and corrective capacity remain coordinated across distributed dependencies [13].

The literature therefore points to a need for governance that is simultaneously differentiated and coordinated. A viable architecture must isolate a sufficiently specific activity for control while preserving its interfaces with enterprise-level strategy, shared infrastructure, portfolio oversight, and external obligations. The resulting design problem is not whether the enterprise should govern AI centrally or locally, but how it can establish bounded governance units whose variation remains compatible with enterprise-wide direction.

2.2. Socio-Technical AI Governance

The technical model cannot serve as the sole object of governance because the meaning and consequences of AI arise from interactions among technical artifacts, organizational purposes, human practices, institutional conditions, and affected stakeholders. Socio-technical systems research has long shown that technical and social arrangements must be designed jointly, while studies of situated action and technology-in-practice demonstrate that organizational effects cannot be inferred from technical properties alone [14,15,16]. An AI deployment must therefore be understood through the activity in which the model is embedded rather than through the model as an isolated artifact [17,18].

This problem becomes particularly significant during problem formulation because the organizational purpose assigned to a deployment shapes its target variables, proxies, evaluation criteria, data requirements, and operational decisions [19]. When those choices are abstracted from their institutional setting, technical specifications may conceal the relationships, assumptions, and downstream consequences through which the system acquires practical effect. Sociotechnical scholarship accordingly cautions that AI governance becomes incomplete when technical abstractions are separated from the settings in which systems are designed, interpreted, and used [5].

Human involvement does not remove this difficulty because the effectiveness of oversight depends on how authority, information, workload, intervention points, and escalation duties are organized. A nominal statement that a human remains in the loop provides limited assurance when the responsible person lacks timely information, practical override authority, organizational support, or the ability to alter the workflow [20,21]. Governance must therefore specify not only whether human review exists, but also when it occurs, what evidence supports it, who may intervene, and what consequences follow from that intervention.

Organizational research on responsible AI reaches a related conclusion because ethical and assurance functions depend on reporting relationships, access to decision makers, cross-functional coordination, incentives, and the capacity to modify development or deployment practices [22,23]. Internal auditing research further demonstrates that accountability requires organizational processes capable of connecting design choices, deployment conditions, evidence, incidents, and corrective action across the lifecycle [7]. The appropriate object of governance is therefore the socio-technical activity through which enterprise participants combine AI capability with organizational action, rather than any technical component considered separately.

2.3. The Boundary Deficit in Existing AI Governance

Contemporary AI governance provides principles, documentation practices, audits, impact assessments, risk-management processes, and regulatory duties, although these mechanisms do not necessarily converge on a shared operational boundary. Ethics frameworks articulate commitments such as fairness, transparency, privacy, responsibility, and human oversight, while organizational methods seek to translate those commitments into procedures and tools [24,25,26,27]. The persistent problem is that general principles do not determine the particular activity, actors, resources, evidence, or corrective powers through which they must be implemented.

Technical documentation improves the visibility of models and datasets by recording intended uses, evaluation conditions, performance characteristics, composition, provenance, processing, and limitations [28,29,30]. These materials are indispensable for technical review, yet they do not independently establish who authorized the deployment, which workflow it affects, what resources sustain it, how external dependencies are governed, or which organizational unit must respond when conditions change. Model-centered governance is consequently too narrow whenever organizational effects arise from the interaction between technical components and their deployment context.

Enterprise-wide governance presents the opposite limitation because policies formulated for an organization as a whole may be unable to distinguish among deployments with different purposes, affected stakeholders, risk levels, technical dependencies, jurisdictions, and lifecycle conditions. Although a common policy can define baseline expectations, implementation still requires an identifiable activity to which decision rights, records, review duties, and escalation mechanisms can be attached. Enterprise-level commitments become operational only when they are translated into governance arrangements for a concrete use case.

Accountability, auditing, and impact-assessment scholarship make this requirement especially clear because each approach presupposes an object whose decisions and consequences can be reconstructed and evaluated. Accountability requires an actor to explain and justify conduct before a forum capable of assessment and response, while auditing requires evidence connecting organizational commitments to design, deployment, monitoring, incidents, and remediation [7,31,32,33]. Impact assessment likewise becomes consequential only when it is integrated into decision-making processes whose participants have access to relevant information and authority to modify, suspend, or reject the proposed activity [6].

Risk-management and regulatory frameworks reinforce the same institutional requirement. The NIST AI Risk Management Framework and ISO/IEC 23894 organize governance around lifecycle activities for identifying, assessing, monitoring, communicating, and treating AI risks [34,35]. The EU AI Act similarly connects obligations to intended purpose, risk classification, documentation, record keeping, human oversight, incident reporting, and post-market monitoring [36]. These requirements cannot be implemented coherently unless the organization can identify the deployment to which the relevant duties, responsible actors, resources, records, and corrective powers belong.

The resulting deficit is therefore a boundary deficit rather than an absence of governance mechanisms. Model-level controls lack sufficient organizational scope, whereas enterprise-wide controls lack sufficient deployment specificity. A use-case-level boundary responds to this mismatch by providing a common system of interest around which technical evidence, organizational authority, risk management, stakeholder effects, and lifecycle responsibility can be assembled.

2.4. Modularity, Organizational Design, and Legal Personhood

Identifying the AI use case as a socio-technical system of interest does not itself provide the institutional capacity needed to govern it. ESE can specify the boundary, functions, and interfaces of a system of interest, but those specifications do not by themselves create a persistent organizational unit capable of holding resources, allocating authority, maintaining records, or structuring external relationships. The resulting institutional gap is addressed through legal-organizational modularity, which makes the identified boundary institutionally recognizable and operable over time. This requirement connects the socio-technical and ESE literature to modular systems theory, organizational design, and organizational law.

Near-decomposability explains how complex systems can remain intelligible when relationships within modules are more stable and intensive than relationships across their boundaries [37]. Modular architecture further requires the disciplined allocation of functions and the specification of interfaces through which components can be combined, varied, or replaced without redesigning the system as a whole [38,39]. Because standardized interfaces and shared design rules can coordinate adaptation across technical and organizational structures, modularity provides a conceptual basis for differentiating AI use cases while preserving enterprise-level coordination [40,41].

The same logic applies to governance because AI deployments may share baseline requirements while differing in purpose, operating environment, risk, technical dependencies, external relationships, and lifecycle conditions. A monolithic governance instrument makes such variation difficult to isolate and revise, whereas entirely bespoke arrangements can fragment common standards and increase coordination costs. A modular governance architecture must therefore preserve a stable institutional identity while allowing defined components to change in response to deployment-specific conditions.

Organizational law contributes a complementary mechanism because legal personality and entity boundaries can associate assets, claims, obligations, decision rights, records, and external relationships with a differentiated organizational unit [42]. Asset-partitioning scholarship explains how legally recognized boundaries can structure relationships among an organization, its participants, creditors, contracting parties, and other stakeholders [43,44,45]. Legal personhood consequently has systems significance when it makes an operational boundary institutionally recognizable and connects responsibility with the resources and authority needed to exercise it.

This functional use of legal personhood differs from proposals to grant autonomous, moral, or electronic personality to AI systems. The relevant subject is not the model or machine considered as an independent rights-bearing actor, but the human and organizational arrangement through which a defined AI use case is authorized and operated. Although the literature on electronic and synthetic personhood reveals risks of control gaps and responsibility displacement, it also underscores the need for institutional forms that keep responsibility attached to the enterprise participants who design, deploy, oversee, and benefit from AI-enabled activity [46,47,48,49,50,51].

Protected series provide a useful prototype because they combine differentiation within a broader organizational structure with the possibility of separate governance, assets, obligations, and records. Their value at this stage of the argument is conceptual rather than doctrinal: they illustrate how structural modularity can give an AI use case an institutional boundary, while contractual modularity can configure governance within that boundary. The statutory requirements, legal limitations, and contractual construction of that prototype are examined in Section 3 rather than in the literature review.

2.5. Design Requirements Derived from the Literature

The literature first establishes that the governance architecture requires a determinate use-case boundary. That boundary must connect a declared purpose with the relevant workflow, technical dependencies, enterprise participants, affected stakeholders, decisions, and operating context because a boundary defined only by the model cannot capture the activity through which organizational effects arise. At the same time, the boundary must remain connected to enterprise strategy and portfolio oversight so that modularization does not become institutional isolation.

