From AI Adoption to AI Transformation: The AX-5R Framework for Socio-Technical Work System Redesign

You’re listening to “From AI Adoption to AI Transformation: The AX-5R Framework for Socio-Technical Work System Redesign,” by K.S. Shin, I.S. Kang, and M. Lee. Published in 2026.

Article

From AI Adoption to AI Transformation: The AX-5R Framework for Socio-Technical Work System Redesign

Kwan Soo Shin 1,2,3,, In Seok Kang 3,4 and Munho Lee 3

PolymathMinds Lab, Asan 31457, Republic of Korea 1

Department of AI and Innovation Management, Hanil University and Presbyterian Theological Seminary, 2 Wanju 55359, Republic of Korea

Graduate School, aSSIST University (Seoul School of Integrated Sciences and Technologies), Seoul 03767, 3 Republic of Korea; the email address (I.S.K.); the email address (M.L.)

Department of Chemical Engineering, Pohang University of Science and Technology (POSTECH), 4 Pohang 37673, Republic of Korea

Highlights

Please indicate how your work links to systems science via your contributions to sys-tems practice, theory, and or methodology.

• AX-5R contributes to socio-technical systems theory and practice by integrating read-iness, redesign, role, risk, and return into a workflow-level control loop that converts fragmented generative AI use into governed, measurable work system transfor-mation.

• Adjacent frameworks supply the anatomy, stages, and controls of organizational AI adoption; AX-5R adds the workflow-level execution sequence that links them into one governed cycle.

What are the main findings and/or the implications of the main findings?

• In a blinded artifact-level ablation probe, the full framework outscored a sham five-part control and its component ablations, significant under two-sided Holm-cor-rected testing, and the machine scores converged with an independent human-ex-pert-rating check (r = 0.78).

• Managers can move from AI pilots to governed scaling using the framework’s im-plementation artifacts, configuration archetypes, and six testable propositions.

Abstract.

Generative artificial intelligence (AI) has diffused rapidly, yet adoption has not reliably progressed to AI transformation (AX). Firms grant tool access but fail to redesign work-flows, clarify accountability, govern risks, or measure value. The gap is a socio-technical systems problem, not a productivity problem: AI tools are inserted into existing routines without redesigning task interdependencies, decision rights, oversight loops, or performance feedback. This paper develops AX-5R, a socio-technical systems architecture that converts fragmented AI use into accountable, governable, and measurable work systems. Synthesizing seven literature streams, it maps failure modes to five interdependent design functions: readiness, redesign, role, risk, and return. AX-5R treats transformation as joint optimization of technical and social subsystems requiring configurational alignment.

A supplementary ablation probe generated 252 artifacts across three workflows and seven prompt arms from two language models, scored by blinded cross-provider judges; the full frame outscored a sham five-part control and its four artifact-relevant ablations,

Academic Editors: Mathias Fonkam and Narasimha Rao Vajjhala

Correspondence: the email address

significant under two-sided Holm-corrected testing, with an independent human-expert-rating check. With a failure-mode derivation matrix, implementation artifacts, and six testable propositions, the framework specifies a minimum architecture in which readiness sets boundaries, redesign restructures tasks, role assigns accountability, risk establishes control, and return supplies learning feedback.

1. Introduction.

Generative AI has diffused rapidly into contemporary management practice. Large language models and related systems are now used for writing, summarization, coding, document search, customer response drafting, ideation, translation, analytics support, and internal knowledge work. This diffusion has lowered the entry barrier to organ-izational AI use. Employees can now experiment with AI without waiting for a centralized enterprise system, a full data science team, or a large-scale IT transformation project.

However, this ease of adoption creates a paradox. The more easily AI tools are adopted, the easier it becomes for organizations to mistake usage for transformation. AI adoption means that individuals, teams, or departments use AI tools for particular tasks. AI transformation (AX), by contrast, begins when AI changes how work is designed, de-cisions are made, responsibilities are allocated, risks are governed, and performance is measured. A firm may have high AI usage but low AX performance. Employees may save time locally, yet the organization may not know which workflows changed, which risks increased, which human responsibilities were reallocated, or which outcomes improved.

This distinction matters because the value of AI depends less on isolated tool use than on organizational redesign. Digital transformation research shows that technology produces value when it changes processes, capabilities, routines, and business models. Information systems research similarly demonstrates that adoption is not equivalent to assimilation, routinization, or effective use. AI intensifies this older implementa-tion problem because it can participate directly in cognitive work. Unlike earlier digital tools that mainly stored, transmitted, or processed information, generative AI can draft arguments, summarize documents, classify cases, recommend options, and simulate ex-pertise.

The managerial challenge is therefore not simply to provide access to AI tools, but to redesign the surrounding work system to align with AI’s appropriate role, accountability boundaries, risk exposure, and feedback logic.

The focal problem, therefore, is how organizations can convert fragmented AI use into workflow- or portfolio-level AX. This paper argues that such conversion requires five linked implementation moves: assessing workflow-specific readiness, redesigning work-flows, clarifying human–AI roles, governing risk through human oversight, and measur-ing return through balanced indicators. These five moves form the AX-5R framework: readiness, redesign, role, risk, and return.

This gap is not merely a productivity problem but a failure of socio-technical systems integration. AI tools are added to individual tasks, but the surrounding work system, in-cluding workflow structure, role allocation, risk controls, and performance feedback, re-mains unchanged. As a result, local efficiency does not necessarily propagate into organ-ization-level capability. Treated this way, AX is realized through the disciplined reconfig-uration of an interdependent socio-technical system in which technical and social subsys-tems are jointly optimized, rather than a tool layered onto unchanged routines. This sys-tem’s framing explains why fragmented adoption stalls and why improving any single component in isolation leaves the larger system untransformed.

It also clarifies why re-sponsible oversight and balanced measurement are not optional add-ons: they are the control and feedback functions without which a work system cannot stabilize, scale, or sustain AI-enabled change.

This article is designed as an integrative framework study. It does not propose a new algorithm, estimate a causal model, or report a company case study. Instead, it synthesizes dispersed research streams into a socio-technical systems architecture for AI-enabled work system transformation. This is appropriate because current AX practice is ahead of theory integration. Managers face urgent questions: Which AI use cases should be selected first? Which workflows are sufficiently ready for AI-enabled redesign? Which tasks can be supported by AI, and which require human approval? How should risk be controlled as AI exposure increases? How should success be measured beyond vague productivity claims?

The existing literature offers useful fragments, but practical AX requires an inte-grated architecture that links readiness, workflow redesign, role allocation, risk govern-ance, and return measurement.

The study is guided by four conceptual research questions and one supplementary artifact-probe question:

RQ1. What explains the gap between AI adoption and workflow- or portfolio-level AX?

RQ2. Which literature streams are most relevant for theorizing AX as an implementation problem?

RQ3. How can organizations redesign workflows and human–AI roles while main-taining oversight?

RQ4. What practical framework can guide managers from AI pilots to scalable AX? RQ5. In a supplementary artifact-level ablation probe using blinded cross-provider scoring, is the full AX-5R frame associated with higher-rated implementation artifacts than its single-component ablations and a sham five-part control?

The paper’s central novelty lies not in adding another list of AI adoption factors but in specifying the socio-technical systems architecture required to convert fragmented AI tool use into redesigned, accountable, governable, and measurable organizational work systems. Unlike adoption-performance studies, governance-only models, broad reviews of AI’s effects, or domain-specific workflow studies, this paper develops a cross-organi-zational execution architecture derived from recurring implementation failure modes. The contribution is fourfold. First, the paper separates AI adoption from AX and argues that AX begins only when work, responsibility, risk control, and performance systems are redesigned together. Second, the paper synthesizes seven literature streams that are often discussed separately.

Third, the paper adds a supplementary artifact-level ablation probe using blinded cross-provider LLM scoring to examine whether the full AX-5R frame is associated with higher-rated implementation artifacts than its single-component ablations and a sham five-part control. Fourth, the paper provides implementation artifacts, includ-ing an AX lineage map, an adoption–transformation gap model, a human–AI role and risk matrix, an AX-5R process model, use-case selection criteria, role allocation levels, risk con-trols, and performance indicators.

2. Theoretical Background and Literature Review.

2.1. Structural Overview.

The AX problem sits at the intersection of seven studies. Digital transformation un-derscores the need to link technology adoption to strategic and organizational change. Information systems adoption explains why initial use does not automatically become as-similation, routinization, or effective use. AI and management research explains the auto-mation–augmentation tension, the reallocation of decision authority, and the emergence of human–AI collaboration as an organization-design problem. Generative AI research explains why general-purpose language systems differ from earlier enterprise systems: they diffuse informally, participate in cognitive work, and generate fluent but fallible out-puts. The workflow redesign literature explains how technologies create value only when tasks, routines, and interdependencies are redesigned.

The AI governance literature ex-plains why human oversight, auditability, and risk controls must be embedded into work systems rather than added as external policy layers. The performance management liter-ature explains why AX performance must be measured using balanced indicators rather than relying solely on tool usage or productivity anecdotes.

These streams are not separate bodies of literature but partial views of a single socio-technical systems problem. AI creates organizational value only when the technical arti-fact, workflow structure, human role system, governance controls, and feedback mecha-nisms are redesigned together. The central gap is therefore not the absence of research on AI adoption, digital transformation, or AI governance. It is the absence of an integrated systems architecture that explains how fragmented AI use can be converted into rede-signed, accountable, governable, and measurable work systems.

In Figure 1, AX is positioned as the missing high-redesign and high-governance cell in prior research. Both axes are defined operationally. A workflow scores high on the hor-izontal axis when its before-and-after process maps differ in task structure, not merely in tool presence, and scores high on the vertical axis when review authority, input rules, escalation paths, and outcome indicators are documented parts of the workflow rather than external policies. The lower-left cell captures tool adoption and acceptance; the lower-right cell captures process redesign without sufficient oversight; the upper-left cell captures governance without work system transformation; and the upper-right cell iden-tifies AX-5R as the integrated work-system architecture that closes the workflow, role, risk, and return gaps together.

measurement embedded in the workflow. The shaded upper-right cell marks AX-5R as the inte-grated work-system architecture in which both axes are satisfied jointly.

