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View corpus contextAI can take on routine management tasks, but few firms achieve mature integration; while many organisations deploy AI tools, only about 1% reach full digital maturity, making emotionally intelligent and ethically minded leadership essential for successful transformation.
METHODOLOGY OF AI-DRIVEN PERSONNEL SUBSTITUTION FOR LEADERSHIP AND MANAGEMENT ADAPTATION RESULTING FROM DIGITALIZATION
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The article synthesizes literature to propose a 'hybrid synergy' methodology in which AI undertakes transactional managerial tasks while human leaders focus on adaptive intelligence, ethics and upskilling, but it provides little new empirical or causal evidence.
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View corpus contextThis study investigates the methodology of personnel substitution by Artificial Intelligence (AI) as a fundamental determinant in the transition toward the «Corporate Governance 5.0» model. The research demonstrates that the contemporary scientific approach to this phenomenon is primarily mixed and exploratory, intentionally combining quantitative survey-based data from managers and HR professionals with qualitative depth, including semi-structured interviews and thematic analysis. The author substantiates that the substitution of leadership is not a mere act of mechanical automation; rather, it is a «hybrid reconfiguration of managerial tasks» performed through the collaborative interaction of humans and artificial intelligence. A central focus of the article is the identification of a significant «institutional gap»: empirical data suggests that while organisations have implemented AI tools, only 1% have achieved full organisational maturity in their implementation. Furthermore, nearly 70% of digital transformation projects fail due to low organisational readiness and workforce resistance. The study categorizes the literature into several distinct methodological lenses-Technology Acceptance, Readiness for Change, and Resource/ Capability Models-which collectively determine the success of AI integration.On a practical level, the substitution of managers manifests in the automation of data-heavy tasks such as recruiting, task assignment, performance evaluation, and retention decisions. Rather than replacing the human leader entirely, AI takes over these transactional functions, allowing the human manager to concentrate on «adaptive intelligence» and existential skills. Leadership adaptation is measured through specific indicators: the ability to define a digital vision, participatory communication, and managerial flexibility. The methodology tracks outcomes such as innovation, employee retention, and resilience. Most importantly, the research reveals that AI has the potential to satisfy employees psychological needs-such as fairness and data-driven objectivity - sometimes even more effectively than human leaders. The study advocates the concept of Hybrid Synergy (The Human-Machine Handshake), in which AI evolves from a simple tool to a full-fledged «teammate» or «cognitive agent». This integration allows for substantial increases in productivity, employee engagement, and the quality of strategic decision-making. The article emphasises the need to move away from traditional hierarchical oversight toward adaptive leadership grounded in ethical governance and a human-centric approach within the Industry 4.0 and 5.0 landscape. As leadership roles are «rewired», organisational success will depend on harmonising AI's computational power with human leaders emotional intelligence and ethical oversight.
Summary
Main Finding
The paper develops a mixed-methods methodology for "substitution" of managerial personnel by AI as a driver of transition to Corporate Governance 4.0/5.0. It shows substitution is a hybrid reconfiguration (AI performs transactional/data-heavy tasks; humans retain adaptive, ethical, and trust-building roles). A major empirical “institutional gap” persists: ~65% of firms have deployed AI tools but only ~1% reach full organizational maturity, and up to ~70% of digital transformation projects fail due to low readiness and workforce resistance. Successful substitution therefore hinges on leadership (digital vision, culture-building, upskilling), human-centered governance, and ethical oversight.
Key Points
- Terminology: distinguishes Substitution (integration of algorithms into management roles) from Displacement (job losses); substitution is treated as managed organizational transformation, not mere automation.
- Hybrid Synergy: proposes the “human–machine handshake” where AI evolves from tool → adviser → teammate (cognitive agent), increasing productivity, engagement, and decision quality when combined with human emotional intelligence.
- Leadership role: leaders are primary drivers of digital readiness by shaping data culture, participatory communication, and managerial flexibility; leadership must harmonize machine computation with human EI and ethics.
- Institutional gap & failure rates: despite broad AI adoption (~65%), only ~1% of firms achieve maturity; ~70% of digital projects fail due to organizational unreadiness and resistance.