Second, governance must be configurable because deployments share some requirements while differing in material conditions. A stable baseline should preserve institutional identity, common authority rules, recordkeeping expectations, escalation relationships, and enterprise responsibility, whereas controlled variation should permit governance to respond to differences in risk, jurisdiction, technical dependency, external participation, and lifecycle status. Configurability must remain reviewable, which means that the applicable governance configuration and the reasons for changing it must be identifiable over time.

Third, the architecture must align authority and interfaces because responsibility becomes nominal when decision rights do not correspond to the technical and organizational dependencies that shape the deployment. It must also align resources with risk because a governance unit cannot monitor, investigate, correct, or suspend an AI use case without adequate personnel, information, technical access, financial support, and escalation capacity. Where risks or dependencies exceed the module’s authority or resources, the architecture must connect the issue to enterprise-level intervention rather than treating the boundary as a shield against broader responsibility.

Fourth, the architecture must make the use case observable and adaptable throughout its lifecycle. Reviewable evidence should connect authorization, design assumptions, technical changes, human interventions, incidents, stakeholder effects, audit findings, and corrective action, while configuration controls should identify when a change requires reassessment, amendment, suspension, or retirement. Because no formal boundary can guarantee substantive performance, implementation must also be assessed for correspondence between documented governance, operational practice, and actual governance capacity.

Taken together, the literature yields seven design requirements: determinate boundary identity, configurable governance, authority-interface alignment, resource-risk capacity, reviewable evidence and corrective capacity, lifecycle adaptation, and organizational anchoring. These requirements provide the criteria against which the framework is constructed rather than conclusions inferred from the legal prototype. Section 3 therefore begins by explaining the design-theoretic method through which these requirements are translated into a legal-organizational substrate, a modular contractual architecture, and a layered AI use-case module.

3. Research Design and Framework Construction

Section 2 derived seven requirements for a use-case-level governance architecture: determinate boundary identity, configurable governance, authority-interface alignment, resource-risk capacity, reviewable evidence, lifecycle adaptation, and organizational anchoring. This section explains how those requirements are translated into a legal-organizational and enterprise-system framework. The construction proceeds from the research method to the protected-series prototype, the modular operating-agreement architecture, the layered AI use-case module, and the analytical relationships among those components.

3.1. Design-Theoretic Research Method

The research problem cannot be addressed through doctrinal legal analysis or technical risk analysis alone because it concerns the design of an institutional arrangement that connects legal form, contractual governance, organizational practice, and enterprise architecture. The study therefore adopts a design-theoretic orientation in which the principal research output is a framework specifying the components, relationships, functions, and conditions required to govern an AI use case as an enterprise subsystem. The unit of analysis is not the AI model, the legal entity, or the enterprise considered separately, but the governance architecture through which a defined AI use case is authorized, operated, reviewed, modified, and terminated.

The source domains were selected according to the functions that the proposed architecture must perform. ESE and digital-transformation research identify requirements concerning enterprise purpose, system boundaries, interfaces, configuration, lifecycle integration, and organizational adaptation, while socio-technical and AI-governance scholarship establishes the need to connect technical components with human roles, institutional settings, evidence, oversight, and affected stakeholders. Organizational-design and modularity research explains how differentiated components can remain coordinated through stable interfaces, whereas organizational law and protected-series law provide mechanisms through which authority, assets, obligations, records, and external relationships may be associated with a legally differentiated unit. The study consequently integrates these domains functionally rather than attempting an exhaustive review of each field.

Framework construction follows an analytical translation procedure. First, the boundary deficit identified in Section 2.3 is converted into the seven design requirements stated above. Second, protected-series features are examined to determine which requirements they can support and which must instead be supplied through contractual or organizational arrangements. Third, those legal and contractual mechanisms are translated into an AI use-case module composed of constitutional, operational, accountability, and lifecycle layers. Fourth, the architecture is connected to governance functions and assessment dimensions so that its formal design can later be compared with operational implementation and observable performance.

Protected series were selected as the primary prototype because they make differentiated governance within a broader organizational structure especially visible, rather than because they are presumed to be universally available or superior to every alternative. The analysis evaluates the prototype by functional correspondence with the design requirements and by internal traceability among legal features, contractual mechanisms, architectural layers, governance functions, and assessment dimensions. At this stage, the framework is evaluated through analytical traceability, internal coherence, selective comparison with external reference points, and illustrative application rather than through predictive or comparative empirical validation. The resulting framework is therefore an analytically constructed design proposition rather than an empirically validated maturity model, and its comparative effectiveness, reliability in independent application, sector-specific calibration, and legal portability remain subjects for subsequent research.

3.2. Modular Legal Personhood as the Legal-Organizational Substrate

A socio-technical boundary does not become governable merely because analysts can describe it. Unless authority, resources, obligations, records, and external relationships are associated with an institutionally recognizable unit, the identified use case may remain dependent on dispersed arrangements that cannot be reviewed or corrected as a coherent whole. The first construction problem is therefore to provide the use-case boundary with sufficient legal and organizational substance without treating the AI system itself as an autonomous legal actor.

Modular legal personhood responds to this problem by using an organizational boundary to structure responsibility around the deployment. Legal personality and entity differentiation can associate assets, claims, decision rights, contractual relationships, and records with a specified organizational unit, while asset partitioning can distinguish that unit’s resources and obligations from those associated with other activities [42,43,44,45]. The framework uses these features instrumentally because the purpose of the legal boundary is to make human and organizational responsibility more specific, rather than to transfer responsibility to the AI system.

Protected series within a Series LLC provide the primary prototype because they permit differentiated organizational arrangements to operate within a continuing relationship with an associated LLC. LLC law generally allows substantial contractual specification of management authority, economic arrangements, information rights, internal procedures, and relationships among organizational participants, subject to mandatory legal limits [52,53]. Protected-series statutes add mechanisms for associating assets, obligations, records, management arrangements, and liability consequences with individual series, although the precise requirements and legal effects vary among jurisdictions [54,55]. These features make the protected series a useful institutional substrate for representing an AI use case as a bounded organizational module.

The legal boundary nevertheless supports governance only when it corresponds to the actual deployment. A protected series that exists formally but lacks control over relevant resources, access to operational records, authority over dependent systems, or the capacity to initiate corrective action does not satisfy the design requirements established in Section 2.5. Operational correspondence therefore requires the declared purpose, participating actors, assets, contracts, workflows, technical dependencies, records, and decision rights of the series to remain aligned with the AI-enabled activity attributed to it.

The associated LLC also remains part of the governance architecture because modularization cannot be allowed to fragment enterprise responsibility. Shared infrastructure, common vendors, enterprise policies, portfolio dependencies, financial support, and escalation authority may remain outside the individual series even when use-case governance is differentiated. Accordingly, modular legal personhood combines local differentiation with organizational anchoring: the protected series provides a specific governance boundary, while the associated LLC retains responsibilities for portfolio coordination, shared capabilities, resource support, escalation, and correction when a risk exceeds the authority or capacity of the module.

3.3. Modular Operating-Agreement Architecture

Legal differentiation alone cannot accommodate the variety of AI deployments because use cases operating within the same enterprise may differ in purpose, jurisdiction, sector, risk classification, technical dependency, vendor involvement, human oversight, and lifecycle status. A single undifferentiated agreement may preserve common governance at the cost of contextual fit, whereas separately drafted agreements for every deployment may produce inconsistency, duplication, and high amendment costs. The contractual design problem is therefore to preserve a stable organizational baseline while permitting controlled and reviewable variation.

The contractual architecture operates at two connected levels. The Series LLC-level operating agreement establishes the relationship between the associated LLC and its protected series, including portfolio oversight, shared infrastructure, common reporting expectations, resource relationships, and reserved enterprise powers. Within that structure, the series-specific operating agreement constitutes the complete body of terms governing an individual protected series, whether those terms appear in one document or in several documents that have been validly adopted and incorporated. This article uses “operating agreement” as a functional term because statutory terminology differs among jurisdictions [56,57].

Using the concepts defined in Section 1.3, the modular operating agreement composes the series-specific agreement from a common contractual core and the deployment-specific riders required by the use case. The common core preserves continuity in institutional identity, baseline authority, organizational relationships, recordkeeping, amendment, escalation, suspension, and termination, while riders introduce controlled variation for conditions that do not apply uniformly across deployments. Because each rider performs a defined governance function, it can be adopted, combined, amended, replaced, suspended, or retired without reconstructing the agreement as an undifferentiated whole. The resulting configuration therefore converts contractual supplementation into a form of governance configuration management.