2.2. Literature Lineage and Benchmarking Logic.

The review is organized as a lineage rather than a strict chronology. The first gener-ation explains technology acceptance, implementation politics, IS success, effective use, situated practice, and the structuring effects of technology. Its core lesson is that acceptance and use, by themselves, do not create organizational value.

Beneath this lineage lies a socio-technical systems foundation. Socio-technical sys-tems theory holds that organizational performance depends on the joint optimization of interacting technical and social subsystems rather than on technology alone, and work system theory extends this principle to the deliberate redesign of the workflows, roles, information, participants, and technologies through which work is accomplished. This foundation is directly relevant to AI, whose value depends on how the sur-rounding work system is reconfigured. However, generative AI sharpens the classic socio-technical problem because it diffuses through decentralized access to tools, participates in cognitive work, produces probabilistic outputs, and blurs the boundary between assis-tance, recommendation, and decision-making influence.

Recent systems research applies the same lens to generative AI, conceptualizing AI adoption in firms as an integrated so-cio-technical system in which governance, capabilities, and technology jointly shape out-comes. Parallel systems studies extend this lens to generative-AI-driven transfor-mation in specific work domains and show that balanced social and technical config-urations, rather than technology alone, drive digital-transformation outcomes. The present paper, therefore, does not merely restate the joint-optimization principle or esti-mate how AI adoption relates to firm performance; it operationalizes joint optimization as AX-5R, an execution architecture that converts fragmented AI use into a redesigned, accountable, governable, and measurable work system. Section 2.12 develops this posi-tioning framework by framework.

The second and third generations shift from use to transformation capability. Digital strategy, digital innovation, platform transformation, IT-enabled organizational transfor-mation, change management, and growth theory locate technology value in strategic re-sponse, organizational renewal, leadership, and complementary assets. Capa-bility, knowledge, process, and sociomaterial research then explain why firms differ in their ability to convert the same technology into repeatable routines and measurable work system change.

The fourth and fifth generations place AI inside task reallocation, organizational de-sign, human–AI collaboration, and governance. Task-based economics and productivity studies show that AI effects vary by task, expertise, workflow integration, and comple-mentary organizational capital. Organizational AI research adds automa-tion–augmentation tensions, conjoined agency, algorithmic control, hybrid intelligence, process automation, and meaningful-work risks. Foundation models, risk, human factors, documentation, auditing, standards, and policy sources set the boundary condi-tion: fluent AI output cannot be equated with reliable organizational action.

2.3. Digital Transformation: Technology Adoption Is Not Transformation.

Digital transformation research treats technology value as an organizational change problem rather than a deployment problem. Digital technologies alter value creation, structures, processes, and business models only when firms combine digital capability, leadership, strategy, operating-model redesign, and dynamic capability formation. Integrative reviews of digital transformation reach the same conclusion re-garding the framework structure: technology-driven change succeeds through interdependent managerial dimensions treated as a single configuration rather than through any single dimension, including in resource-constrained small and medium en-terprises. Applied to AX, the implication is direct: AI access without workflow rede-sign remains adoption; redesign without risk control remains fragile; and measurement based only on usage mistakes for value remains fragile.

This stream settles that transfor-mation is an organizational change problem, not a deployment problem. What it leaves unresolved is the task level: which workflow is redesigned, who verifies AI output inside that workflow, and what evidence justifies scaling all remain outside its unit of analysis. AX-5R adopts the stream’s change thesis and supplies the missing workflow-level execution logic through Redesign and Return.

2.4. Information Systems Adoption: From Use to Assimilation and Effective Use.

Information systems adoption research explains why acceptance is necessary but in-sufficient. TAM and UTAUT explain intention and use, while IS success, effective use, implementation, and practice-lens research distinguish adoption from assimilation, rou-tinization, and net benefits. This distinction is especially important for generative AI because informal experimentation can spread before policies, workflow maps, review standards, or performance measures are in place. AX therefore asks not only whether AI is used, but whether use becomes a redesigned and governable routine. This stream establishes the adoption–assimilation distinction on which the paper’s gap claim rests. It does not, however, explain how use converts into assimilation when the technology is fallible, decentralized, and cognitive, since its models predate that combination.

AX-5R treats that conversion mechanism as precisely the missing construct and names its components.

2.5. AI and Management: Automation, Augmentation, and Human–AI Collaboration.

AI and management research frames AX as an automation, augmentation, and or-ganization-design problem. AI can substitute for tasks, complement human capabilities, alter decision-making structures, generate joint agency, create data-network effects, and change competitive advantage. Agentic IS, AI management, hybrid intel-ligence, organization design, and AI teammate research further show that delegation, au-tonomy, learning, inscrutability, and complementarity must be designed rather than as-sumed. Trust and reliance studies add the human boundary: employ-ees may overtrust fluent output or reject algorithms after errors, so AX requires explicit autonomy levels, review duties, escalation rules, and approval points. The settled insight here is that human–AI configurations must be designed rather than assumed.

What remains open is the implementation question: the stream specifies which tensions exist (automation versus augmentation, reliance versus rejection) but not the ar-tifacts through which a specific workflow resolves them. AX-5R answers with the Role component and its allocation matrix.

2.6. Generative AI and Foundation Models: A New Adoption Context.

Foundation models make AX urgent because they are general-purpose, flexible, and accessible through natural-language interfaces. Their broad capabilities support writ-ing, summarization, coding, classification, and reasoning-adjacent work, but also create risks of plausible errors, fabricated sources, sensitive data exposure, and unreviewed AI-generated material. Adoption-side research continues to accumulate at the level of use intention, including mixed-methods work on the determinants of generative AI use intention in domain-specific settings such as construction engineering; such studies sharpen the explanation of why individual uptake spreads quickly, and equally sharpen the gap this paper addresses, because use intention is an individual-level antecedent of adoption, not evidence of work system transformation.

Generative AI there-fore makes adoption more bottom-up and less centralized, increasing the need for rules about input, output, review, escalation, and measurement. This literature establishes the technology-side boundary condition: fluent output with plausible error, diffusing bottom-up through natural-language interfaces. It does not provide an organizational re-sponse architecture, which is not its job. AX-5R takes the risk profile established here as a design input for Readiness and Risk.

2.7. Workflow Redesign and Technology-in-Practice.

Workflow redesign research provides the mechanism for transformation. Process in-novation and reengineering argue that technology value emerges when processes are re-designed rather than superficially digitized. At the same time, practice, affordance, and algorithm-at-work research show that technology effects depend on routines, identities, enactment, and control relations. In AX, redesign decomposes work into retrieval, summarization, drafting, comparison, recommendation, approval, execu-tion, and documentation, then assigns AI only to suitable subtasks. Robotic process auto-mation research reinforces the same point: automation still requires decisions about which steps are scripted, monitored, escalated, or redesigned. The redesign stream contrib-utes the paper’s core mechanism: value emerges from process restructuring, not tool in-sertion.

It is an open question for generative AI: which subtasks may be del-egated to a probabilistic system and under what review? AX-5R resolves this by pairing Redesign with Role and Risk rather than treating redesign as a stand-alone move.

2.8. AI Governance, Human Oversight, and Risk Management.

AI governance research shows that scaling increases exposure to risks of inaccuracy, bias, privacy, security, compliance, intellectual property, and accountability. NIST, the EU AI Act, and ISO/IEC 42001 translate these risks into management-system expecta-tions, while datasheets, model cards, audit frameworks, and accountability-gap research show why AI use needs documentation, traceability, and review artifacts. Systems research similarly treats the governance of interconnected socio-tech-nical systems as a design problem inside the system architecture rather than an external policy layer. HR and meaningful-work research also add that governance must pro-tect fairness, autonomy, employee reactions, and work quality.

In AX, governance is therefore proportional: low-risk summarization may need light-source checks, while legal, financial, employment, medical, safety, or regulated decisions require strict account-ability or exclusion from automation. Governance research clarifies the control obligations and the documentation forms for them. Left unspecified is the coupling problem: how control attaches to value-creating redesign instead of running as a parallel compliance program. AX-5R embeds proportional control within the workflow cycle, a design choice that distinguishes it from stand-alone governance frameworks (Section 2.12).

2.9. Performance Management and Value Realization.

Performance management research prevents AX from becoming a usage narrative. Generative-AI productivity effects vary by task, expertise, review burden, and workflow integration, so time savings alone are insufficient. Balanced-scorecard, IS success, and digital-transformation research point toward broader measurement: financial and operational efficiency, output quality, user satisfaction, customer impact, risk control, capability development, and strategic alignment. AX therefore requires baselines, pilot outcomes, and learning indicators that tell managers whether to scale, re-vise, or stop a use case. This stream establishes that usage and time savings are not value evidence. What it leaves open is which indicators can be used to adjudicate a scaling decision for a specific redesigned workflow.

AX-5R’s Return component con-verts the balanced-measurement principle into pilot-level baselines and stop, revise, or scale rules.

2.10. Closing the Systems Gap: AX as Integrated Execution.

Together, these studies explain why AI use must be converted into redesigned, gov-erned, and measured work. Yet they remain fragmented across adoption, transformation, workflow, governance, and performance traditions. What remains missing is a systems-level implementation architecture that specifies how organizations move from frag-mented tool use to accountable, governable, and measurable work system transformation. AX-5R is proposed to fill that gap. Stated critically rather than descriptively, the seven streams jointly explain why adoption fails to progress to transformation, but each stops one level short of execution. The strategy stream lacks the workflow unit; the workflow stream lacks the accountability and control logic; the governance stream lacks the value-creation coupling; and the measurement stream lacks the redesign object it is meant to measure.

AX-5R is the configuration that simultaneously closes these four gaps, which is why it is derived from failure modes (Section 3.2) rather than assembled by juxtaposition.

In Figure 2, AX is presented as a five-generation lineage rather than a simple conver-gence diagram. The figure shows how IS adoption, digital transformation, work system redesign, AI management, and generative-AI governance accumulate toward the paper’s focal claim: AX is realized through an integrated socio-technical systems architecture, AX-5R, rather than being a synonym for adoption or a stand-alone governance program. These generations are overlapping conceptual streams organized by their dominant con-tribution to AX, not strict chronological periods.