- Practical substitution areas: recruitment, task allocation, performance evaluation, retention analytics; AI handles transactional/data tasks while humans focus on adaptive problems, sensemaking, and trust.
- HR & legal context (Ukraine focus): recommends Cloud HRM, wellbeing programs, KPI/ESG integration; cites legal instruments that shape release/adaptation (e.g., Civil Code, Law on Joint-Stock Companies №2465-IX and labor law provisions such as ZU 2136-IX).
- Risks: communication overload, “karoshi” (burnout, health risks), information asymmetry and employee alienation.
- Quantitative impacts cited: onboarding/adaptation time may fall ~15–20%; digital tools can free 20–30% of time from routine tasks (enabling redeployment or hourly work).
- Recommended practices: continuous performance management (real-time feedback, pulse surveys), upskilling, participatory communication, people-centered HRM and ethical safeguards.
Data & Methods
- Research design: Mixed Methods (exploratory, convergent). Combines systematic/conceptual literature review, bibliometric modelling, quantitative surveys and qualitative interviews.
- Quantitative: questionnaire-based samples typically 100–300 respondents (author cites 150–300 ranges), analytic tools include multiple regression, ANOVA, Structural Equation Modeling (SEM), DTSS (digital transformation stress scale), engagement indices; used to test technology acceptance, readiness, and resource/capability conversion.
- Qualitative: semi-structured and in-depth interviews, thematic analysis (NVivo 14), constant comparative methods, participatory observation to surface psychological barriers, sensemaking, and leadership adaptation patterns.
- Synthesis: concept-building (matrices, functional maps) and cross-study thematic grouping into five methodological boundaries: Technology Acceptance, Readiness & Change, Resource/Capability models, Human-centered HRM, and Systematic/Conceptual Synthesis.
- Evidence base: literature synthesis spanning ~2019–2025 and practitioner/analytic sources (global plus Ukraine-specific legal and wartime resilience context).
Implications for AI Economics
- Labor reconfiguration: substitution reallocates work from routine, data-heavy tasks to machines and raises demand for adaptive, interpersonal, and creative skills—altering skill premiums and complementarities between AI and human capital.
- Productivity vs distribution: potential productivity gains are substantial but uneven due to the institutional gap; firm heterogeneity implies divergent returns to AI investment—policy must address adoption frictions and capability-building.
- Measurement challenges: researchers and policymakers should distinguish substitution (task reallocation) from displacement (job loss) in empirical work; measure maturity, resilience, and wellbeing outcomes (not just adoption counts).
- Human capital policy: public and firm-level investment in upskilling/reskilling, managerial training in digital leadership, and support for transition (e.g., temporary job protection, wage insurance) will shape labor-market outcomes.
- Firm strategy & governance: ROI from AI depends on complementary investments (data culture, leadership, ESG/KPI alignment). Incentives and regulation (including transparency, algorithmic accountability, and worker protections) can affect adoption paths and social welfare.
- Externalities & wellbeing: digitalization creates non-market effects (communication fatigue, health risks) that should be internalized via regulation, workplace standards, and wellbeing programs.
- Macroeconomic/resilience angle: in contexts of instability (e.g., wartime Ukraine), AI-enabled decentralization and digital continuity can be critical for operational resilience—implying strategic value beyond short-run cost savings.
- Research agenda: quantify the institutional gap’s causes and costs; develop firm-level maturity metrics; evaluate long-run effects of hybrid leadership on productivity, inequality, and employment trajectories.
JEL: M12, J24, B49.
Assessment
Paper Typedescriptive
Evidence Strengthlow — The paper is primarily a conceptual and literature-synthesis piece that cites secondary surveys, bibliometric analyses and qualitative studies; it presents no original causal identification strategy, no pre/post or counterfactual analysis, and relies on descriptive statistics and claims from prior studies and industry reports (e.g., '65% implemented AI, 1% maturity') without independently validated empirical tests.