The distinction between the common core and deployment-specific riders does not correspond strictly to a division between internal and external norms. Enterprise policies, ethical commitments, legal duties, sectoral standards, and contractual obligations may appear in either component depending on whether they apply generally or vary by deployment. The modular operating agreement instead provides a configuration architecture through which heterogeneous normative requirements are translated into a single reviewable governance arrangement for the use case. In this sense, contractual modularity performs a governance-configuration function: it preserves stable enterprise requirements while permitting controlled adaptation to jurisdiction, sector, client, vendor, technical, and lifecycle conditions.

The architecture requires explicit rules for rider selection, hierarchy, adoption, and change. The applicable configuration should be traceable to identified characteristics of the deployment, and the agreement should specify who may determine that a rider is required, which evidence supports the decision, how conflicts between the common core and a rider are resolved, and when a change triggers reassessment. Without these controls, apparent modularity could create uncertainty concerning which provisions govern the use case at a particular time.

Contractual configuration acquires institutional effect only when the documents, governing law, and operational practices correspond. The common core and applicable riders must be adopted through legally sufficient procedures, remain consistent with mandatory law, and be reflected in actual allocations of authority, resources, records, and responsibilities [52,53,54,55]. The consequence is a configurable governance architecture in which common enterprise requirements remain stable while deployment-specific variation becomes identifiable, reviewable, and capable of controlled revision.

3.4. AI Use-Case Module Architecture

The contractual configuration must be translated into an enterprise architecture because legal terms alone do not show how governance operates across organizational roles, technical dependencies, evidence, and lifecycle change. Figure 1 summarizes the resulting AI use-case module as an intermediate enterprise subsystem situated between the associated LLC, affected stakeholders, the external environment, and critical external dependencies. Within that subsystem, the architecture distinguishes four connected layers: constitutional, operational, accountability, and lifecycle. These layers do not represent separate organizations or sequential stages because they describe complementary aspects of the same governed activity.

The constitutional layer defines the module’s foundational identity and relationship with the enterprise. ESE requires system boundaries, stakeholder concerns, decision authority, lifecycle responsibilities, and mission-level relationships to be specified before operational components can be coordinated [3,4,11]. Accordingly, this layer specifies the authorized purpose, boundary, management structure, reserved enterprise powers, principal decision rights, resource commitments, escalation relationships, and conditions for suspension or termination. The common core is the principal contractual mechanism for this layer because these provisions must remain sufficiently stable to preserve the module’s identity when particular technical or regulatory conditions change. The consequence is a determinate institutional boundary against which unauthorized expansion, repurposing, and governance drift can be identified.

The operational layer connects that institutional boundary to the components and practices through which the use case functions. Socio-technical research shows that system effects depend on the interaction of technical artifacts, workflows, organizational roles, and situated practices, while systems architecture emphasizes the interfaces through which dependencies and changes propagate [15,16,17,35]. Human-automation research further demonstrates that oversight is meaningful only when reviewers possess adequate information, intervention opportunities, practical authority, and organizational support [20,21]. This layer therefore addresses data and model dependencies, workflows, infrastructure, vendors, access controls, human roles, review points, intervention authority, and material technical interfaces. Deployment-specific riders are particularly important at this layer because operational requirements may differ substantially among use cases even when the modules share the same constitutional baseline. The resulting configuration aligns decision authority with the interfaces through which performance, dependency, and risk can propagate.

The accountability layer makes the module reviewable by connecting governance obligations with evidence and corrective capacity. Model cards, datasheets, and related documentation practices provide evidence about models, datasets, intended uses, and lifecycle conditions, although accountability also requires records of organizational decisions, reviews, incidents, and responses [28,29,30]. Auditing and accountability scholarship accordingly emphasizes the preservation of evidence through which authorizations, design choices, deployment conditions, interventions, findings, and remediation can be reconstructed and evaluated [7,31,32,33]. This layer therefore includes recordkeeping, documentation, audit access, impact-assessment results, approvals, interventions, incidents, complaints, affected-stakeholder information, review findings, escalation records, and remediation decisions. Although record separateness can improve observability, it must not prevent the associated LLC, regulators, auditors, or other authorized reviewers from reconstructing the relationship between module-level decisions and enterprise-level responsibility. The consequence is an evidentiary structure through which formal governance claims can be compared with operational conduct.

The lifecycle layer governs change because a use case that was adequately configured at authorization may cease to correspond to its operating environment. Systems engineering and modular-design research treat configuration control, interface stability, lifecycle integration, and controlled component replacement as necessary conditions for maintaining coherence in evolving systems [4,37,39,40,41]. AI risk-management and regulatory frameworks similarly require continuing monitoring, reassessment, incident response, and post-deployment control when models, data, intended purposes, risks, or operating conditions change [34,35,36]. This layer therefore specifies material-change triggers, periodic review, revalidation, amendment, rider replacement, resource reassessment, suspension, retirement, record retention, and portfolio coordination. The common core preserves continuity in change authority and escalation, while riders permit the applicable configuration to evolve when models, data sources, vendors, workflows, legal classifications, or stakeholder effects change. The consequence is a lifecycle mechanism that permits adaptation without allowing change to become informal or unreviewable drift.

The four layers are interdependent because weakness in one layer can undermine the others. A clear constitutional boundary has limited effect when the operational deployment exceeds it, while detailed operational controls cannot establish accountability when evidence is fragmented or corrective authority is absent. Similarly, an adequate initial configuration may deteriorate unless lifecycle mechanisms detect and govern material change. This interdependence reflects the broader ESE requirement that system boundaries, interfaces, resources, evidence, and lifecycle controls be coordinated rather than optimized independently [3,4,11]. The module becomes governable only when the four layers remain mutually consistent and connected to enterprise-level support and intervention.

3.5. Analytical Traceability of the Framework

A framework that combines legal, contractual, organizational, and technical elements risks becoming a descriptive collection of mechanisms unless the relationships among those elements are explicit. Analytical traceability addresses this problem by connecting each design requirement derived in Section 2.5 to a legal-organizational response, a contractual or architectural realization, and an expected governance consequence. The mapping synthesizes the ESE and systems-architecture requirements concerning boundaries, interfaces, resources, configuration, and lifecycle control [3,4,11,35]; the modularity literature concerning decomposition, functional allocation, interfaces, and coordinated adaptation [37,38,39,40,41]; the accountability and documentation literature concerning reviewable evidence and corrective processes [7,28,29,30,31,32,33]; and organizational-law scholarship concerning legal personality and asset partitioning [42,43,44,45]. Table 1 presents the resulting construction.

The table represents a construction logic rather than an empirical finding. Its rows should therefore be read as traceable design propositions: the cited literature supports the underlying requirements, while this study proposes the particular correspondence among protected-series features, contractual mechanisms, architectural layers, and governance consequences. Protected-series features do not automatically produce the stated consequences because the legal form must be implemented through valid contractual arrangements, operational correspondence, adequate resources, accessible evidence, and effective enterprise oversight. The framework therefore distinguishes the existence of a formal mechanism from its correspondence with the deployment and from its performance in practice.

This distinction also explains the allocation of later sections. Section 4 examines the mechanisms through which the constructed architecture is expected to perform boundary, configuration, authority, resource-risk, accountability, and lifecycle functions. Section 5 then examines whether those mechanisms exist formally, correspond to operational reality, and demonstrate adequate governance performance. The construction developed here consequently provides an analytically traceable proposition that can be implemented, assessed, compared, and revised without being presented as an already validated universal model.

4. Governance Functions

Section 3 constructed the AI use-case module as a legal-organizational and enterprise-system architecture. The present section examines how that architecture performs governance functions rather than restating its components. For this functional analysis, the composite requirement of reviewable evidence and corrective capacity is separated into evidentiary continuity and corrective continuity; organizational anchoring operates as a cross-cutting condition across the functions; and lifecycle adaptation is expressed as lifecycle and portfolio coordination because material changes may propagate through shared enterprise dependencies. The analysis therefore distinguishes seven connected functions: boundary maintenance, configuration control, authority-interface alignment, resource-risk capacity, evidentiary continuity, corrective continuity, and lifecycle and portfolio coordination. The functions are discussed in related groups before Section 4.5 synthesizes their cumulative dependencies.

