Reconceptualizing Competence through Facets: ATHENA as a Strategic Framework for Adaptive Talent Management in the Age of AI

Human resource management (HRM) remains predominantly organized around competency and occupation-based representations that implicitly presume relative stability in work content. Yet artificial intelligence (AI) increasingly changes work at the level of tasks, activity sequences, decision criteria, and human–tool interaction. This theory-building article introduces ATHENA (Advanced Tool for Holistic Evaluation and Nurturing of Abilities), a facet-based framework that reconceptualizes competence as an emergent, context-bound configuration of mobilizable human resources rather than a stable entity attached to job titles. ATHENA proposes an intermediate analytical layer through facets specified at developmental mastery levels to connect activity-based work analysis with recruitment, learning design, internal mobility, and strategic workforce planning under task volatility. The framework is organized around five interdependent dimensions: cognition, conation, knowledge, emotion, and sensorimotor resources. These dimensions are decomposed into nineteen sub-dimensions and sixty facets, each interpretable through four progressive mastery levels. We clarify how the framework was theoretically derived, distinguish it from competency models, KSAO (Knowledge, Skills, Abilities, Other Characteristics) approaches, ability requirement scales, work analysis, psychometrics, and skills-based HRM, and specify how different AI modalities alter activity demands and facet configurations. The article provides a worked example of an AI-augmented analyst role, operationalizes five testable propositions, and defines boundary conditions, governance requirements, and a research agenda. ATHENA is not presented as a validated measurement instrument; rather, it is a conceptual and methodological scaffold for empirical validation and responsible organizational experimentation.

Summary

Main Finding

ATHENA is a theory-driven, facet-based framework that reconceptualizes competence as an emergent, context-bound configuration of mobilizable human resources rather than a stable attribute tied to job titles. It introduces an intermediate analytical layer (facets-at-level) between task/activity descriptions and high-level competency labels to make HR decisions (recruitment, learning, mobility, workforce planning) more resilient to AI-driven task volatility. ATHENA organizes human resources across five interdependent dimensions (cognition, conation, knowledge, emotion, sensorimotor), decomposed into 19 sub-dimensions and 60 facets, each described at four progressive mastery levels.

Key Points

Problem addressed: Traditional competency and occupation-based HR representations assume stability of work content; AI changes tasks, activity sequences, decision criteria, and human–tool interactions, creating misalignment between static HR models and shifting task demands.
Core constructs:
- Work activity: observable, goal-oriented, tool-mediated behaviors.
- Facet: a mobilizable human resource (e.g., working memory, uncertainty management) usable across activities.
- Configuration: patterned combination of facets mobilized for an activity/role.
- Competence: effective performance in context — an emergent outcome of configurations.
Architecture:
- Five dimensions: cognition, conation, knowledge, emotion, sensorimotor.
- 19 sub-dimensions and 60 facets (the paper provides a full list).
- Facet-at-level: each facet is interpreted at one of four mastery levels capturing autonomy, stability, transferability, contextual sensitivity.
Positioning among frameworks:
- ATHENA sits between task-level work analysis (e.g., O*NET/ESCO) and competency/psychometric models; it aims to link activities to mobilizable resources and to be compatible with existing taxonomies while adding configurational explanatory power.
Methodological stance:
- Theory-building, integrative conceptual synthesis drawing on cognitive psychology, work analysis, motivational theory, instructional design, and HR literature.
- Not an empirical validation or psychometric instrument; explicitly postpones high-stakes measurement until use-specific validation.
Practical content:
- Provides an activity-to-facet mapping mechanism, a worked example (AI-augmented analyst), five operationalizable propositions, boundary conditions, governance requirements, and a research agenda.
Role of AI:
- AI alters facet demands unevenly across tasks; ATHENA is designed to detect how facet configurations shift with different AI modalities and task reconfigurations.

Data & Methods

Study type: Conceptual/theory-building article (no original empirical data).
Derivation method:
- Integrative conceptual synthesis across multiple literatures (executive functions, motivation, learning design, HR frameworks).
- Selection principles for facets: (1) refer to mobilizable human resources (not tasks/outcomes), (2) interpretable across activities, (3) fine-grained yet usable, (4) compatible with existing taxonomies.
Validation status: Framework presented as a scaffold for empirical validation; authors recommend subsequent studies on content validity, inter-rater reliability for activity→facet mapping, construct validity of facet measures, predictive/criterion validity for performance/adaptation, and intervention effectiveness.
Proposed empirical approaches (paper-level suggestions):
- Activity-to-facet mapping exercises, case studies (e.g., AI-augmented analyst), experimental and quasi-experimental tests of facet-targeted training, and measurement validation in HR contexts.
Limitations acknowledged:
- Not an exhaustively validated ontology; facets derived conceptually rather than via large-scale factor analysis or Delphi consensus.
- Some facets may overlap with existing psychometric constructs; ATHENA treats them functionally (mobilization in context).

