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Review

Economics of AI and Sustainability in Industry 5.0: Quest for Entrepreneurial and Organizational Intelligence Under Creative Destruction

by
Artie Ng
1,2,* and
C. F. Cheung
2
1
Balsillie School of International Affairs, Waterloo, ON N2L 6C2, Canada
2
Department of Industrial & Systems Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
*
Author to whom correspondence should be addressed.
Sustainability 2026, 18(12), 6086; https://doi.org/10.3390/su18126086 (registering DOI)
Submission received: 22 May 2026 / Revised: 6 June 2026 / Accepted: 12 June 2026 / Published: 13 June 2026
(This article belongs to the Special Issue Climate Change, Energy Policy, and Industry 5.0)
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Abstract

Industry 5.0, deploying artificial intelligence (AI) at its core, reframes industrial evolution from a predominantly technology- and efficiency-driven innovation model toward a virtuously human-centric, sustainable, and resilient model of value creation by organizations. This review paper, based on an interdisciplinary literature review, explores how AI, within the Industry 5.0 paradigm, reshapes economic logics, the understanding of information asymmetry, and sustainability trajectories, and the implications for entrepreneurial strategy and business model innovation, which demand the development of a new form of organizational intelligence. While the literature suggests that AI, when deployed within a mature Industry 5.0 framework, could generate synergistic economic and sustainability values through circular, human-centered, and digitally augmented systems, human–AI co-intelligence gains are contingent on insights that address systems quality, reskilling, ethics, and reorienting resources from overly short-term profit maximization toward wisdom for long-term socio-ecological, climate resilience, and ESG performance. This study introduces a framework for tackling organizational sustainability dynamics, anticipating the emergence of new industries and the retransformation of enduring ones amid creative destruction in the AI era. Future studies to fill knowledge gaps and implications for human competencies that will enhance organizational intelligence are articulated.

1. Introduction

Industry 5.0 integrates advanced AI, IoT, cyber–physical systems, edge computing, and blockchain to couple human creativity with machine intelligence, aiming to achieve simultaneous gains in productivity, environmental performance, and social well-being [1,2,3]. While comparative analyses of Industry 4.0 and 5.0 show that AI-enabled Industry 5.0 configurations can deliver substantial resource-efficiency and low-carbon emission benefits, contemporary industrial sectors are undergoing various phases of transformation in their adoption of AI technologies for automation and service innovation. Prior studies have reported potential improvements in resource utilization and reductions in carbon emissions when circular economy and human-centric design principles are combined [4,5,6]. In advancing industrial production and quality services, AI-optimized manufacturing, predictive maintenance, and adaptive robotics enable individualized, higher-margin offerings by minimizing waste and energy demand, underpinning new forms of sustainable competitive advantage [7,8,9,10].
The concepts of managerial economics and the applications of microeconomics to understanding firms’ resource utilization and allocation under constraints require reconsideration in light of the current rapid deployment of AI across industries. The economics of AI are evident in the automation of production and workflow processes, and large corporations can benefit greatly from accelerated product innovation [11,12]. Recent research studies on the applications of AI in a corporate setting have also revealed profound implications for environmental, social, and governance (ESG) performance under conventional internal resource constraints [13,14,15,16,17].
Creative destruction under Industry 5.0 propels a technology-driven innovation disruption towards a more sustainability-oriented transformation of industries [2,18,19]. As elaborated in a subsequent study, the transformative power of AI in existing industrial sectors and traditional practices can be explained by Schumpeter’s creative destruction theory, which suggests an innovation cycle of business-model and economic renewal while incessantly destroying the old ones [20]. Rather than replacing humans with automation as in Industry 4.0, Industry 5.0 emphasizes human–machine collaboration, cobots, and AI that augment human creativity and skills [21,22]. This implies strong competitive pressure: firms that do not adapt to the collaborative, digitally enabled, personalized, and sustainable production model could soon become outdated and lose their comparative advantages. Such a destructive side is expected to impact unsustainable, purely efficiency-driven business models and low-skilled, routine roles, while new forms of work, reskilling, and more meaningful, knowledge-intensive jobs emerge [21,23,24,25,26].
Against these swift, transformative forces, this paper offers a critical, interdisciplinary literature review to elucidate pertinent prior studies, which are still fragmented in their approaches, on how AI applications, under the Industry 5.0 paradigm, revolutionize managerial economics and organizational sustainability amid a shifting competitive landscape. It explores the implications for entrepreneurial strategy and business model innovation as they are empowered by advanced organizational intelligence. Recent perspectives on AI-driven innovation in the service sector undergoing industrialization are presented as exemplary, followed by a discussion of entrepreneurial potential, existing knowledge gaps, and future research directions. A theoretical model of AI governance is proposed to articulate and integrate the elements crucial to organizational intelligence, as a novel contribution to the existing body of knowledge. Implications for human competency in organizational settings are presented in the concluding section, followed by this study’s limitations.

2. Literature Review

The literature review conducted for this review paper is based on an interdisciplinary examination of pertinent themes identified via Google Scholar using the selected keywords adopted in this paper, including Industry 5.0; Economics of AI; Information Asymmetry; Organizational Sustainability; ESG Performance; Human Competency. Additional references are incorporated to critically review cross-disciplinary literature on interrelated concepts and research output in this swiftly emerging body of knowledge.