The seven governance functions do not reproduce the seven design requirements on a one-to-one basis. Organizational anchoring operates as a cross-cutting condition across the functions, while reviewable evidence is divided into evidentiary continuity and corrective continuity because evidence supports governance only when findings can lead to action. Lifecycle adaptation is correspondingly extended to portfolio coordination where changes affect shared enterprise dependencies.

4.1. Boundary Definition and Configurational Governance

The first governance problem is a mismatch between the boundaries used by existing AI controls and the organizational activity that produces the relevant effects. Model-level governance is too narrow because the consequences of AI depend on purpose, workflow, data, infrastructure, human authority, and downstream decisions, whereas enterprise-level governance is too broad to distinguish among deployments with materially different risks, dependencies, stakeholders, and operating conditions. This mismatch permits model reuse, workflow expansion, vendor change, or repurposing to alter the governed activity without formal recognition that the use case itself has changed [5,6,19].

The architecture constructed in Section 3 responds by making the authorized use-case boundary an explicit and reviewable object of governance. Rather than treating the model inventory or enterprise policy as the operative boundary, the framework requires the deployment’s declared purpose and operational configuration to remain identifiable over time. The relevant governance function is therefore boundary maintenance: the organization must be able to determine whether the activity being operated still corresponds to the activity that was authorized.

Boundary maintenance also requires configurational governance because the conditions governing a use case may change without eliminating the identity of the use case itself. The framework addresses this distinction by separating the stable elements that preserve institutional continuity from the variable elements that respond to deployment-specific conditions, as developed in Section 3.3. The governance question is consequently not whether variation exists, but whether the applicable configuration, the reasons for its selection, and the authority for its amendment remain traceable.

The consequence is that use drift and configuration mismatch become reviewable rather than remaining informal organizational developments. Reviewers can compare the authorized boundary with the current deployment, determine whether changes are material, and require reassessment, amendment, escalation, or suspension when correspondence has been lost. Enterprise consistency and deployment-specific adaptation can therefore coexist, provided that boundary changes and configuration changes remain subject to explicit governance control.

4.2. Authority, Interfaces, and Resource-Risk Alignment

The second governance problem is a mismatch between assigned responsibility and the authority and capacity required to exercise it. An AI use case may depend on developers, data providers, infrastructure operators, vendors, professional users, compliance functions, and downstream decision makers that remain subject to different managerial and contractual controls. Responsibility becomes nominal when the actor expected to govern the use case cannot obtain necessary information, control a relevant interface, alter a dependent component, or require action from another participant. The same problem arises when formal authority exists but is unsupported by sufficient personnel, expertise, technical access, financial resources, or remediation capacity.

Distributed authority makes this mismatch especially difficult to detect because no single participant may control the complete chain through which an AI-enabled decision is produced. Systems-of-systems research shows that operationally and managerially independent components can contribute to a common outcome while continuing to evolve under different authorities and timescales [12,13]. Systems architecture and socio-technical systems engineering similarly emphasize that control depends on the relationships and interfaces among technical components, organizational roles, and external actors rather than on the formal assignment of responsibility alone [17,18,35]. The relevant governance question is therefore whether authority covers the interfaces through which the use case operates and through which risk can propagate.

The architecture constructed in Section 3 responds by making authority-interface correspondence a reviewable governance condition. Rather than repeating the allocation mechanisms developed in Section 3.4 and Section 3.5, the present analysis asks whether each material dependency is matched by an actor who can obtain information, make or require a decision, and initiate intervention. Where an interface remains controlled by a vendor, shared enterprise function, infrastructure provider, or another module, governance must identify the contractual or organizational path through which the responsible actor can obtain cooperation or escalate the matter. An ungoverned interface exists when responsibility has been assigned but no actor possesses an effective means of controlling, influencing, or escalating the relevant dependency.

Human oversight provides a particularly important test of authority-interface correspondence. A person designated to review an AI output cannot exercise meaningful oversight when relevant information is unavailable, intervention occurs too late, workloads make review impracticable, or the reviewer lacks authority to override, suspend, or escalate the resulting decision [20,21]. The functional inquiry therefore concerns whether oversight can alter the course of the use case, rather than whether a human role appears in the formal governance arrangement. This distinction makes it possible to identify oversight that is formally present but operationally ineffective.

Authority must also correspond to resource capacity because control cannot be exercised without the means required to investigate, monitor, intervene, and remediate. Organizational research on responsible AI shows that governance functions depend on expertise, cross-functional access, institutional support, reporting relationships, and the ability to influence development and deployment decisions [22,23]. Risk-management frameworks similarly presuppose sufficient information, personnel, technical access, monitoring capability, and response resources throughout the lifecycle [34,35]. The relevant question is therefore not whether resources have been allocated in the abstract, but whether available capacity is proportionate to the risks and dependencies associated with the particular use case.

The framework responds to a capacity deficit by requiring escalation when the module cannot govern a risk through its own authority and resources. Escalation is not an exception to modular governance because the use-case boundary is intended to identify the level at which a problem becomes visible, not to require every problem to be resolved locally. When a risk depends on shared infrastructure, enterprise-wide policy, portfolio-level resources, or powers reserved outside the module, responsibility must move through the organizational relationships established in Section 3.2. The enterprise consequently remains responsible for supplying support or exercising intervention where the module’s governance capacity is insufficient.

The principal consequence is that responsibility can be evaluated as an operational capability rather than inferred from titles, contractual language, or organizational charts. Reviewers can identify whether each material interface is covered by effective authority, whether assigned actors possess resources proportionate to their responsibilities, and whether unresolved gaps are connected to a credible escalation path. Authority-interface alignment and resource-risk alignment therefore convert formal responsibility into actionable governance, while also revealing where the use-case module depends on enterprise-level support.

4.3. Observability, Accountability, and Corrective Control

The third governance problem is that evidence concerning an AI use case is often fragmented across technical systems, organizational units, contractual relationships, and external providers. Model documentation may describe intended uses and performance conditions, operational records may show how outputs entered a workflow, contractual files may identify vendor obligations, and audit or incident records may document later review. When these materials cannot be connected, however, reviewers cannot reconstruct who made a decision, which information was available, whether the authorized configuration was followed, or how the organization responded after a problem became visible [7,28,29,30]. The existence of documentation therefore does not by itself make the use case observable.

The functional problem is one of evidentiary continuity rather than record volume. Accountability requires a reviewable sequence connecting authorization, design assumptions, deployment conditions, human interventions, material changes, incidents, findings, and responses. Accountability scholarship accordingly treats explanation and justification as institutional relationships in which an actor must provide an account to a forum capable of evaluating it, while auditing research emphasizes evidence that links organizational commitments to decisions and conduct across the lifecycle [31,32,33,58]. The relevant question is therefore whether the available evidence permits an independent reviewer to reconstruct the governance history of the use case.

The architecture developed in Section 3 responds by making evidentiary continuity a governance condition rather than merely a recordkeeping obligation. Without repeating the contents of the accountability layer specified in Section 3.4, the present analysis asks whether records generated by different participants can be associated with the same use case, ordered over time, and connected to the decisions they support. Observability exists when a reviewer can move from a formal requirement to the evidence of its implementation, identify deviations, and trace the organizational response. An evidentiary gap exists when a material decision or event cannot be connected to an identifiable actor, applicable governance requirement, supporting information, or subsequent action.

Record concentration must not become informational isolation. Although organizing evidence around the use case can reduce fragmentation, accountability is weakened when the module’s records exclude enterprise decisions, shared infrastructure changes, vendor actions, or dependencies involving other modules. The functional requirement is therefore accessible traceability across organizational boundaries, including the ability of the associated LLC, authorized auditors, regulators, and other competent forums to examine how module-level conduct was shaped by enterprise-level arrangements. Observability must reveal responsibility relationships rather than reproduce the formal limits of the legal boundary.

The same evidentiary continuity that makes a use case reviewable also addresses a second governance failure: review that cannot alter the governed activity. Once evidence connects requirements, decisions, deviations, and consequences, it becomes possible to determine not only what occurred but also whether an identified actor possesses the authority to respond. Impact assessments, audits, complaints procedures, and incident reviews have limited effect when the reviewing body can describe a failure but cannot obtain additional evidence, impose conditions, require remediation, suspend operation, or escalate the matter to an actor with sufficient authority [6,7,32]. The framework therefore extends evidentiary continuity into corrective continuity by requiring each substantiated finding to connect to an identifiable decision path through which proportionate action can be initiated.