Methods Rigorlow — Although the author describes a mixed-methods research programme and cites methods used in the literature (surveys, SEM, NVivo thematic analysis), the article itself offers no transparent primary-data design, sampling frame, estimation strategy, or robustness checks; claims are largely inferential from secondary sources rather than arising from a clearly reported original empirical study.
SampleNo original empirical sample is reported. The article synthesizes prior studies that typically surveyed managers, HR professionals and SME leaders (referenced sample sizes commonly 100–300 respondents), qualitative semi-structured interviews, bibliometric/NVivo analyses, and industry/analyst reports; specific primary-data collection by the author is not documented.
Themeshuman_ai_collab org_design adoption skills_training governance
GeneralizabilityBased on secondary literature and industry reports rather than new representative data, Findings and examples are often context-specific (corporate governance, insurance sector, Ukrainian wartime conditions) limiting applicability to other sectors/countries, Referenced survey samples in the literature are small-to-moderate (100–300) and likely non-representative, Outcomes are descriptive (readiness, attitudes, role reconfiguration) rather than measured economic impacts (productivity, wages), limiting transfer to macroeconomic conclusions, Potential publication / reporting bias in the synthesized studies (positive narratives about AI adoption) is not assessed
Claims (10)
| Claim | Direction | Outcome | Confidence & Evidence | Details |
|---|---|---|---|---|
| More than 65% of companies have implemented AI tools, but only 1% have reached full digital maturity in their implementation. Adoption Rate | mixed | AI adoption and organizational digital maturity |
Reading fidelity
high
Study strength
low
|
more than 65% adoption; 1% full maturity
|
| Up to 70% of digital transformation projects fail because of low organizational readiness and insufficient consideration of the human factor. Organizational Efficiency | negative | Digital transformation project success or failure |
Reading fidelity
high
Study strength
low
|
up to 70% of projects fail
|
| AI-based leadership substitution is presented as a hybrid reconfiguration of managerial tasks rather than complete replacement of human leaders: AI performs transactional functions such as recruiting, task assignment, performance evaluation, and retention decisions, while human managers focus on adaptive intelligence and trust. Task Allocation | positive | Allocation of managerial and leadership tasks between AI and humans |
Reading fidelity
high
Study strength
speculative
|
not reported
|
| Leadership is described as a primary driver of AI adaptation because leaders build a data-oriented and supportive organizational culture and strengthen employee skills. Organizational Efficiency | positive | Successful implementation of digital ecosystems and organizational AI adaptation |
Reading fidelity
high
Study strength
medium
|
not reported
|
| Integration of AI and data analytics into human-resource management can increase efficiency and reduce employee turnover. Turnover | positive | HR management efficiency and employee turnover |
Reading fidelity
high
Study strength
low
|
not reported
|
| The paper reports that low pay is a motivation for voluntary resignation for 49% of employees and lack of professional-growth prospects for 40%. Turnover | negative | Reasons for voluntary employee turnover |
Reading fidelity
high
Study strength
low
|
49% and 40%
|
| The intelligent adaptation model using LMS, VR trainers, and AI chatbots is expected to reduce onboarding time by 15–20% compared with the traditional model. Task Completion Time | positive | Employee onboarding and adaptation time |
Reading fidelity
high
Study strength
speculative
|
15–20% reduction in adaptation time
|
| The paper proposes that quantitative studies in this area commonly use samples of approximately 100–300 respondents and methods including SEM and ANOVA to assess technology acceptance, engagement, and productivity. Organizational Efficiency | null_result | Technology acceptance, employee engagement, and productivity |
Reading fidelity
high
Study strength
low
|
samples of 100–300 respondents
|
| The paper argues that AI may satisfy employees' psychological needs, including fairness and data-driven objectivity, more effectively than human leaders in some circumstances. Worker Satisfaction | positive | Employees' perceived fairness, objectivity, and psychological-need satisfaction |
Reading fidelity
high
Study strength
speculative
|
not reported
|
| The proposed human–machine hybrid-synergy model is expected to increase productivity, employee engagement, and the quality of strategic decision-making. Decision Quality | positive | Productivity, employee engagement, and strategic decision quality |
Reading fidelity
high
Study strength
speculative
|
not reported
|