2.11. Construct Boundary: AX and Neighboring Concepts.

Because AX is an emerging managerial term, it must be distinguished from neigh-boring concepts. Here, AX is not used as a synonym for AI adoption, digital transfor-mation, AI governance, AI maturity, or responsible AI. These concepts overlap, but each emphasizes a different managerial problem. AI adoption focuses on whether AI tools are used. Digital transformation focuses on broader technology-enabled organizational change. AI governance focuses on risk, accountability, and control. AI maturity focuses on diagnostic assessment. Responsible AI focuses on ethical and social acceptability. AX, as used in this paper, focuses on the execution problem of converting AI tool use into redesigned work systems that have clear human–AI roles, proportionate oversight, and measured value.

This boundary is important for theoretical and practical reasons. If AX is treated only as adoption, organizations may measure usage and training while ignoring whether work has changed. If AX is treated only as governance, organizations may control risk while failing to redesign value-creating workflows. If AX is treated only as digital transfor-mation, the distinctive challenges of generative AI (fluent but fallible output, shadow AI use, prompt-level data exposure, and ambiguous accountability) may disappear inside a broad transformation vocabulary. AX therefore occupies a middle position: it translates the strategic logic of digital transformation and the control logic of AI governance into an operational model for AI-enabled work redesign.

The construct boundary is summarized below.

In Table 1, AX is distinguished from neighboring concepts by shifting the unit of analysis from tool use, maturity assessment, governance principles, or general digital transformation to redesigned work systems. The table clarifies that AX is not a synonym for AI adoption; it is the realized work-system state that an organization’s AX implementation capability produces by aligning workflows, roles, risks, and returns around AI-en-abled work.

The boundary table also defines the paper’s theoretical claim, which rests on a hier-archy of four related but distinct levels. AX-5R is a socio-technical work-system architec-ture: it specifies a workflow-level readiness gate and four transformation functions (rede-sign, role, risk, and return) through which fragmented AI use is converted into governed, accountable, and measurable work. AX implementation capability is the repeatable or-ganizational ability to enact, adapt, and learn from this architecture across a portfolio of workflows. AX denotes the realized state of a workflow once minimum readiness condi-tions are met and the four transformation functions are enacted. AX performance refers to the measurable consequences of that realized state, and is not synonymous with AX itself.

AX-5R is therefore an architecture, not a capability, and the five Rs are its design functions rather than five parallel capabilities.

The construct can now be stated formally. AX is defined at the level of the work sys-tem, that is, an individual workflow or a portfolio of workflows, not at the level of the firm, the tool, or the individual user. Four conditions are individually necessary and jointly sufficient for a workflow to count as transformed rather than merely exposed to AI: (a) the task structure has been redesigned around AI-suitable subtasks rather than left unchanged (Redesign); (b) an accountable human role with explicit review or approval authority is allocated over AI-touched outputs (Role); (c) oversight and escalation controls proportionate to the workflow’s stakes and data sensitivity are in place (Risk); and (d) outcomes are measured against a pre-AI baseline on more than usage or time saved (Re-turn).

Readiness is deliberately excluded from the definition: it is the entry condition that makes satisfying the four definitional conditions feasible, not a component of the trans-formed state itself, which is consistent with its behavior in the ablation probe (Section 3.3). Each condition has a directly observable indicator, namely the implementation artifacts of Section 5: a before-and-after workflow map for (a), a completed role allocation matrix with named approval points for (b), documented input rules, source checks, audit logs, and escalation rules for (c), and a baseline and post-pilot indicator dashboard for (d). The in-dicator also provides each condition’s threshold: a condition is satisfied when its artifact exists and is enacted in the live workflow, not merely documented.

A workflow satisfying none or only some of the four conditions instantiates adoption, partial integration, or gov-ernance without transformation, as located in Figure 1.

Readiness is architecturally integral to AX-5R but ontologically antecedent to the re-alized AX state. It is the workflow-level entry and re-entry condition for the redesign, role, risk, and return transformation core: it determines whether a workflow is eligible to enter the transformation cycle, and it is reassessed when return feedback reveals new con-straints, drift, or capability gaps. Readiness is therefore one of the five design functions of the architecture, but it is not one of the four conditions that define an achieved AX state. Because the artifact-level ablation probe (Section 3.3 and Appendix A) evaluates imple-mentation artifacts after the AX-5R framing is supplied, it was not designed to test work-flow eligibility directly; the non-significant readiness contrast is therefore consistent with, but does not confirm, this placement of readiness as an entry condition.

The seven literature streams that inform this synthesis are summarized below.

In Table 2, the seven literature streams are translated into implementation require-ments. The table shows that each stream contributes one necessary part of AX: organizational change, effective use, human–AI role design, generative AI risk awareness, work-flow redesign, governance, and balanced performance measurement.

2.12. Positioning AX-5R Against Adjacent Frameworks: What Generative AI Changes.

The construct boundary in Table 1 separates AX from neighboring concepts. A re-viewer of any new framework, however, may still ask a sharper question: given that socio-technical systems theory, work system theory, digital transformation frameworks, AI ma-turity models, and AI governance frameworks already exist, what exactly does AX-5R add? This subsection answers that question directly, at the level of framework architecture ra-ther than concept labels.

Work system theory is the closest neighbor. Alter’s work system framework decom-poses any work system into elements, including workflows, roles, information, partici-pants, and technologies, and its granularity is deliberately close to AX-5R. The two frameworks nevertheless answer different questions. Work system theory provides the anatomy of a work system: it tells analysts what a work system consists of. AX-5R specifies the transformation control loop for one specific technology class: it tells managers in what order, under what entry conditions, with what accountability allocation, under what con-trol loops, and against what feedback evidence a generative-AI-enabled redesign should proceed. Anatomy does not entail a control loop, and a control loop is exactly what the adoption–transformation gap demands.

Generative AI motivates this control loop through five design considerations that pre-generative frameworks did not need to encode. First, diffusion is decentralized and bottom-up: employees adopt tools through natural-language interfaces before any formal implementation project exists, so adoption routinely precedes governance rather than fol-lowing it. Second, output is fluent but probabilistic: plausible error, fabricated sources, and unstable answers make verification a designed task rather than an optional courtesy. Third, the technology participates directly in cognitive work: draft-ing, summarizing, classifying, and recommending, so the accountability boundary runs inside tasks rather than between jobs. Fourth, use is prompt-mediated: sen-sitive data can leave the organization one prompt at a time, which moves input govern-ance from the IT perimeter into everyday work practice.

Fifth, capability drift: vendor-side model updates silently change system behavior, so a workflow validated once is not permanently validated, and re-validation must be looped into performance feedback. Readiness, redesign, role, risk, and return are the five capabilities that re-spectively absorb these considerations; a framework lacking any one of them leaves the corresponding consideration unmanaged.

Table 3 positions AX-5R against each adjacent framework family on these terms.

In Table 3, each adjacent framework family contributes a necessary layer, and none is displaced. The specific claim of AX-5R is integrative and executional: it is the only layer in this stack that specifies a transformation control loop at the level of the individual work-flow, with components that map onto recurring generative-AI failure modes (Section 3.2) and outputs that are implementation artifacts rather than principles, stages, or policies. This is also why five components are specified: each absorbs one of the five generative-AI design considerations above and corresponds to one recurring failure mode derived in Section 3.2, so removing a component leaves a failure mode without a designated owner, while adding a sixth would re-partition functions already assigned rather than absorb a new failure mode.

This mapping is a design argument rather than a logical necessity, and the five components do not all operate at the same level: four are embedded in implementation artifacts, whereas Readiness is the organizational entry condition that the construct definition in Section 2.11 deliberately places before the transformed state. The ablation probe in Section 3.3 tests the non-redundancy of the artifact-embedded components at the artifact level, and its results align with this division: removing any artifact-embedded component was penalized, whereas removing Readiness was not.

3. Research Method.

This study uses an integrative literature synthesis and framework design approach, supplemented by an artifact-level ablation discriminant probe. The objective is not to es-timate a causal effect, conduct a systematic review of a narrowly bounded domain, or report a company case study. Rather, the objective is to synthesize dispersed research streams into a socio-technical systems architecture for AI-enabled work system transfor-mation. This design is appropriate because AX is still an emerging construct and its rele-vant knowledge is distributed across digital transformation, information systems adoption, AI management, generative AI, workflow redesign, governance, and performance management.

3.1. Integrative Literature Synthesis.

The synthesis protocol is summarized below.

Table 4 makes the review procedure traceable. The review was not designed as a systematic review or meta-analysis; it was a purposive, theory-building synthesis aimed at identifying recurring implementation failure modes and translating them into a socio-technical systems architecture. The final corpus contains 117 sources, including peer-re-viewed conceptual and empirical studies, influential books and working papers, technical papers on the foundations of generative AI, and official standards or policy documents used as boundary-setting evidence.

A source was included when it explained technology-enabled organizational transformation; distinguished adoption, use, assimilation, or ef-fective use; analyzed AI’s effect on work, decision-making, or organizational capability; addressed generative-AI risks, oversight, accountability, or auditability; or offered performance-measurement logic relevant to implementation. Sources were excluded when they focused solely on technical model architecture without organizational implications, of-fered speculative commentary without clear conceptual value, or lacked relevance to im-plementation, governance, work redesign, or performance measurement.

3.2. Framework Derivation Logic.

The AX-5R derivation logic is summarized below.

In Table 5, the five AX-5R components are traced to recurring implementation failure modes in the literature. The table shows that AX-5R is not introduced as a mnemonic list but derived as a problem-to-requirement architecture. Readiness responds to tool access without organizational preparedness, Redesign to AI use without process-level change, Role to AI output without accountable human responsibility, Risk to experimentation without controlled scaling, and Return to productivity claims without balanced evidence.

This synthesis used problem-to-requirement logic. Readiness identifies whether the organization and workflow can absorb AI-enabled change. Redesign converts individual AI use into process-level change. Role clarifies human–AI responsibility. Risk embeds proportional oversight. Return evaluates whether the redesigned work system creates measurable value and transferable learning. In systems terms, these components corre-spond to boundary and precondition diagnosis, task-interdependence restructuring, agency and accountability allocation, control and escalation loops, and outcome feedback.