The principal consequence is that accountability becomes a closed governance loop rather than a retrospective collection of documents. Evidentiary continuity makes the use case observable, institutional review converts the reconstructed account into an evaluative judgment, and corrective continuity connects that judgment to amendment, remediation, escalation, suspension, or retirement. Reviewers can therefore assess not only whether records exist, but whether the organization can reconstruct events, attribute decisions, evaluate compliance with the applicable configuration, and act when divergence is identified. Observability and corrective control together convert documentation into actionable accountability.

4.4. Lifecycle Adaptation and Portfolio Coordination

The fourth governance problem is that an authorized configuration can lose correspondence with the deployment over time, while the same changes can also propagate beyond the individual use-case boundary through shared enterprise dependencies. Model updates, data shifts, vendor modifications, workflow expansion, changes in affected stakeholders, and revised legal or risk classifications may alter the use case even when its formal authorization remains unchanged. Where models, datasets, infrastructure, vendors, personnel, or decision processes are shared, a change or failure within one module may also affect other use cases or the enterprise as a whole. Lifecycle governance must therefore address both temporal drift within the module and cross-boundary effects arising from interconnected dependencies [12,13,34,35,36].

The architecture constructed in Section 3 responds by treating continuing correspondence as a governance function rather than presuming that an initially adequate configuration will remain adequate. Without repeating the lifecycle mechanisms specified in Section 3.4, the functional inquiry asks whether material changes are detected, whether their implications are assessed against the authorized use case, and whether the governance arrangement is revised before operational practice diverges from formal control. A lifecycle failure exists when the deployment changes materially but the organization continues to rely on an authorization, risk assessment, authority allocation, or oversight arrangement that no longer reflects current conditions.

Effective adaptation requires change control to connect reassessment with implementation because amendment of a document does not by itself restore correspondence. Systems engineering and modular-design research emphasize configuration control, interface management, controlled replacement, and verification when evolving components may affect system coherence [4,39,40,41]. The relevant governance process must therefore connect the identified change with the evidence considered, the decision reached, the operational measures adopted, and verification that authority, resources, technical controls, vendor obligations, monitoring, and records now reflect the revised configuration. The consequence is controlled adaptation in which organizational practice changes together with the formal governance arrangement.

The same change-identification process that preserves module-level correspondence also provides the trigger for portfolio coordination when a change affects shared dependencies or creates consequences outside the module. Systems-of-systems research shows that locally managed components may produce broader effects when they remain operationally interconnected and evolve under different authorities [12,13]. The functional question is therefore whether findings from one use case are communicated to an enterprise actor capable of identifying affected modules, evaluating common dependencies, coordinating shared responses, and exercising authority that lies beyond the individual module. Portfolio coordination is required when a change cannot be understood or contained solely within the boundary in which it was first detected.

The principal consequence is that lifecycle governance preserves modular differentiation without allowing change to produce either silent drift or displaced risk. At the module level, reviewers can determine whether the current deployment still corresponds to its authorized configuration and whether material changes have been implemented through a traceable decision process. At the enterprise level, they can determine whether shared dependencies and cross-module effects have triggered appropriate coordination, support, or intervention. Lifecycle adaptation and portfolio coordination therefore convert change from an informal operational development into a reviewable governance process that maintains both use-case integrity and enterprise coherence.

4.5. Governance Synthesis

The final governance problem is that the functions examined in this section may be treated as independent checklist items. Under such an interpretation, an organization might regard extensive records as sufficient despite an indeterminate use-case boundary, or rely on formally assigned responsibility even when the designated actors cannot control material interfaces. The synthesis problem is therefore to determine how the separate functions operate as a connected governance process rather than as an accumulation of controls.

The framework responds by treating the functions developed in Section 4.1 to Section 4.4 as cumulative and mutually constraining. Figure 2 summarizes the seven functions by distinguishing their substantive meaning, primary operational roles, and logical dependencies. This distinction clarifies both what governance condition each function addresses and how that function contributes to the integrated governance process.

The dependency represented in Figure 2 is logical rather than strictly temporal. Several functions may operate concurrently or require repeated reassessment, although later functions remain constrained when the conditions established by earlier functions are unresolved. The integrated view therefore prevents any individual mechanism from being treated as sufficient evidence of effective governance.

5. Implementation and Assessment

A conceptually complete governance architecture may remain ineffective when it is adopted only as a formal package, implemented without regard to deployment maturity, or assessed through evidence that does not reveal its operation in practice. This section addresses five connected problems in implementing and assessing the framework: proportional implementation, operational correspondence, operationalization, analytical demonstration, and interpretation and validation. The corresponding subsections address these problems through staged institutionalization, evidence-based assessment, observable dimensions and indicators, an illustrative application, and a bounded validation strategy. Together, these elements examine whether the governance functions developed in Section 4 are proportionately implemented, operationally aligned, observable, and capable of empirical examination.

5.1. Proportional Implementation through Staged Institutionalization

The proportional implementation problem arises because the level of institutional commitment may not correspond to the maturity, risk, and operational significance of the use case. Immediate adoption of the complete legal-organizational architecture may impose unnecessary cost on an exploratory or low-consequence deployment, whereas delayed institutionalization may allow a consequential use case to expand without a stable boundary, effective authority, or reviewable evidence. The implementation question is therefore not whether every AI use case should immediately receive the same organizational form, but when the evidence and risk associated with a deployment justify increasing levels of formalization.

Staged institutionalization is organized through four readiness gates, each of which requires specified evidence and an authorized decision on whether the use case may proceed, proceed subject to conditions, return for revision, pause, escalate, or terminate. The four gates are the boundary-readiness gate, configuration-readiness gate, operational-readiness gate, and continuing-assurance gate. Unlike the first three gates, which ordinarily precede or structure activation, the continuing-assurance gate recurs throughout operation and may return the use case to an earlier gate when material change, incident, or evidentiary deficiency undermines the existing governance arrangement.

The boundary-readiness gate asks whether the purpose, scope, participants, affected stakeholders, principal dependencies, intended decisions, and initial risk conditions are sufficiently determinate to create a governable system of interest. A use case that cannot pass this gate should remain subject to clarification or limited experimentation because later controls cannot compensate for an indeterminate object of governance. Passing the gate authorizes further configuration without predetermining that a protected series or any other particular legal form must immediately be adopted.

The configuration-readiness gate and operational-readiness gate reviews convert the accepted boundary into a functioning governance arrangement. Configuration readiness asks whether current deployment conditions are covered by applicable governance provisions and whether material interfaces are connected to authority, resources, evidence, and escalation paths. Operational readiness then asks whether responsible actors can obtain necessary information, exercise intervention powers, preserve required records, and initiate escalation before activation. These inquiries reflect systems-engineering requirements for lifecycle integration and configuration control, as well as AI risk-management requirements for governance, monitoring, and response capacity [4,34,35].

The continuing-assurance gate applies recurrently after activation rather than operating as a one-time approval point. Material changes, incidents, repeated overrides, new stakeholder effects, evidence gaps, or altered dependencies trigger reassessment of whether the existing boundary, configuration, authority, resources, and evidentiary arrangements remain adequate. Figure 3 summarizes the four gates and shows that each applies the lenses of existence, correspondence, and performance. The consequence is an implementation path in which governance intensity can increase, decrease, or be reconfigured as the deployment’s maturity, consequences, dependencies, and reversibility change.

5.2. Operational Correspondence and Assessment Evidence

The operational correspondence problem arises because passage through a readiness gate does not show that the approved mechanisms continue to reflect the deployment or influence organizational conduct. Document counts cannot establish whether the authorized boundary remains current, whether designated actors can control material interfaces, whether resources are sufficient, or whether findings lead to correction. Assessment must therefore examine the functional chain developed in Section 4 rather than treat the existence of individual mechanisms as evidence of overall effectiveness.

Building on the correspondence distinction established in Section 3.5, the framework applies three assessment lenses. Existence asks whether the relevant legal, contractual, organizational, and technical mechanism has been formally established. Correspondence asks whether that mechanism reflects the use case as currently operated, including its purpose, dependencies, participants, risks, and decision processes. Performance asks whether evidence from a specified period shows that the mechanism has supported detection, review, intervention, escalation, or correction when required. The three lenses prevent formal adoption from serving as a proxy for governance performance.