Implications for AI Economics

Practical and research implications for economists studying labor, firms, and AI-driven change:

Labor demand and task-level reallocation
- ATHENA suggests that AI affects the mix of facet demands within occupations rather than wholesale occupation elimination. Economic models should move from job-level to facet- or activity-level representations when assessing automation exposure and complementarities.
- Empirical implication: measure exposure to AI by mapping tasks to facet requirements and model changes in facet demand over time; expect heterogeneous impacts across workers with different facet portfolios.
Human-AI complementarity and substitution
- Complementarities will be facet-specific (e.g., AI may complement procedural knowledge but substitute routine attention). Incorporate facet-level elasticities in production and assignment models to predict wage and employment effects more precisely.
- Policy/firm implication: identify which facets amplify returns to AI adoption (skill-biased augmentation) and which reduce marginal returns to certain human resources.
Wage structure, returns to training, and investment decisions
- Facet mastery levels imply non-linear returns to training: investments that raise a worker from intermediate to advanced mastery on high-value facets may have outsized returns. Models of firm training and worker human-capital investment should allow for discrete mastery thresholds and complementarities across facets.
- Empirical strategies: evaluate targeted training programs on facet-level outcomes and downstream earnings/productivity effects using RCTs or quasi-experiments.
Internal mobility, matching frictions, and labor market signaling
- ATHENA’s configurational approach strengthens the case for internal mobility (firms can remap existing facet portfolios to new configurations) and for richer intra-firm matching mechanisms. Labor market models should account for reduced external turnover costs when firms can redeploy facet portfolios internally.
- For signaling: facet-at-level measures (if validated) could improve match quality, but measurement and standardization challenges remain.
Measurement and data opportunities
- Operationalizing ATHENA invites new microdata: activity-level time-use, task descriptions linked to facet-requirement codings, administrative HR records (assignments, promotions, training), and AI-usage logs. Matched employer-employee datasets could estimate facet-demand shifts and returns.
- Suggested empirical designs: difference-in-differences around AI deployment, instrumenting AI exposure with firm-level adoption timing, field experiments on facet-targeted interventions, and validation studies linking facet measures to productivity.
Macroeconomic and distributional dynamics
- Aggregate impacts of AI will depend on the distribution of facet endowments across the workforce and on firms’ capacity to reconfigure work and invest in facet development. Models of structural change should incorporate heterogeneity in facet portfolios by occupation, education, age, and region.
- Distributional risk: workers concentrated in low-facet-flexibility portfolios face higher displacement risk. Policy responses (retraining, portability frameworks) should be informed by facet-level diagnostics.
Policy and governance
- ATHENA highlights governance needs: transparency about how AI changes facet demands, standards for facet measurement and validation, and safeguards around high-stakes use (selection, promotion). Regulatory frameworks should promote responsible experimentation and evaluation of AI-driven role redesign.
- Public policy can support the creation of interoperable facet taxonomies and funding for validation studies and targeted training initiatives.
Research agenda for AI economists (concrete priorities)
- Validate facet mappings empirically: link activity descriptions to facet demands across industries; quantify inter-rater reliability.
- Estimate returns to facet mastery: causal estimates of productivity/wage gains from facet-level training.
- Measure facet-demand shifts from AI: use firm rollouts of AI tools to identify which facets increase/decrease in importance and the labor-market consequences.
- Integrate facets into structural models of task assignment and human-capital investment to improve predictions of employment, wages, and inequality under AI adoption.
- Explore firm-level decisions: how do firms choose between tech adoption and facet development? Model complementarities and substitution at the facet level.

Concluding note: ATHENA provides a practically oriented conceptual scaffold that can sharpen economic analysis of AI-driven labor change by shifting focus from fixed job labels to mobilizable human resources and their mastery. Economists can use ATHENA as a blueprint to design measurement instruments, field interventions, and structural models that capture the nuanced, task-contingent nature of human–AI complementarities.