3.3. Supplementary Ablation Discriminant Probe.

As a supplementary structural test of the framework, the study ran an ablation dis-criminant probe without using human subjects. The probe is an AI-augmented structural scenario test in which controlled prompt conditions generate comparable implementation artifacts. It tests whether the full AX-5R frame produces distinguishable implementation artifacts under matched task conditions, not whether AX-5R improves organizational per-formance.

Two cross-provider language models, gpt-4o-mini and claude-haiku-4-5-20251001 (hereafter Claude Haiku 4.5), generated 252 implementation artifacts across three work-flows and seven prompt arms. The three workflows were customer-service response, in-ternal management reporting, and HR document screening. The seven arms were the full AX-5R frame, five single-component ablations that each removed one of readiness, rede-sign, role, risk, and return, and a sham five-part control consisting of a plausible but trans-formation-irrelevant five-step method.

To reduce same-provider circularity and label-conformity bias, each artifact was scored independently by both provider models. For the primary analysis, each artifact’s score was taken from the judge belonging to the provider opposite to the generator: OpenAI-generated artifacts were scored by the Anthropic model, and Anthropic-gener-ated artifacts were scored by the OpenAI model. Judges were blind to the prompt arm and to any framework name. No second artifact was placed in the judge’s context, elimi-nating pairwise position-order bias.

Each artifact was scored on five fixed implementation-quality criteria (detailed in Appendix A) and on a single holistic implementation-quality score from 0 to 10, which served as the primary outcome. The criteria captured generic implementation-quality dimensions derived from the literature synthesis rather than explicit conformity to AX-5R labels.

Because the two generators differed in baseline verbosity and scoring level, holistic scores were standardized within each generator before pooling, thereby isolating framing effects from generator-level score shifts. All contrasts were also estimated within each generator separately. For each arm, the full-versus-arm difference was summarized using Cohen’s d with 95% confidence intervals, and the Mann–Whitney test was used to assess the difference. Because the probe tested a directional discriminant expectation specified in advance (the full framework should receive higher implementation-quality ratings than ablated or sham arms), one-sided tests were pre-specified; however, all results for these contrasts are reported two-sided in Table 6, which is the more conservative convention, and the substantive conclusions are identical under either convention.

The five pre-spec-ified contrasts (the sham control and the four artifact-relevant ablations) were corrected for multiple comparisons with the Holm-Bonferroni procedure; all five remained signifi-cant after correction under both the one-sided and the two-sided convention (largest Holm-adjusted two-sided p = 0.042). The Readiness contrast is reported separately as a scope-limited contrast outside the pre-specified set for the reason given below. Per-arm descriptive statistics (means, standard deviations, 95% confidence intervals, and group sizes, both raw within each generator and pooled after within-generator standardization) are reported in Appendix A Table A2.

The cross-provider score correlation between the two providers was moderate (Pear-son r = 0.66, n = 251). As an additional check on the machine-judged scores, three human domain experts, none of whom is an author, independently rated a stratified subsample of 56 artifacts (8 per arm, balanced across generators and near-balanced across the three workflows) under the same anchored rubric, blind to arm labels and to the framework’s identity; the mean expert rating converged with the cross-provider machine scores (r = 0.78), and full inter-rater reliability and convergence results are reported in Appendix A.8.

The probe results are summarized below.

Note: n/a indicates that the readiness contrast lay outside the pre-specified family of five contrasts and was therefore not entered into the Holm–Bonferroni correction. p-values are from Mann–Whit-ney tests on the pooled standardized scores, while the confidence intervals are normal-approxima-tion intervals for Cohen’s d. Because the test is rank-based and the interval is parametric, an interval that crosses zero can accompany a significant rank-based p, as in the role contrast.

On the rank scale matched to the test, the rank-biserial correlations (bootstrap 95% confidence intervals, 10,000 resamples) are 0.88 [0.78, 0.95] for the sham contrast, 0.58 [0.39, 0.75] for redesign, 0.37 [0.15, 0.59] for risk, 0.30 [0.06, 0.53] for role, and 0.27 [0.02, 0.50] for return; all four ablation intervals exclude zero, while the readiness interval, outside the pre-specified set, does not (−0.20 [−0.44, 0.04]).

In Table 6, the full AX-5R frame received higher ratings than a sham five-part control by a large margin (Cohen’s d = 2.46, p < 0.001), which suggests that the observed difference is more consistent with the framework’s substantive content than with merely presenting five labeled sections. Removing any of the four artifact-embedded components, redesign, role, risk, or return, received lower blind-scored implementation-quality ratings (all two-sided p < 0.05), and redesign removal showed the largest rating drop (d = 1.01), consistent with workflow redesign as the mediating mechanism in Proposition 2. The artifact-em-bedded components were therefore not cost-free to remove, consistent with the configu-rational claim in Proposition 6.

In contrast, readiness behaved as an organizational entry condition rather than an artifact-quality feature: removing readiness did not lower artifact quality, and the non-significant point estimate was, if anything, slightly negative (d = −0.28, two-sided p = 0.124, meaning the readiness-removed arm scored nominally higher), con-sistent with its function as a pre-implementation organizational condition rather than an artifact-embedded design feature, so the artifact-level probe is not expected to be equally sensitive to it. The direction of the artifact-relevant contrasts was positive within both gen-erators, although the strength of individual contrasts varied by generator; the pooled within-generator standardized analysis therefore serves as the primary summary, with the per-generator contrasts reported as robustness diagnostics.

These contrasts are based on generator-standardized holistic scores with opposite-provider scoring. All five pre-specified contrasts remained significant after Holm–Bonferroni correction under the con-servative two-sided convention (largest adjusted p = 0.042), and the pre-specified one-sided analysis yields the same conclusions with smaller p-values. Full descriptive statistics for every arm appear in Appendix A Table A2.

3.4. Evidence Boundary and Validity Limits.

The study’s evidence burden is conceptual coherence, the literature coverage, con-struct boundary clarity, failure-mode derivation, practical usability, worked application logic, a supplementary artifact-level probe, and testable propositions. AX-5R has not yet been field-validated against organizational performance outcomes. The workflow vi-gnettes and the ablation discriminant probe provide artifact-level evidence rather than field- or organizational-level performance evidence. Later empirical work should examine whether AX-5R predicts AX performance across firms, industries, tasks, and regulatory settings.

To state the evidential status of each claim type precisely: the construct boundary (Section 2.11) and the framework architecture (Section 4) rest on the literature synthesis; the non-redundancy of the artifact-embedded components rests on the ablation discrimi-nant probe, which is artifact-level, model-generated, and model-judged evidence with an added human-expert-rating check (Appendix A.8); and the six propositions are testable claims whose organizational validation is the research agenda of Section 5.8, not a result of this paper. The probe was designed so that it could fail: a sham control with identical surface form provided the discriminant test, and one component (readiness) indeed showed no artifact-level penalty, an outcome the framework accommodates by assigning readiness to the organizational entry stage rather than to artifact-embedded design.

A fur-ther interpretive limit follows from the design itself: four of the five scoring criteria are intentionally aligned with the artifact-embedded components, so lower scores under ab-lation partly reflect that definitional alignment rather than an independent validity test. The contrasts that carry inferential weight are therefore the sham control, which shows that a plausible but transformation-irrelevant five-part scaffold does not earn equivalent ratings, and the human expert layer (Appendix A.8), which checks that the machine-scored separation is not an artifact of model judging. No claim in the manuscript asserts field-validated effects on organizational performance.

Except for Table 6 and Appendix A Table A2, all figures and tables in the manuscript are author-developed conceptual and implementation artifacts derived from the literature synthesis. Table 6 is presented as a pre-specified, artifact-level check of prompt-structure fidelity and internal consistency, scored by independent cross-provider model judges, not as mechanism or validity evidence at the organizational level.

4. AX-5R Implementation Framework.

The AX-5R framework theorizes AI transformation as an iterative socio-technical work-system architecture. AI tools create organizational value only when organizations assess readiness, redesign work, allocate roles, govern risk, and measure value and learn-ing. These moves correspond to five recurring failure points: access without preparedness, use without process change, output without accountability, experimentation without con-trolled scaling, and activity without value evidence. AX-5R is therefore not only a process model but a socio-technical work-system architecture for converting fragmented AI use into redesigned, accountable, governable, and measurable work systems. The overall se-quence is as follows:

AI tool adoption → workflow-specific readiness → workflow redesign → human–AI role clarity → proportional risk control → balanced return measurement → scalable AX performance.

The order is sequential but not strictly linear. Role ambiguity, risk exposure, performance evidence, and implementation learning can feed back into readiness diagnosis and workflow redesign.

In Figure 3, AX-5R is shown as a readiness-gated transformation cycle rather than a set of five parallel components. Readiness sits outside the internal cycle as an entry and re-entry gate that determines whether a workflow is eligible to proceed. Within the gate, redesign, role, risk, and return form the transformation core: redesign restructures the workflow, role allocates human and AI authority, risk sets proportional oversight, and return supplies performance and learning feedback that re-enters readiness and redesign.

The figure also locates the six propositions: Proposition 1 governs entry at the gate; Prop-osition 2 is the mediation path from adoption through redesign to AX performance; Prop-ositions 3 and 4 are moderation relationships acting on that path; Proposition 5 is the return feedback loop; and Proposition 6 is the configurational fit that binds the four trans-formation functions.

4.1. Readiness.

Readiness is the organization’s workflow-specific preparedness to use AI responsibly and effectively. It includes data, process, employee, leadership, measurement, and gov-ernance readiness. The key question is not whether AI access exists, but whether a work-flow has the documents, review expertise, baseline measures, data rules, and policy ma-turity needed to absorb AI use into routines. Readiness prevents premature scaling by identifying workflows where data are unavailable, review standards are weak, accounta-bility is unclear, or baseline outcomes cannot be measured. In systems terms, readiness defines the boundary and preconditions of AI-enabled transformation.