Evidence triangulation addresses the possibility that any single source may present an incomplete or self-serving account of implementation. Documentary evidence may include agreements, authorizations, policies, risk assessments, meeting records, and vendor obligations, while system-generated evidence may include logs, access records, change records, alerts, overrides, and incident data. Interviews, observations, audit findings, complaints, and affected-stakeholder reports may reveal whether the documented arrangement corresponds to practice. Auditing and accountability research supports the use of evidence capable of connecting organizational commitments, decisions, deployment conditions, and corrective action across the lifecycle [7,31,32,33]. Evidence quality should therefore be evaluated according to provenance, completeness, temporal relevance, accessibility, and independence.

External governance frameworks provide reference points for coverage without determining whether the proposed architecture is legally sufficient or empirically effective. The NIST AI Risk Management Framework supplies lifecycle-oriented governance and risk-management outcomes, ISO/IEC 23894 provides guidance for integrating AI risk management into organizational activities, and the EU AI Act connects applicable obligations to matters including intended purpose, risk management, documentation, human oversight, incident response, and post-deployment monitoring [34,35,36]. Table 2 shows how these sources are used to examine coverage rather than to claim equivalence, certification, or legal conformity.

The resulting assessment logic is diagnostic rather than certifying. External frameworks help determine whether important areas have been omitted, while the three assessment lenses show whether the mechanisms selected by the organization exist, correspond to the deployment, and perform their assigned functions. This combination supplies the evidentiary basis for the dimensions and indicators developed below.

5.3. Operationalization Through Assessment Dimensions and Indicators

The operationalization problem arises because broad governance concepts such as boundary integrity, accountability, and lifecycle responsiveness can be affirmed without specifying what evidence would support or contradict those claims. Unstructured expert judgment may identify important weaknesses, although it provides limited comparability when assessors use different objects, populations, periods, thresholds, or evidentiary standards. Each governance function must therefore be translated into an observable assessment dimension and evaluated through a protocol specified before the results are interpreted.

The framework responds by converting the seven governance functions developed in Section 4 into seven corresponding assessment dimensions. Figure 4 summarizes this translation and shows how each dimension is connected to a defined measure, denominator or sampling basis, measurement period, threshold, and evidentiary standard. The figure also distinguishes indicator results from the broader diagnostic judgment, which must account for severity, confidence, material exceptions, and the organizational response required.

Organizational anchoring is assessed as a cross-cutting condition within authority-interface coverage, resource-risk capacity, and lifecycle and portfolio coordination rather than as a separate eighth dimension.

Table 3 specifies the diagnostic question, illustrative indicators, and principal evidence associated with each dimension. The dimensions do not reproduce the four architectural layers described in Section 3.4, because they assess functional relationships that may extend across several layers. Nor do they establish validated measurement scales, because their denominators, thresholds, sampling rules, and evidence-quality criteria require calibration to the sector, deployment, and decision context.

Indicator protocols must be specified before results are interpreted. The denominator should identify the relevant population of interfaces, responsibilities, events, findings, or changes, while the measurement period should reflect the lifecycle and frequency of the activity being assessed. Thresholds should be justified by risk, reversibility, applicable requirements, organizational tolerance, and the consequences of delayed intervention rather than selected after the results are observed. Evidence-quality ratings should accompany each result because a numerically complete indicator derived from inaccessible, outdated, or insufficiently corroborated evidence may provide little assurance [7,34,35].

Aggregate values should not conceal severe exceptions. A high boundary-integrity rate may coexist with a single unauthorized expansion affecting a large population, while a high corrective-closure rate may conceal an unresolved action involving irreversible harm. The assessment should therefore report indicator values together with severity, confidence, material exceptions, and unresolved evidence gaps. The consequence is a multidimensional governance profile that supports longitudinal and comparative inquiry without presenting a single universal score as proof of effective governance.

5.4. Analytical Demonstration Through an Illustrative Application

The analytical demonstration problem is to show how the assessment framework identifies connected governance failures without presenting an illustrative scenario as evidence of empirical validity. A useful scenario must reveal whether the framework can distinguish formal existence from operational correspondence and trace how a weakness in one function affects later functions. The following hospital-triage scenario is therefore used as an analytical demonstration of the assessment sequence rather than as evidence of comparative effectiveness.

The scenario begins with a hospital authorizing an AI-enabled triage use case to support the prioritization of patients entering an emergency department. The authorized arrangement limits the system to clinical decision support, preserves final clinical authority, identifies the vendor model and hospital data environment, establishes review and escalation responsibilities, and requires records of recommendations, overrides, incidents, and material changes. At initial activation, the use case appears to satisfy the formal existence lens because its purpose, responsible actors, operational dependencies, and review procedures have been documented.

The scenario concerns a hospital that deploys a clinical-triage service supplied and periodically updated by an external AI vendor. The hospital remains the enterprise responsible for the governed use case, while the vendor controls a material technical dependency and holds information required for monitoring, review, and corrective action. At authorization, the service is limited to clinical decision support, final clinical authority remains with the hospital, and the applicable governance arrangement specifies review, escalation, update-notification, evidence-access, and incident-cooperation responsibilities. Recommendations, overrides, incidents, and material changes are recorded so that technical behavior, clinical judgment, and organizational response can be reconstructed.

A later combination of technical and organizational changes tests whether the formal arrangement continues to correspond to operation. The vendor updates the model, the hospital incorporates its output into additional patient-flow and resource-allocation decisions, clinical workload reduces the time available for meaningful review, and relevant evidence becomes divided between hospital and vendor systems. The same model service is also used by another hospital function, although the implications of the update and the emerging incident pattern are not communicated across the shared dependency. Figure 5 summarizes how these changes produce a divergence between the authorized configuration and the use case as operated, and how assessment findings can be connected to a coordinated governance response.

The figure presents the scenario-level progression, while Table 4 applies the seven assessment dimensions to the specific weaknesses identified within that progression. The table distinguishes the observed condition, its diagnostic implication, and the response required to restore correspondence. This division avoids treating the scenario as a single undifferentiated failure and shows how the general assessment framework can organize related findings without reducing them to one aggregate judgment.

The dimension-level findings in Table 4 confirm that the scenario involves connected governance weaknesses rather than unrelated deficiencies. The assessment identifies the loss of correspondence at the boundary and configuration levels, then shows how that loss affects authority, capacity, evidence, correction, and portfolio coordination. Its diagnostic value lies in distinguishing the initial divergence from the later governance consequences and in connecting each finding to a proportionate response.

The scenario therefore demonstrates how the framework can organize assessment findings, connect them to required responses, and distinguish a formal arrangement from its continuing implementation. It does not establish that the dimensions are complete, that different assessors would reach the same judgment, or that the proposed architecture performs better than alternative governance arrangements. Those questions require the validation strategy addressed in the following subsection.

5.5. Interpretation and Validation Limits

The interpretation and validation problem arises because quantified indicators and a coherent illustrative application may create an unwarranted impression of precision, completeness, or causal effectiveness. An indicator may be sensitive to how the denominator, threshold, sampling period, severity rule, or evidence-quality standard is defined, while an author-constructed scenario may favor the concepts used to construct it. The framework should therefore not be interpreted as a validated maturity model, certification standard, universal scoring instrument, or proof that protected-series implementation produces superior outcomes.

The framework addresses this risk by requiring results to be reported as a dimension-level profile accompanied by confidence, material exceptions, and evidentiary limitations. An overall aggregate score should not replace the underlying diagnostic findings, and a critical failure involving authority, irreversible harm, inaccessible evidence, or absent corrective capacity may warrant intervention regardless of favorable results elsewhere. Interpretation should also remain proportional to the use case because thresholds appropriate for a high-consequence clinical deployment may be unnecessary or misleading for a reversible internal application. The assessment result is therefore a reasoned governance judgment supported by specified evidence rather than an automatic decision generated by a numerical score.

Validation should proceed through progressively stronger forms of external examination. Structured expert review can assess whether the dimensions provide adequate and non-redundant coverage, independent application can test consistency among assessors, and bounded organizational pilots can examine feasibility, data availability, and implementation burden. Longitudinal studies can evaluate whether the indicators detect deterioration or predict governance failures, while comparative studies can examine whether the proposed architecture produces different outcomes from alternative legal and organizational arrangements. The external-framework comparison in Section 5.2 examines coverage, and the illustrative scenario examines analytical usability, although neither substitutes for these validation steps.