Assessment

Paper Typetheoretical Evidence Strengthn/a — The article is a theory-building, conceptual framework without empirical tests or causal estimates; no data-driven identification of effects is provided. Methods Rigormedium — The paper systematically derives a multi-dimensional framework, situates it against existing competency/KSAO models, provides a worked example and testable propositions, and articulates boundary conditions and governance; however, it offers no empirical validation, measurement instrument, or robustness checks. SampleNo empirical sample — a conceptual, literature-informed framework (ATHENA) with a worked illustrative example of an AI-augmented analyst role; the contribution consists of taxonomy (5 dimensions, 19 sub-dimensions, 60 facets), mastery levels, and theoretical propositions rather than original data. Themeshuman_ai_collab org_design skills_training adoption GeneralizabilityNot empirically validated — applicability across sectors, firm sizes, and cultural contexts is untested, Framework may require adaptation for different AI modalities beyond those considered, Operationalization and measurement of facets are not provided, limiting immediate practical use, Organizational adoption depends on HR systems, legal/regulatory contexts, and managerial capacity, Designed at an abstract level; may miss micro-level task or industry-specific nuances

Claims (12)

Claim	Direction	Outcome	Confidence & Evidence	Details
Human resource management (HRM) remains predominantly organized around competency and occupation-based representations that implicitly presume relative stability in work content. Adoption Rate	positive	organizational representation of HRM (competency- and occupation-based structures)	Reading fidelity high Study strength medium	not reported 0.12
Artificial intelligence (AI) increasingly changes work at the level of tasks, activity sequences, decision criteria, and human–tool interaction. Task Allocation	positive	change in work characteristics (tasks, sequences, decision criteria, human-tool interactions)	Reading fidelity high Study strength medium	not reported 0.12
This article introduces ATHENA (Advanced Tool for Holistic Evaluation and Nurturing of Abilities), a facet-based framework that reconceptualizes competence as an emergent, context-bound configuration of mobilizable human resources rather than a stable entity attached to job titles. Skill Acquisition	positive	conceptualization of competence (emergent/context-bound vs. stable/job-attached)	Reading fidelity high Study strength speculative	not reported 0.02
ATHENA proposes an intermediate analytical layer through facets specified at developmental mastery levels to connect activity-based work analysis with recruitment, learning design, internal mobility, and strategic workforce planning under task volatility. Organizational Efficiency	positive	linkage between activity-based analysis and HR processes (recruitment, learning design, internal mobility, workforce planning)	Reading fidelity high Study strength low	not reported 0.06
The ATHENA framework is organized around five interdependent dimensions: cognition, conation, knowledge, emotion, and sensorimotor resources. Training Effectiveness	positive	dimensions composing the conceptual model of competence	Reading fidelity high Study strength medium	not reported 0.12
These five dimensions are decomposed into nineteen sub-dimensions and sixty facets, each interpretable through four progressive mastery levels. Training Effectiveness	positive	granularity of the facet taxonomy (number of sub-dimensions, facets, mastery levels)	Reading fidelity high Study strength medium	not reported 0.12
The paper clarifies how the framework was theoretically derived and distinguishes it from competency models, KSAO approaches, ability requirement scales, work analysis, psychometrics, and skills-based HRM. Governance And Regulation	positive	conceptual distinctiveness from existing HRM/psychometric approaches	Reading fidelity high Study strength low	not reported 0.06
The article specifies how different AI modalities alter activity demands and facet configurations. Task Allocation	positive	changes in activity demands and facet configurations induced by AI modalities	Reading fidelity high Study strength speculative	not reported 0.02
The article provides a worked example of an AI-augmented analyst role. Task Allocation	positive	illustrative application of ATHENA to a specific role (AI-augmented analyst)	Reading fidelity high Study strength high	not reported 0.2
The paper operationalizes five testable propositions. Research Productivity	positive	testable propositions derived from the framework	Reading fidelity high Study strength medium	not reported 0.12
The paper defines boundary conditions, governance requirements, and a research agenda for ATHENA and its use. Governance And Regulation	positive	identified governance requirements and research agenda elements	Reading fidelity high Study strength high	not reported 0.2
ATHENA is not presented as a validated measurement instrument; rather, it is a conceptual and methodological scaffold for empirical validation and responsible organizational experimentation. Training Effectiveness	null_result	validation status of ATHENA as a measurement instrument	Reading fidelity high Study strength high	not reported 0.2