4.2. Redesign.

Redesign means changing the workflow rather than adding AI to an unchanged pro-cess. It maps the current and AI-supported process, specifies what AI produces, what ev-idence it uses, who reviews it, what criteria apply, and where approval occurs. By decom-posing work into retrieval, summarization, drafting, classification, comparison, recom-mendation, approval, execution, and documentation, redesign converts a vague use case into a work architecture. It is the central mediating mechanism through which AI adoption progresses to AX rather than mere tool use. In systems terms, redesign restructures task interdependencies.

4.3. Role.

Role refers to the allocation of task authority, review responsibility, and accountabil-ity between humans and AI systems. AI may retrieve, summarize, draft, classify, recom-mend, or execute, but higher autonomy requires stronger oversight. AX separates task authority from accountability authority: AI may draft or recommend, while humans remain accountable for review, exception handling, approval, and stakeholder conse-quences. Role clarity moderates redesign effectiveness by reducing ambiguity about when to trust, verify, override, escalate, or document AI output. In systems terms, role allocates agency and accountability.

4.4. Risk.

Risk refers to the control architecture governing inputs, outputs, processes, and inci-dents as AI exposure scales. Input controls specify permissible data, output controls spec-ify review requirements, process controls specify logging, and incident controls specify escalation. Risk control is proportional: internal summaries may need light-source checks, customer-facing drafts may need stronger review, and high-impact legal, financial, em-ployment, medical, safety, or regulated decisions may require strict human accountability or exclusion from automation. This embeds governance into the work routine rather than leaving it as a policy document. In systems terms, risk establishes control and escalation loops.

4.5. Return.

Return means measuring whether the redesigned workflow creates value and gener-ates transferable learning. It is broader than financial ROI and includes efficiency, quality, human capability and burden, risk reduction, customer or stakeholder impact, and organ-izational learning. Baseline and post-redesign indicators allow managers to scale, revise, or stop a use case. A pilot that saves time but increases errors needs redesign; a pilot that creates local productivity but no transferable learning remains an act of adoption rather than transformation. In systems terms, return supplies performance feedback and learn-ing.

4.6. Interdependence of the Five Components.

The five components are mutually reinforcing and systemically interdependent. Readiness without redesign remains diagnosis; redesign without role clarity creates ac-countability gaps; role clarity without risk control may fail in high-stakes settings; risk control without return measurement may become bureaucracy; and return measurement without readiness and redesign may capture local tool effects rather than transformation. AX-5R therefore treats scalable AX performance as configurational alignment across the four transformation functions once minimum readiness conditions are met, not as excel-lence in any single component.

In systems terms, each component performs a distinct function. Readiness defines system boundaries and preconditions; redesign restructures task interdependencies; role allocates agency and accountability; risk establishes control and escalation loops; and re-turn supplies performance feedback and learning. AX-5R is therefore a functional socio-technical architecture for AI-enabled work system transformation rather than a checklist of adoption factors.

Interdependence does not mean harmony. The five components generate managed tensions, and naming them is part of the framework’s realism. The sharpest is between risk and redesign: control slows redesign agility, and redesign pressure erodes control discipline. AX-5R does not dissolve this tension; it prices it through proportionality, so that low-stakes workflows buy agility with light controls while high-stakes workflows deliberately trade speed for accountability (Section 6.3). A second tension runs between return and experimentation: premature measurement can kill exploratory pilots, while unmeasured pilots never earn the right to scale; the baseline-then-pilot sequencing in Phase 2 exists to stage this trade-off rather than deny it.

A third tension links readiness and momentum: readiness gates protect the organization, but can become a veto point that starves transformation of early wins. These tensions are not design flaws. They are the reason configurational alignment (Proposition 6) is the framework’s success criterion: an aligned configuration is one in which each tension has been explicitly priced for that workflow’s stakes, rather than resolved by default in favor of speed or of control.

5. Implementation Artifacts and Propositions.

This section translates the AX-5R conceptual systems architecture into implementa-tion artifacts and testable propositions. The artifacts are not presented as field-validated instruments or universal managerial checklists. Rather, they operationalize the systems functions specified in Section 4: boundary and precondition diagnosis, task-interdepend-ence restructuring, agency and accountability allocation, control and escalation loops, and outcome feedback. Their purpose is to show how the AX-5R architecture can be applied to use-case selection, human–AI role allocation, risk control, performance measurement, staged scaling, and future empirical research.

5.1. AI Adoption Versus AX.

In Table 7, AI adoption and AI transformation are separated by their respective units of analysis and managerial logics. Adoption remains centered on tool access and local use, whereas AX requires redesigned workflows, explicit oversight, and a performance system that can distinguish activity from organizational value.

5.2. Use-Case Selection.

In Table 8, AX use-case selection is framed as a managerial screening problem. The table recommends beginning with frequent, document-based, reviewable, measurable, and lower-risk workflows because these characteristics enable comparison of baseline and post-redesign performance while controlling implementation risk.

5.3. Human–AI Role Allocation.

In Table 9, human–AI role allocation is organized by increasing AI task authority. The table makes explicit that the human role changes as AI moves from retrieval and summarization to drafting, recommendation, and bounded autonomous action, but ac-countability does not disappear; it must be reassigned, reviewed, or restricted.

In Figure 4, human–AI role allocation is linked to proportional risk governance. AI exposure is defined as the combined level of AI autonomy and task consequentiality, while the vertical axis represents the strength of human accountability and oversight. Low-exposure assistive work, such as retrieval or summarization, can be governed through light but explicit review. As use cases move toward recommendation, decision support, agentic execution, or high-impact tasks, they must move upward toward stronger accountability, approval, auditability, and escalation. The scalable AX zone is reached only when increasing AI exposure is matched by proportionate human account-ability and oversight. Otherwise, high-exposure AI use becomes shadow AI: technically capable but poorly governed.

5.4. Risk Control Checklist.

In Table 10, AI governance is operationalized as concrete controls rather than abstract principles. The table links each risk area to a control question and a practical artifact, al-lowing managers to embed oversight into everyday workflow design instead of treating governance as a separate compliance layer.

5.5. Performance Indicators.

In Table 11, AX performance is measured through a balanced set of indicators. The table prevents a narrow interpretation of return as time savings alone by adding indica-tors of quality, customer, human, risk, and learning that can reveal whether AI-supported work is genuinely improving or merely becoming faster. In systems terms, return indica-tors function as feedback signals that determine whether a redesigned workflow should be scaled, revised, or stopped.

5.6. Three-Phase AX Implementation Roadmap.

The AX-5R framework can be translated into a three-phase roadmap: diagnose, re-design, and scale. This roadmap is designed for managers who need to move from an abstract AI strategy to a concrete implementation.

Phase 1 is diagnosis. The organization maps current AI usage, informal practices, candidate workflows, data sensitivity, high-stakes decisions, regulated activities, and ac-countability gaps. The output is an AX opportunity and risk map that separates low-risk, high-value opportunities from those that require stronger governance.

Phase 2 is redesign. The organization selects a small number of workflows. It speci-fies the current process, AI-supported process, review point, approval rule, data input rule, role level, and performance measure for each workflow. Training should cover work-flow logic, data boundaries, review standards, escalation rules, and documentation, not only prompt writing.

Phase 3 is scaling, and it is a governed transfer process rather than replication. Scaling should occur only after baseline indicators show whether the redesigned workflow re-duced cycle time, improved quality, reduced burden, increased stakeholder value, and avoided unacceptable risk. A validated pattern is then transferred through four adapta-tion steps. First, an eligibility test: the candidate workflow must belong to the same task family (for example, retrieval-summarize-draft-review), and its data sensitivity and deci-sion stakes must be equal to or lower than those of the validated workflow; a higher-stakes candidate re-enters Phase 1 rather than inheriting validation. Second, risk-tier reclassifi-cation: controls are not copied but re-proportioned, since the same pattern may need source checks only in one workflow and named approval in another.

Third, artifact adap-tation: data input rules, role boundaries, escalation thresholds, and performance baselines are re-specified for the receiving workflow, with the receiving team, not the originating team, accountable for the adapted artifacts. Fourth, incident-lesson import: known failure modes and near misses from the validated workflow are written into the receiving work-flow’s controls before launch. A lightweight AX playbook preserves approved use cases, prohibited uses, workflow maps, role levels, risk controls, performance templates, and the lessons learned from accumulated incidents, making each subsequent transfer safer than the first.

The roadmap is summarized below.

In Table 12, the AX-5R framework is translated into a three-phase implementation roadmap. The table shows how managers can move from diagnosis to redesigned work and then to scaling, while keeping each phase tied to observable outputs rather than gen-eral AI enthusiasm.

This roadmap reinforces the central claim: AX begins with diagnosis of work, ma-tures through redesigned workflows, and scales through measured outcomes, reusable artifacts, and governance routines.

5.7. Worked Workflow Vignettes.

The following vignettes are illustrative application probes, not case-study evidence. They show how AX-5R converts AI enthusiasm into redesignable work systems.

In customer-service responses, adoption begins when agents use AI to draft replies. AX begins when the workflow is decomposed into intake, issue classification, policy re-trieval, draft response, human review, and final send. AI supports retrieval and drafting; humans verify facts, tone, policy fit, and the need for escalation. Risk controls restrict sensitive data, require source checks, and route high-risk complaints to supervisors. Re-turn is measured through cycle time, first-contact resolution, correction rate, escalation rate, satisfaction, and reviewer burden. The transformation is not AI drafting itself, but the redesign of intake, retrieval, review, escalation, and measurement.

In internal management reporting, adoption begins when analysts use AI to summa-rize meeting notes, sales reports, or operating data. AX begins when the reporting work-flow is redesigned around evidence traceability: data collection, retrieval, synthesis, draft generation, managerial review, and decision-log update. AI supports retrieval, summari-zation, and first drafts, while analysts remain accountable for source validation, interpre-tation, exceptions, and final recommendation. The return includes reporting time, factual corrections, decision-cycle speed, and template reuse. The transformation lies in evidence traceability and decision-log integration, not just faster report writing.

In HR document screening or compliance review, adoption begins when staff use AI to summarize resumes, policies, contracts, or training records. AX restricts AI to retrieval, summarization, and checklist preparation, while humans retain criteria interpretation, fairness review, final judgment, and exception handling. Risk controls require approved data handling, bias review, audit logs, approval gates, and prohibition of autonomous high-stakes decisions. Return includes review consistency, processing time, correction rates, workload, and compliance incidents. The transformation lies in restricting AI to as-sistive roles while preserving human judgment, fairness review, and auditability.