The principal consequence is a deliberately bounded assessment claim. The framework makes the governance propositions developed in Section 3 and Section 4 observable, contestable, and capable of empirical examination, while leaving their reliability, validity, calibration, and comparative effectiveness open to testing. Section 6 considers the implications of this limited claim for functional portability, proportionality, responsibility fragmentation, and the broader contribution to ESE.

6. Discussion

The framework developed in the preceding sections raises five questions that determine the scope and significance of its contribution: functional portability, proportionality, responsibility fragmentation, its contribution to ESE, and empirical validation. These questions concern the conditions under which the proposed architecture can be generalized, the circumstances in which its institutional demands may become excessive, the safeguards needed to prevent modularity from displacing responsibility, the theoretical implications for ESE, and the evidence required to test the framework. The discussion therefore interprets the framework and its limits rather than restating its components, governance functions, or assessment dimensions.

6.1. Functional Portability and Legal-Form Dependence

The portability problem is that the framework may appear inseparable from the protected-series form through which it was constructed. Protected-series law varies among jurisdictions, while differences in formation, asset association, internal governance, recordkeeping, liability consequences, and external recognition may affect whether a series can perform the functions attributed to it [52,53,54,55]. A framework dependent on the availability of one organizational form would have limited relevance to enterprises operating in jurisdictions that do not recognize protected series or in settings where creating a legally differentiated unit would be impractical. Protected series serve as the primary prototype because they make differentiated governance within a continuing organizational structure especially visible, not because they are presumed to be universally available or superior to every alternative. The portability question is therefore whether another arrangement can reproduce the required governance functions and institutional relationships rather than whether it replicates the protected-series form.

The framework addresses this problem by distinguishing the governance functions from the legal vehicle used to perform them. The protected series remains analytically valuable because legal personality and asset partitioning make authority, resources, obligations, records, and external relationships visible within a differentiated organizational boundary [42,43,44,45]. Its role is therefore to demonstrate how institutional differentiation can support the AI use-case boundary, rather than to establish protected-series adoption as a universal requirement. Functional portability asks whether another legal or organizational arrangement can reproduce the relevant relationships with sufficient specificity and continuity.

Portability must be evaluated function by function rather than inferred from the name or formal category of the alternative arrangement. An internal charter may define purpose and authority without providing separate control over assets or contracts, while a subsidiary may supply a strong legal boundary but impose costs and rigidity disproportionate to the deployment. Procurement arrangements, dedicated accounting structures, regulated operating units, or board-approved governance plans may reproduce some functions while leaving others dependent on enterprise-level processes. An alternative is therefore functionally comparable only insofar as it supports an identifiable boundary, controlled configuration, effective authority, adequate capacity, accessible evidence, lifecycle adaptation, and organizational anchoring.

The principal consequence is that modular legal personhood should be understood as a design pattern with form-dependent implementations. The protected series provides a particularly explicit prototype, although its legal availability does not establish its organizational suitability, and its absence does not make use-case-level governance impossible. Functional portability broadens the framework beyond a single jurisdiction while preserving a demanding test: alternative arrangements must be evaluated by the governance relationships they actually establish rather than by formal resemblance to the prototype.

6.2. Proportionality and the Risk of Over-Formalization

The proportionality problem is that institutional differentiation can impose cost, delay, rigidity, and administrative burden that exceed the governance needs of a particular use case. A low-consequence, reversible, or exploratory deployment may not justify the same legal, contractual, evidentiary, and organizational commitments as a deployment affecting safety, legal rights, essential services, or large populations. Excessive formalization may also produce compliance activity that consumes organizational resources without materially improving control over the deployment. At the same time, under-formalization may permit a use case to become operationally consequential before its authority, resources, evidence, and corrective pathways have been adequately established.

The framework addresses this tension by treating proportionality as a relationship between governance intensity and the consequences of governance failure. Relevant considerations include the severity and reversibility of potential effects, the scale and duration of the deployment, the vulnerability of affected stakeholders, the degree of automation, the number and independence of external dependencies, the difficulty of detecting errors, and the legal or regulatory duties applicable to the activity. Risk-management and regulatory frameworks similarly differentiate governance obligations according to context, risk, lifecycle conditions, and the potential consequences of deployment [34,35,36]. The issue is therefore not whether institutionalization should be minimized, but whether its intensity is justified by the use case’s present and foreseeable governance demands.

The staged approach developed in Section 5.1 provides the implementation response without making a particular stage permanent. An initially limited arrangement may be adequate while a use case remains bounded, reversible, and closely supervised, although increasing scale, dependency, consequence, or irreversibility may justify stronger organizational and legal differentiation. Conversely, formal structures should not continue merely because they were once adopted when the use case has been terminated, reduced in scope, or replaced by a less consequential activity. Proportionality therefore applies both to institutional escalation and to justified simplification.

The principal consequence is that the framework does not require every AI use case to become a protected series or equivalent legal entity. It requires every use case to possess governance arrangements proportionate to the risks, dependencies, and organizational effects that must be controlled. Protected-series implementation becomes appropriate only when the value of a differentiated and persistent institutional boundary exceeds its legal and administrative burden. This interpretation preserves the framework’s practical relevance while preventing modular legal personhood from becoming an end independent of governance need.

6.3. Responsibility Fragmentation and Anti-Evasion Safeguards

The responsibility-fragmentation problem is that the same legal and organizational differentiation that clarifies accountability can also be used to divide, obscure, or externalize it. A narrowly defined module may be assigned formal responsibility without the resources needed to discharge it, while an associated LLC or other parent organization may attempt to treat shared infrastructure, portfolio decisions, or enterprise incentives as matters outside the use-case boundary. Liability segregation and separate records may intensify this risk when they are used to restrict access to evidence or portray organizationally connected decisions as the conduct of an isolated unit. Critiques of electronic and synthetic personhood similarly warn that entity-based arrangements may displace attention from the human and organizational actors that design, control, and benefit from AI systems [46,47,48,49,50,51,59].

The framework addresses this problem by treating the use-case boundary as an instrument for locating responsibility rather than limiting the field of inquiry. Operational correspondence requires the nominal module to match the activity it is said to govern, while authority and resource adequacy prevent responsibility from being assigned to an institutionally empty unit. Accessible traceability permits reviewers to examine decisions and dependencies extending beyond the module, and enterprise-level escalation preserves responsibility where effective authority or capacity remains with the associated organization. These conditions make the legitimacy of modularization depend on substance rather than formal separation.

The same safeguards also establish an anti-evasion principle: organizational differentiation should not reduce the ability of affected stakeholders, auditors, regulators, or other competent forums to identify responsible actors and obtain a response. Accountability requires an actor capable of explaining and justifying conduct before a forum able to evaluate that account and initiate consequences [31,32,33]. Organizational research on responsible AI further indicates that accountability depends on access to decision makers, reporting relationships, institutional support, and the capacity to influence deployment decisions [23,28]. Where formal modularization obstructs those relationships, it weakens rather than strengthens governance.

The principal consequence is that modularity improves accountability only when it localizes responsibility without displacing enterprise responsibility. A module may govern decisions and risks within its effective authority, although shared dependencies, inadequate resources, reserved powers, and portfolio-level decisions remain attributable to the organizational actors that control them. When the formal boundary conceals rather than reveals those relationships, governance assessment should follow the operational allocation of authority, benefit, information, and control. Modular legal personhood is therefore defensible as an accountability architecture only under continuing anti-evasion review.

Figure 6 integrates the portability, proportionality, and anti-evasion arguments developed in Section 6.1 to Section 6.3 into a function-based selection process. The process does not privilege a particular legal form because every proposed arrangement must be evaluated by the governance functions it establishes and by its ability to preserve responsibility across the use-case and enterprise boundaries.

The selection process separates two questions that might otherwise be conflated. The proportionality inquiry determines the degree of institutional differentiation justified by the use case, whereas the functional and anti-evasion inquiries determine whether the selected arrangement is substantively adequate. A less formal arrangement may therefore be acceptable when it performs the required functions, while a legally differentiated arrangement should be rejected or revised when it lacks operational correspondence, resources, accessible evidence, or continuing enterprise responsibility.

6.4. Contribution to Enterprise Systems Engineering

The ESE problem addressed by this study is that legal and contractual arrangements are often treated as external constraints on enterprise architecture rather than as configurable components of the enterprise system itself. ESE provides methods for analyzing purpose, boundaries, stakeholders, interfaces, resources, lifecycle change, and organizational transformation, although the institutional mechanisms through which those relationships acquire authority and continuity may remain under-specified [3,4,11,35]. AI governance presents this limitation sharply because technical controls and enterprise-wide policies require an intermediate organizational object through which they can be connected to a particular activity.

The first contribution to ESE is the identification of the AI use case as an intermediate enterprise-system boundary. This boundary is not simply a technical decomposition because it incorporates organizational purpose, human authority, affected stakeholders, resources, evidence, external dependencies, and lifecycle responsibility. Socio-technical systems research supports the inclusion of these relationships within the system of interest, while the present framework adds an institutional mechanism through which the resulting boundary can remain identifiable and governable [5,6,17,18]. The use-case boundary therefore connects technical specificity with enterprise-level accountability.

The second contribution is the treatment of legal-organizational configuration as an ESE design object. Modular-systems theory explains how bounded components, stable interfaces, and controlled variation can reduce complexity and support adaptation [37,38,39,40,41]. The framework extends this reasoning by distinguishing structural modularity, which differentiates the institutional unit, from configurational modularity, which varies the governance applicable within that unit. Legal form and contract consequently become elements through which enterprise boundaries, interfaces, authority, and lifecycle controls can be designed rather than merely background conditions with which the architecture must comply.

The third contribution is a correspondence-based account of governance performance. The framework does not infer governability from the existence of a legal unit, contractual provision, technical control, or documentary record, because governance depends on continuing correspondence among formal arrangements, operational activity, authority, resources, evidence, and corrective capacity. This account enables ESE analysis to examine where a governance function ceases to support the next and how that breakdown affects the enterprise system. The broader implication is that digital transformation involves the configuration of normative and organizational architectures alongside technical architectures.

The principal consequence is an expanded conception of enterprise-system design. AI-enabled transformation can be analyzed not only through technology, process, data, and organizational structure, but also through the legal and contractual arrangements that stabilize boundaries, allocate authority, preserve evidence, and govern change. This contribution remains design-theoretic, although it produces propositions that can be compared across legal forms, sectors, and organizational settings. The framework thereby connects institutional design with ESE without reducing either field to the concepts of the other.

6.5. Empirical Research Agenda

The empirical-validation problem is that the framework’s internal coherence, literature-derived requirements, external reference points, and illustrative application do not establish completeness, reliability, usability, or comparative effectiveness. The hospital scenario shows how the proposed dimensions can organize an analysis, although an author-constructed example cannot demonstrate that independent assessors would apply the framework consistently or that organizations using it would produce better governance outcomes. The framework must therefore remain a set of testable design propositions rather than be presented as a validated maturity model or universal assessment instrument.

The first stage of empirical research should examine content validity and practical usability. Structured review by participants with expertise in ESE, organizational law, AI governance, risk management, technical assurance, and affected-stakeholder representation can assess whether the design requirements and assessment dimensions omit material governance relationships or contain unnecessary overlap. Independent assessors can then apply the framework to the same deployment materials to determine whether the concepts, evidence requirements, and decision rules support sufficiently consistent judgments. These studies should also document the time, expertise, access, and organizational burden required to perform the assessment.

The second stage should examine implementation and change over time. Bounded organizational pilots can test whether the required evidence is available, whether responsibility and escalation paths can be implemented, and whether assessment findings lead to changes in authority, resources, monitoring, or deployment scope. Longitudinal studies can examine whether the dimensions detect declining correspondence before significant incidents occur and whether corrective responses restore alignment. The assessment indicators developed in Section 5.3 provide initial variables for such inquiry, although their denominators, thresholds, and evidentiary standards require sector-specific calibration.

The third stage should examine comparative and causal claims. Studies can compare protected-series implementation with subsidiaries, internal governance units, contractual programs, or other arrangements performing similar functions, while controlling for differences in risk, organizational scale, sector, and regulatory environment. Relevant outcomes may include the detection of use drift, time to obtain evidence, coverage of material interfaces, response time following substantiated findings, persistence of unresolved corrective actions, and propagation of incidents through shared dependencies. External frameworks such as the NIST AI Risk Management Framework, ISO/IEC 23894, and applicable regulatory requirements may provide common reference points for comparing coverage, although they do not determine the effectiveness of a particular organizational form [34,35,36].

The principal consequence of this research agenda is that the framework becomes falsifiable and revisable. Evidence may show that some dimensions are redundant, that particular indicators cannot be collected reliably, that legal differentiation adds little beyond strong internal governance, or that the administrative burden exceeds the benefits in certain settings. Conversely, empirical study may identify conditions under which a persistent use-case boundary improves change detection, evidentiary continuity, corrective responsiveness, or portfolio coordination. The contribution of the framework therefore lies not only in the architecture proposed here, but also in establishing a structured program through which its assumptions and consequences can be tested.

7. Conclusions

This article addressed a boundary problem in enterprise AI governance: model-level controls are too narrow to capture the organizational activity through which AI produces consequences, whereas enterprise-wide policies are too broad to govern deployments with materially different purposes, dependencies, risks, and affected stakeholders. It answered this problem by identifying the AI use case as an intermediate enterprise-system boundary and by developing modular legal personhood as a means of giving that boundary institutional form. Protected series supplied the primary legal prototype, while modular operating agreements provided a configurable contractual architecture through which stable governance requirements could be combined with deployment-specific variation. The resulting AI use-case module connects technical operation with organizational purpose, authority, resources, evidence, corrective control, and lifecycle responsibility. The principal conclusion is that an AI use case can become a governable enterprise subsystem when its legal, contractual, organizational, and operational boundaries remain aligned.

The framework contributes to Enterprise Systems Engineering by treating legal and contractual arrangements as components of enterprise-system design rather than as external constraints applied after technical architecture has been established. Structural modularity differentiates the use case as an institutional unit, while configurational modularity permits its governance to change without dissolving the continuity of that unit. This combination extends modular design from the allocation of technical and organizational functions to the configuration of authority, obligations, evidence, resources, and change procedures. The framework does not attribute autonomous personality or responsibility to the AI system because modular legal personhood organizes responsibility around the socio-technical deployment and the enterprise participants who authorize, operate, oversee, and benefit from it. Its ESE contribution therefore lies in connecting a determinate system boundary with the institutional mechanisms required to preserve that boundary throughout digital transformation.

The functional analysis showed that no individual legal or organizational mechanism can establish governability by itself. Boundary maintenance must identify the activity being governed, the applicable configuration must remain traceable, responsible actors must possess authority over material interfaces and resources proportionate to assigned risks, and evidence must connect review with corrective action. Those relationships must also remain aligned as the deployment changes and as effects propagate through shared enterprise dependencies. Because each function supplies conditions required by the next, a weakness should be diagnosed where the functional chain loses continuity rather than inferred from the absence or presence of any single document, entity, or control. The framework consequently evaluates governance through continuing correspondence between formal arrangements and the use case as actually operated.

Implementation remains conditional on proportionality, functional suitability, and safeguards against responsibility fragmentation. The protected-series form is useful because it makes institutional differentiation explicit, although neither its availability nor its formal adoption establishes that it is appropriate for a particular deployment. Alternative legal or internal organizational arrangements may perform comparable functions when they establish an identifiable boundary, effective authority, adequate capacity, accessible evidence, controlled adaptation, and continuing enterprise responsibility. Conversely, modularization is not legitimate when it isolates records, assigns responsibility to an under-resourced unit, obscures shared dependencies, or prevents affected stakeholders and competent reviewers from identifying the actors capable of response. The use-case boundary must therefore operate as a mechanism for locating and coordinating responsibility, not as a device for limiting inquiry or externalizing risk.

The proposed framework remains design-theoretic and diagnostic rather than empirically validated. The assessment dimensions and illustrative application make its propositions observable and contestable, although they do not establish completeness, reliability among independent assessors, predictive validity, comparative effectiveness, or sector-specific suitability. Future research should test whether the framework can be applied consistently, whether its evidence requirements are practicable, whether its indicators detect deteriorating correspondence, and whether institutional differentiation improves governance relative to alternative arrangements. Such findings may support revision, simplification, or rejection of particular components rather than confirmation of the framework as a universal model. The broader implication is that responsible AI-enabled digital transformation requires more than enterprise policy and technical control: it requires an institutional architecture that makes each consequential use case identifiable, configurable, observable, correctable, and adaptable while preserving responsibility within the enterprise as a whole.