Across the three vignettes, the diagnostic questions remain stable: Is the workflow ready? What process changes? What role does AI play? Who remains accountable? What risks are controlled? What outcomes distinguish transformation from usage?

5.8. Propositions: Toward a Research Agenda for AX as Work System Transformation.

The AX-5R framework is intended as a practical implementation model, but it can also support a focused research agenda. The propositions below translate the framework into six testable claims. They are deliberately stated at a level that can be examined through case studies, surveys, field experiments, longitudinal process research, or config-urational analysis.

The six propositions are not equally urgent for initial validation. Empirical priority belongs to Propositions 2 and 6 for three reasons. First, they carry the framework’s two load-bearing claims: redesign as the mediating mechanism and configuration as the unit of success. If either fails, the framework requires revision rather than refinement, making them the fastest routes to falsification. Second, both already have artifact-level initial evi-dence from the ablation probe (the largest single-component drop for redesign and the non-redundancy of the artifact-embedded components), so field studies can be designed to test a stated effect direction.

Third, they discipline the remaining propositions: Propo-sition 1 specifies the entry condition that determines the admissible set of workflows in which the other propositions operate; Propositions 3 and 4 are moderation claims that presuppose the mediation structure of Proposition 2; and Proposition 5 specifies the feed-back mechanism through which return evidence re-enters readiness and redesign. A prac-tical sequence is therefore entry and eligibility (Proposition 1), mediation (Proposition 2, longitudinal or two-wave designs), configuration (Proposition 6, qualitative comparative analysis across workflows), moderators (Propositions 3 and 4, field experiments), and feedback (Proposition 5, throughout).

5.8.1. Workflow-Specific Readiness.

Organizations often begin AI initiatives by selecting tools. However, the literature on digital transformation and IS assimilation suggests that access to tools does not guarantee organizational value. Readiness is multidimensional and workflow-specific. It includes leadership commitment, employee capability, data availability, workflow clarity, governance maturity, and measurement capability. Ecosystem-level analyses of AI value chains converge on the same point from the supply side: workforce readiness and mana-gerial competency, rather than technical capacity, are the binding constraints on AI value realization. A firm may be ready for internal document summarization but not ready for customer-facing advice, legal interpretation, or personnel evaluation.

Proposition 1. Workflow-specific readiness, assessed at the workflow rather than solely at the firm or tool level, increases the likelihood that an adopted generative-AI use case enters the AX trans-formation cycle.

Its downstream relationship with AX performance operates through the transfor-mation core specified in Propositions 2 to 5.

5.8.2. Workflow Redesign as the Mediating Mechanism.

Existing digital transformation and process-redesign research repeatedly shows that value arises when technologies reshape organizational processes. Genera-tive AI is no exception. AI tools may increase individual speed, but organizational trans-formation depends on whether the workflow itself changes. A simple binary adoption variable is therefore insufficient. Researchers should measure whether the process map changed, whether review points were added, whether tasks were reallocated, and whether performance indicators were revised.

Proposition 2. Workflow redesign mediates the relationship between generative AI adoption and AX performance.

5.8.3. Human–AI Role Clarity.

AI transformation creates ambiguity because AI systems can generate outputs that appear to be work products. If AI drafts a report, recommends a vendor, summarizes a regulation, or classifies a customer request, responsibility can become unclear. Role clarity specifies what AI may do, what humans must verify, who approves final outputs, and which cases require escalation. This role logic is consistent with research on human–AI decision structures, reliance on automation, and conjoined agency.

Proposition 3. Human–AI role clarity positively moderates the relationship between workflow redesign and AX performance by reducing accountability ambiguity, overreliance, and avoidance of useful AI support.

5.8.4. Proportional Risk Governance.

Governance is often treated as a constraint on innovation. In AX, however, appropri-ate oversight can enable scaling. Without oversight, managers may hesitate to expand AI use because risks remain unclear. With proportional oversight, firms can distinguish low-risk use cases that should be encouraged from high-risk use cases that should be restricted, consistent with risk-based and management-system approaches to AI governance.

Proposition 4. Proportional risk governance strengthens the relationship between workflow rede-sign and AX performance by reducing uncontrolled exposure without blocking low-risk experi-mentation.

5.8.5. Balanced Return Measurement.

Many AI initiatives are justified by expected time savings, but time savings are only one part of AX value. A workflow may become faster but less accurate. A team may pro-duce more documents but require more review. A customer support process may respond faster but escalate more complaints. Therefore, return must be measured across efficiency, quality, human, risk, customer, and learning indicators, consistent with balanced performance and IS-success measurement traditions.

Proposition 5. Balanced AX performance measurement increases the likelihood that evidence from AI pilots feeds back into workflow readiness and redesign, enabling scalable organizational learning rather than isolated productivity anecdotes.

5.8.6. Configurational Alignment and Strategic Consequence.

The five components of AX-5R are mutually reinforcing, but they do not operate at the same level of analysis. Readiness primarily serves as a workflow entry and a precon-dition diagnosis, whereas redesign, role, risk, and return are more directly embedded in implementation artifacts. The five components fail in chained ways when developed in isolation, as elaborated in Section 6.1; this chaining is what makes AX performance con-figurational rather than additive: at the workflow or workflow-portfolio level, and condi-tional on meeting minimum readiness conditions, firms need alignment across the four transformation functions rather than excellence in one component alone.

Proposition 6. Among workflows meeting minimum readiness conditions, AX performance is higher when redesign, role, risk, and return form an internally coherent configuration fitted to the workflow’s task, risk, expertise, and regulatory conditions than when any one transformation func-tion is developed in isolation.

Alignment denotes coherent fit among the four transformation functions under the workflow’s boundary conditions; it does not require equal component maturity or iden-tical design weights across workflows.

Proposition 6 gains empirical content when the space of configurations is made ex-plicit. Three archetypal configurations, defined by which components carry design weight under which boundary conditions, illustrate the testable structure. Table 13 states them as predictions, not findings.

The archetypes yield comparative predictions that configurational methods can test directly: velocity-first adopters should show the largest gap between usage metrics and transformation outcomes; guarded pilots should show the smallest incident rates but the longest time to value; and balanced transformers should dominate both on sustained AX performance. Configurations, not component scores, are the predicted unit of success, which is what distinguishes Proposition 6 from a maturity claim. The balanced-trans-former row functions as the reference configuration implied by Proposition 6 itself; the discriminating predictions are carried by the two unbalanced archetypes and the corollary.

The strategic implication is deliberately framed as a research program claim rather than an empirical finding. The paper does not claim that AX-5R has already been field-validated as a source of durable competitive advantage; rather, it specifies a testable higher-order capability that future studies can examine to explain why firms with similar AI tools differ in AI value realization.

6. Discussion.

6.1. Contributions to Systems-Based AI Transformation.

AX-5R is not proposed as another maturity checklist. It specifies a minimum socio-technical systems architecture for AI-enabled work transformation: boundary and pre-condition diagnosis (readiness), task-interdependence restructuring (redesign), agency and accountability allocation (role), control and escalation loops (risk), and outcome feed-back and learning (return). In doing so, AX-5R operationalizes the joint-optimization prin-ciple of socio-technical systems theory for the generative-AI context. Fragmented AI use becomes accountable, governable, and measurable transformation only when the tech-nical artifact, work structure, human role system, governance controls, and feedback mechanisms are redesigned together.

The paper makes four cumulative increments. First, it defines the boundary between AI adoption and AX at the work-system level rather than treating transformation as tool use, firm-level aspiration, or governance compliance. Second, it specifies the mechanism of conversion: redesign mediates the path from adoption to AX performance, making Proposition 2 the process-theoretic core. Third, it specifies the systems condition of success: conditional on readiness, redesign, role, risk, and return must form an internally coherent configuration, making Proposition 6 the configurational core.

Fourth, it renders these claims observable through workflow maps, role protocols, control artifacts, balanced dashboards, and a supplementary discriminant probe, so that the contribution is a readiness-gated socio-technical architecture with an explicit mechanism, configuration logic, and observable implementation traces rather than another list of adoption factors.

The configurational claim in Proposition 6 is therefore, itself, a systems property. The four transformation functions are interdependent, and readiness gates their operation, so strengthening one in isolation leaves the larger work system underspecified; the chained failure modes named in Section 4.6 are the concrete form of this interdependence. AX per-formance therefore depends on the coherent configuration of these functions under a sat-isfied readiness gate rather than on excellence in any single component.

AX-5R operationalizes the joint-optimization principle of socio-technical systems theory in a specific way. The five Rs should not be assigned one-to-one to either the tech-nical or the social subsystem, because doing so would reproduce the very separation that socio-technical theory warns against. Instead, each R is a coupling mechanism: it names an interface at which the technical and social subsystems must be jointly designed. Read-iness tests whether technical feasibility and social absorptive and review capacity jointly permit a workflow to enter or re-enter the cycle. Redesign jointly restructures AI af-fordances and task interdependencies. Role couples system autonomy with human au-thority and accountability. Risk couples technical controls with organizational oversight and escalation.

Return couples technical performance signals with social value and learn-ing, feeding evidence back into reconfiguration and renewed readiness assessment. Table 14 states these five interfaces explicitly.

Observable implementation artifacts are summarized in Table 5 and elaborated in Section 5.

In Table 14, AX-5R is recast in systems terms: the five components are not a checklist but the functional architecture, that is, diagnosis, restructuring, allocation, control, and feedback, that a work system requires in order to convert AI tool use into governed trans-formation.

This positioning locates AX-5R within systems and socio-technical traditions that hold that technology creates organizational value only when social and technical subsys-tems are jointly optimized and redesigned. Generative AI sharpens this classic problem because adoption is now inexpensive, decentralized, cognitive, probabilistic, and often informal. The binding constraint on transformation has therefore shifted from tool access to the disciplined reconfiguration of the work system. AX-5R specifies the imple-mentation architecture for meeting that constraint.

Read against the adjacent framework families in Table 3, the contributions can be stated comparatively. Where socio-technical and work system theory establish that joint optimization is necessary, AX-5R specifies the order and artifacts through which it is executed in generative AI. Where digital transformation research locates value in or-ganizational change rather than deployment, AX-5R converts that thesis into workflow-level implementation moves. Where maturity models diagnose stages, AX-5R provides the mechanism for movement between stages. Where governance frameworks specify control obligations, AX-5R positions those controls inside the re-design cycle so that control and value creation are co-designed.

The ablation probe adds artifact-level discriminant evidence that this integration is substantive rather than nomi-nal: a sham five-part scaffold of equal form but no transformation content scored far lower than the full frame (Table 6).

6.2. Boundary Conditions.

The AX-5R framework is designed for general business organizations, especially knowledge-intensive and administrative workflows, but its application depends on con-text. Several boundary conditions should be recognized.

First, task risk matters. Low-risk knowledge work, such as internal summarization, drafting, and document search, can often be performed earlier than high-stakes decision work. Legal, medical, financial, employment, safety, and public-sector decisions require stronger governance and may not be appropriate for early AX experimentation.

Second, data sensitivity matters. Workflows involving confidential, personal, regu-lated, or proprietary information require input controls and approved tools. A workflow may be technically suitable for AI but governance-ineligible because the data cannot be safely used.

Third, domain expertise matters, and the framework treats it as a designed input ra-ther than an assumption. AI outputs can only be reviewed effectively when reviewers can judge output quality; where expertise is thin, human oversight becomes symbolic rather than substantive. AX-5R responds at three points. In readiness, reviewer capability is an explicit entry criterion: a workflow whose outputs people cannot evaluate is not ready for AI-enabled redesign at that autonomy level, regardless of tool quality. In role, the alloca-tion matrix must name a competent approver, not merely an available one. Where none exists, the workflow is restricted to lower AI autonomy, sampled external review, or ex-clusion from automation. In return, correction and escalation rates function as a running indicator of whether the review is substantive.

Expertise scarcity, therefore, narrows the admissible configuration space rather than silently weakening oversight, and it is the sub-stantive condition behind the predicted weakening of Proposition 3’s moderation in low-expertise contexts (Table 13).

Fourth, organizational culture matters. AX requires employees to experiment, report errors, share reusable practices, and accept redesigned roles. If employees fear replace-ment or punishment, AI use may become hidden, performative, or resisted.

Fifth, firm size matters. Large firms may have formal governance teams, legal depart-ments, audit functions, and AI centers of excellence. Smaller firms may need simpler checklists and lightweight controls. AX-5R can be adapted to both, but the implementation artifacts should differ in complexity. Integrative evidence on SME digital transformation supports this differentiated design expectation.

Sixth, the regulatory environment matters. Organizations in highly regulated sectors must align AX with legal and compliance requirements. In such contexts, role and risk should be developed before aggressive scaling, and some high-impact uses may need to remain outside automation.

These boundary conditions do not invalidate AX-5R; they parameterize the proposi-tions. Task risk and data sensitivity shift the design weight toward role and risk, thereby setting the governance intensity at which Proposition 4’s moderation should peak. Re-viewer domain expertise is the substantive condition behind Proposition 3: where exper-tise is thin, role clarity on paper cannot deliver accountable review, so the moderation is predicted to weaken (Table 13’s corollary row). Culture and firm size determine whether redesigned routines are reported and stabilized, which conditions the mediation path of Proposition 2. The regulatory environment fixes which configurations are even admissible, restricting the configuration space over which Proposition 6 operates. Each boundary con-dition is therefore not a caveat appended to the framework.

Still, a moderator or scope condition is attached to a specific proposition, and Table 13 states the resulting configura-tion-level predictions.

6.3. Implications for Practice.

The central practical implication is that AX is not a technology rollout. It is realized through an organizational execution system. Generative AI lowers the cost of adoption, but it does not lower the difficulty of transformation. In fact, easy adoption may increase the difficulty of transformation because AI use can spread before workflows, responsibil-ities, controls, and measures are ready.

AX-5R provides a way to manage this tension. Readiness prevents premature scaling. Redesign prevents superficial tool insertion. Role prevents accountability ambiguity. Risk prevents uncontrolled exposure. Return prevents unmeasured enthusiasm. Together, the five components translate scattered AI use into an organizational learning cycle.

The framework also avoids two symmetrical errors. The first is automation maximal-ism: applying AI wherever possible without adequate oversight. The second is govern-ance paralysis: blocking useful low-risk applications because high-risk cases are difficult. AX requires differentiated governance. Low-risk use cases can be encouraged with light but explicit controls. High-risk use cases require strict human approval, auditability, es-calation paths, or exclusion from automation.

For practitioners, the sequence is direct. Begin with a readiness diagnosis. Select a reviewable workflow. Map the current process. Insert AI only where it supports retrieval, summarization, drafting, classification, comparison, or recommendation. Define human review and approval. Add proportional risk controls. Measure baseline and post-pilot outcomes. Use performance, risk, and learning evidence to scale, revise, or stop the use case.

6.4. Future Research Agenda.

The framework opens several research directions.

First, researchers can operationalize workflow-specific AX readiness. Current AI adoption studies often measure tool use or intention to use. Future studies should meas-ure workflow clarity, governance maturity, data availability, review expertise, employee AI literacy, and measurement capability.

Second, researchers can study workflow redesign as a mediator. Empirical work should examine whether AI adoption improves performance directly or whether the effect is mediated by process redesign. This would move AX research beyond simple adoption-performance models.

Third, researchers can examine human–AI role clarity. Field experiments could com-pare teams with identical AI tools but different role protocols. Outcome measures could include output quality, error rates, employee confidence, escalation behavior, and clarity of accountability.

Fourth, researchers can study proportional governance. Rather than asking whether governance helps or hinders innovation, studies can examine which governance intensity fits which risk level. This would produce a more nuanced AI governance theory.

Fifth, researchers can develop AX implementation maturity models. Such models should avoid generic maturity scoring and instead assess whether, once minimum readi-ness conditions are met, redesign, role, risk, and return are coherently aligned at the work-flow or workflow-portfolio level.

Sixth, researchers can investigate industry-specific adaptations. The framework can be applied to healthcare, finance, education, manufacturing, logistics, professional ser-vices, and public administration. Each context will require different role boundaries, risk controls, auditability requirements, and return indicators.

Seventh, researchers can study the long-term strategic consequences of AX imple-mentation capability. As generative AI tools become widely available, the advantage may shift from tool possession to organizational capability in redesigning and governing AI-enabled work.

Eighth, researchers can develop AI-augmented validation protocols for AX. Such protocols should not ask language models whether AX-5R is correct or treat LLM agree-ment as validation. Instead, they should use AI systems to generate controlled scenario variants or document-grounded workflow artifacts, then evaluate those artifacts through pre-specified human, expert, or rule-based adjudication. This would let researchers stress-test boundary conditions while avoiding the error of treating fluent AI output as empirical evidence.

The research agenda is summarized below.

In Table 15, the conceptual framework is converted into an empirical research agenda. The table identifies testable themes, example research questions, and possible methods to help AX-5R move from conceptual synthesis to cumulative empirical validation.

Taken together, these directions define AX not as a finished instrument to be accepted or rejected, but as a cumulative research program for diagnosing, redesigning, governing, measuring, and scaling AI-enabled work systems.

7. Conclusions.

Generative AI adoption has become easier with decentralized access; however, AI transformation remains difficult because work systems must still be redesigned. This pa-per argued that the central challenge is not access to generative AI tools but the organizational ability to redesign work, allocate responsibility, govern risk, and measure outcomes. The proposed AX-5R framework integrates readiness, redesign, role, risk, and return into a socio-technical systems architecture for AI-enabled work system transformation.

The article contributes to systems-based AI transformation research by synthesizing seven literature streams into a single AX execution architecture and by explicitly deriving the failure modes of AX-5R. Its central novelty is to specify how fragmented AI tool use can be converted into redesigned, accountable, governable, and measurable organizational work systems. It contributes to practice by providing implementation artifacts, in-cluding diagnostic maps, role-allocation tools, risk controls, performance indicators, and workflow vignettes, that managers can use to move from pilots to scalable transformation.

The evidence boundary is explicit. The framework’s derivation is conceptual and in-tegrative; the ablation probe and expert-rating check provide artifact-level discriminant evidence for the non-redundancy of the artifact-embedded components; and organizational performance validation remains ahead, through the case, survey, experimental, configurational, and longitudinal designs specified in Section 5.8 and Table 15. Within that boundary, the framework already does the work this genre requires: it converts the fragmented literature into a single, falsifiable execution architecture with observable arti-facts.

The practical message is direct. Organizations should not ask only whether employ-ees are using generative AI. They should also ask whether AI use has changed the work system in a governed and measurable way. If the workflow is unchanged, the role bound-ary is unclear, the risk control is informal, and the return measure is anecdotal, the organ-ization has adopted AI but has not undergone transformation. If those elements are rede-signed together, adoption progresses to transformation. AX-5R specifies the minimum so-cio-technical architecture for executing, governing, and measuring that conversion.

validation, K.S.S. and I.S.K.; formal analysis, K.S.S.; investigation, K.S.S.; resources, K.S.S.; data cu-ration, K.S.S.; writing of the original draft, K.S.S.; writing, review, and editing, K.S.S., I.S.K. and M.L.; visualization, K.S.S. and M.L.; supervision, K.S.S.; project administration, K.S.S. All authors have read and agreed to the published version of the manuscript.

Funding: This research received no external funding.

Institutional Review Board Statement: The ablation discriminant probe analyzed only language-model-generated artifacts and did not involve human participants or animals. In the expert-rating check (Appendix A.8), three domain experts served as professional evaluators of these artifacts ra-ther than as research subjects: no intervention was performed; the raters’ names and completion dates were recorded on the returned scoring sheets for administrative purposes only and were pseu-donymized before analysis; no other personal or sensitive data were collected, no data about the raters themselves were analyzed, and their ratings are reported only in anonymized, aggregate form.

Ethical review and approval were therefore not required in accordance with Article 2 of the Bio-ethics and Safety Act of the Republic of Korea and Article 2 of its Enforcement Rule, which define human subjects research as research involving physical intervention in a person, interaction to collect data about a person, or the use of personally identifiable information for research purposes, none of which applies to this expert evaluation of study materials.

Author Contributions: Conceptualization, K.S.S. and I.S.K.; methodology, K.S.S.; software, K.S.S.;

Informed Consent Statement: All three expert raters were informed in writing of the rating task, the blinding design, and the aggregate-only reporting of their ratings, and participated voluntarily on that basis.

Data Availability Statement: The data and analysis code for the ablation discriminant probe re-ported in Section 3 and Appendix A are openly available in Zenodo at the linked source. The data and documentation are released under a Creative Commons Attribution 4.0 license, and the analysis code under the MIT License. The archive contains the 252 generated artifacts, the independent cross-provider judge scores, the reported analysis output, the analysis scripts (which include the generation prompts and scoring rubric), and the expert-rating materials (rater instructions, the blinded artifact subsample, the reliability and convergence analysis script, and the aggregate expert-rating results). Individual expert ratings are not deposited, consistent with the aggregate-only reporting to which the raters agreed.

The machine-scored results, including the effect sizes and the cross-provider score correlation (r = 0.66) in Table 6, are reproducible from the included data without API calls.

Acknowledgments: Use of Generative AI: Generative AI language models were used in two distinct capacities, disclosed here for transparency. First, as a study object and measurement instrument: the ablation discriminant probe (Section 3 and Appendix A) used gpt-4o-mini and claude-haiku-4-5-20251001 to generate the implementation artifacts that constitute the study’s data, which were then scored by independent cross-provider model judges under a fixed rubric. Second, as a writing aid, generative AI tools assisted with language editing and formatting. All intellectual content, analytical decisions, interpretations, and conclusions are the authors’ own. No AI system is listed as, or qual-ifies as, an author, consistent with the publisher’s authorship policy. The authors reviewed, edited, and take full responsibility for all AI-assisted text and all interpretations reported in the manuscript.

Conflicts of Interest: The authors declare no conflicts of interest.

Appendix A. Ablation Discriminant Probe Protocol

Appendix A.1. Purpose and Evidence Boundary

The ablation discriminant probe is a supplementary artifact-level test, an AI-aug-mented structural scenario probe. It does not use human subjects, observe firms, or meas-ure organizational performance. It tests whether implementation artifacts generated un-der the full AX-5R frame receive higher implementation-quality ratings than artifacts gen-erated under single-component ablation frames and a sham five-part frame of equal form but no transformation content. The unit of analysis is the generated artifact, not a person, team, firm, or field outcome. Accordingly, the probe is not treated as a field validation of AX-5R, but rather as a controlled artifact-level test to determine whether the framework produces distinguishable implementation outputs under matched task conditions.

Appendix A.2. Generators and Design

Two cross-provider language models served as generators: gpt-4o-mini and claude-haiku-4-5-20251001. Each generated artifacts for the three workflow contexts under seven arms, with six repetitions per generator-workflow-arm cell, producing 252 artifacts in to-tal.

The same output constraints were used across conditions: 350 to 500 words, concise headings and bullets where helpful, enough operational detail for a manager to turn the output into a pilot checklist, no invented company names, no budgets, and no software vendor claims.

Appendix A.3. Workflow Contexts

The customer service workflow involved a mid-sized online retailer receiving prod-uct questions, refund requests, shipping complaints, and warranty claims. The manage-ment-reporting workflow concerned a business unit preparing monthly operating reports from spreadsheets, meeting notes, project trackers, and prior reports. The HR document-screening workflow covered promotion packets, job descriptions, training records, and employee development documents, with final employment-related decisions remaining human.

Appendix A.4. Arms

The full AX-5R arm asked for a plan built around readiness, redesign, role, risk, and return, working together. Each of the five ablation arms requested a plan built around four components, with one component removed and unnamed. The sham control used the PLANT method (Procure, Localize, Announce, Network, Tally). This plausible but transformation-irrelevant five-step structure does not encode work system redesign, role allocation, proportional oversight, or balanced return measurement. The sham controls whether any five-part structure, rather than AX-5R specifically, drives the result.

Appendix A.5. Independent Blinded Scoring

To reduce same-provider circularity and label-conformity bias, the scoring rubric evaluated implementation-quality criteria rather than explicit conformity to AX-5R labels, and each artifact was scored independently by both provider models. For the primary analysis, each artifact’s score was taken from the judge belonging to the provider opposite to the generator: OpenAI-generated artifacts were scored by the Anthropic model, and Anthropic-generated artifacts were scored by the OpenAI model. Scores from both pro-viders enabled estimation of the cross-provider score correlation.

Judges were blind to the arm and to any framework name. No second artifact was placed in the judge’s context, eliminating pairwise position-order bias. Each judge scored five fixed implementation-quality criteria: workflow redesign depth, accountable human– AI role allocation, proportionate oversight, balanced measurable outcomes, and staged integration. Each criterion was scored from 0 to 2, and each artifact also received a holistic implementation-quality score from 0 to 10. The holistic score was the primary outcome. These criteria were selected because they represent generic implementation-quality di-mensions derived from the literature synthesis, not because they require AX-5R labels to appear.

Scoring Criteria and Their Literature Basis

To address the concern that the rubric might simply reward conformity to AX-5R labels, Table A1 maps each scoring criterion to its independent literature basis. It shows that each rewards substantive implementation quality rather than the presence of an AX-5R term. The criteria were specified before scoring and do not name or require the AX-5R components.

Appendix A.6. Analysis

Because the two generators differed systematically in baseline verbosity and absolute score levels, holistic scores were standardized within the generator before pooling. This isolates the framing effect from generator-level score shifts. All contrasts were also esti-mated within each generator separately. For each arm, the full-versus-arm difference was summarized by Cohen’s d with a 95% confidence interval and a Mann–Whitney test; one-sided tests were pre-specified for the directional discriminant expectation stated in ad-vance, and all results for these contrasts are reported two-sided in Table 6 (Section 3.3), the more conservative convention.

These contrasts are interpreted as descriptive rather than confirmatory evidence of rating separation. The 36 artifacts per arm span three workflows and six repetitions per generator; the contrasts treat artifacts as independent after within-generator standardiza-tion and do not model workflow-level clustering, which is an additional reason the probe is reported as artifact-level evidence rather than confirmatory organizational evidence. As a context-level sensitivity check, treating the six generator-by-workflow cells as matched units and applying exact paired Wilcoxon tests to the per-cell arm means yields the same direction in all six cells for the sham, redesign, and risk contrasts and in five of six cells for the role and return contrasts; with only six matched units, however, no contrast can re-main significant under Holm correction (all adjusted p = 0.156).

The probe’s inferential unit is therefore the artifact, the contrasts are described throughout as pre-specified arti-fact-level evidence rather than as confirmation of context-general effects, and extending the number of independent workflow and model contexts is part of the research agenda (Section 5.8). All five pre-specified contrasts (the sham control and the four artifact-rele-vant ablations) remained significant after Holm-Bonferroni correction for multiple com-parisons under both the one-sided and the two-sided convention. The cross-provider score correlation was the Pearson correlation between the two providers’ holistic scores across the 251 artifacts that received a valid score from both providers (r = 0.66); this in-dexes linear association across providers rather than absolute agreement.

One artifact was excluded from this reliability estimate because one provider’s response could not be parsed into a valid score, while the opposite provider’s score used in the primary analysis remained valid.

Appendix A.7. Interpretation and Limits

The ablation discriminant probe should be interpreted within a narrow evidence boundary. It provides artifact-level evidence that the full AX-5R frame produces distin-guishable implementation outputs under matched prompt conditions. It does not show that AX-5R improves organizational performance, predicts firm-level transformation out-comes, or has been validated in field settings.

The probe is also not equally sensitive to all five AX-5R components. Redesign, role, risk, and return are directly embedded in implementation artifacts, whereas readiness pri-marily serves as a workflow entry and an organizational precondition. Therefore, a null readiness ablation should not be interpreted as evidence that readiness is unnecessary for AX. Rather, it indicates that an artifact-level probe is less sensitive to readiness than to artifact-embedded design features. Field studies, surveys, case research, and configura-tional analyses are needed to test whether AX-5R predicts transformation outcomes across organizations, workflows, industries, and regulatory settings.

Table A2. Per-arm descriptive statistics for the ablation discriminant probe (primary cross-provider judge scores).

Raw scores are shown separately by generator because the two generators differ in baseline score levels, which is why scores were standardized within each generator before pooling (Section 3.3); the confidence intervals are for the pooled standardized means.

Appendix A.8. Human-Expert-Rating Check

To address the possibility that model judges share systematic biases that human ex-perts would not, three doctoral-level experts, spanning senior management practice, pub-lic policy research, and university faculty, none of whom is an author, rated a stratified subsample of 56 artifacts (8 per arm; 28 per generator; the three workflows near-balanced within each arm) using the same five anchored criteria and holistic 0 to 10 scale as the model judges, blind to arm labels, framework identity, and study hypotheses. Raters worked independently in randomized presentation orders, and the materials were pro-vided in each rater’s language with identical rubric content.

The convergence result is di-rect: the mean expert rating correlated r = 0.78 (Spearman rho = 0.78, n = 56) with the cross-provider machine scores on the same artifacts, and the expert panel reproduced the dis-criminant core of the probe, rating the full frame far above the sham five-part control (d = 1.90, exact two-sided Mann–Whitney p = 0.005) and, mirroring the machine result, rating the Readiness-removed arm nominally highest. Inter-rater reliability for the holistic score was ICC = 0.29 for a single rater and ICC = 0.55 for the averaged rating used in the convergence analysis (consistency forms 0.34 and 0.61); quadratic-weighted kappa for the five ordinal criteria, averaged across rater pairs, ranged from −0.03 (integration) to 0.43 (role).

The four artifact-ablation contrasts were not individually resolvable in this eight-per-arm subsample, which has power only for large effects; the primary artifact-level ev-idence for those contrasts remains the full-sample cross-provider panel (n = 36 per arm), and the expert check functions as a judge-validity audit rather than a replication of the experiment. The rating materials, the blinded artifact subsample, the analysis script, and the aggregate rating results are included in the public archive.

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