2.1. Industry 5.0, Human-Centric AI, and Circularity

Industry 5.0 reframes industrial transformation around human-centrism, sustainability, and resilience in automation-driven Industry 4.0 beyond efficiency [2,27]. Human-centered AI is positioned as a core enabler, particularly in smart manufacturing, additive manufacturing, supply chains, and product customization, linking the circular economy and socio-technical design [7]. Systematic reviews on Industry 5.0 and the circular economy show that AI, automation, robotics, machine learning, blockchain, 3D printing, and digital twins underpin closed-loop, resource-efficient, and transparent production systems [6,28,29]. A number of these recent research studies make use of systematic or bibliometric reviews to map Industry 5.0, sustainability, AI, and entrepreneurship, indicating a still-emerging but rapidly consolidating field [4,30,31,32]. Others are conceptual, narrative, or industry-informed reviews on AI-driven entrepreneurship, SME innovation, and business strategy in the digital/Industry 5.0 era [27,33,34,35].

2.2. AI, Agency Theory, and Information Asymmetry

AI can be applied to tackle classic problems in economics in which a party can obtain more complete information than the others, leading to adverse selection, moral hazard, and inefficient contracts. As agency theory explains conflicts between principals and agents under asymmetric information and moral hazard, AI agents that continuously monitor performance, compliance, and behavior can reduce “hidden action” problems and align managerial decisions with shareholder interests according to prior studies [36,37]. Generative AI enables monitoring of financial disclosures and may limit “AI whitewashing”, adverse selection, and manipulation, but could also introduce opacity risks that require corresponding governance and regulation [37,38].
Prior studies reveal that information asymmetry generates adverse selection (Akerlof “market for lemons”) when uninformed parties cannot distinguish high from low quality, leading to mispricing and low-quality dominance [39,40,41,42]. AI and data science can strengthen signaling and screening by extracting rich signals from big data and unstructured text, helping principals infer agents’ types and reduce adverse selection in finance, insurance, and AI product markets [40,41,42]. AI can also reduce information asymmetry in healthcare management and environmental disclosure by improving access to and processing of relevant information, thereby mitigating classic agency and screening problems between workers and employers, patients and providers, and firms and regulators [43,44]. At the same time, AI-enhanced markets could suffer from information asymmetry owing to system quality, thereby recreating adverse selection dynamics that must be countered through appropriate disclosure and institutional design [42,45,46,47]. Inappropriate AI use could even result in corporate misconduct owing to information asymmetry between businesses and large language models [47].
As reflected in prior studies, AI functions as an information technology that sharpens signals, enhances monitoring, and narrows information gaps, directly engaging agency theory and the economics of asymmetric information. While AI systems could mitigate adverse selection and moral hazard, without careful disclosure, transparency, and governance, they can themselves become new “markets for lemons” [39,42].

2.3. Managerial Economics of Sustainability in AI-Empowered Industry 5.0

From a managerial economic perspective crucial to business decision-making, AI in Industry 5.0 creates new entrepreneurial opportunities in data-driven services, platform ecosystems, and circular business models (e.g., product-as-a-service, sharing and repair platforms) that can monetize lifecycle information and closed-loop logistics [1,8,48]. Empirical and bibliometric research on sustainable and innovative entrepreneurship indicates that AI adoption is positively associated with sustainable business growth, SME competitiveness, and sustainable entrepreneurship, particularly where firms leverage AI for environmental performance, transparency, and risk management [1,2,48]. At the corporate level, AI has been shown to enhance economic sustainability and facilitate measurable ESG performance demanded by institutional investors, mediated by productivity gains and improved information transparency, especially when supported by proactive public policy and augmented by higher-quality human capital [49,50].
Nonetheless, the economics of AI-enabled Industry 5.0 remain constrained by substantial upfront infrastructural investments, skills shortages, and uneven institutional readiness; limited evidence of efficacy in reducing information asymmetry in managerial and governance contexts remains a concern. Other identified key cost and risk drivers include cybersecurity, data privacy compliance, algorithmic bias, workforce displacement, and the growing carbon footprint of digital and edge-AI infrastructure, particularly under climate change [51,52,53].
A recent International Energy Agency (IEA) report on Energy and AI points out that the expansion of data centers for AI operations will continue to drive energy demand, thereby affecting energy sustainability (source: IEA (2026). Data center electricity use surged in 2025, even with tightening bottlenecks driving a scramble for solutions, https://www.iea.org/news/data-centre-electricity-use-surged-in-2025-even-with-tightening-bottlenecks-driving-a-scramble-for-solutions, accessed on 5 June 2026).
Industry and energy system studies reveal that achieving net-positive sustainability outcomes requires Green IoT, energy-efficient edge AI, AI-enabled smart grids, and careful management of rebound effects associated with increased digitalization [5,28]. Human-centered AI frameworks, ethical governance, and worker upskilling are increasingly considered as economic necessities, not only moral imperatives, because they condition long-term legitimacy, social license to operate, and resilience of AI-intensive business models [7,9,28,30,52].
Industry 5.0 represents a concerted quest to capture value from AI-driven personalization, efficiency, and service innovation, as well as to design models that internalize environmental externalities and social impacts. Emerging roadmaps for Industry 5.0 and sustainable entrepreneurship identify several strategic priorities: (i) leveraging AI and IoT for cleaner production, resource optimization and resilient supply chains; (ii) embedding circular economy and human-centric design into product–service systems; (iii) building governance capabilities and organizational intelligence for ethical, trustworthy AI; and (iv) orchestrating ecosystems that align technological innovation, workforce development and policy incentives. Gaps remain in evidence on real-world implementation, especially for SMEs and firms in emerging economies lacking AI infrastructure, and in quantifying the full cost–benefit profile of AI across regulatory, energy sustainability, and labor-market conditions. A summary of the pertinent core research themes, interlinked with the economics and sustainability of organizations in the age of AI, is provided in Table 1.

2.4. Entrepreneurial Quest for Economic and Sustainability Performance Through AI Deployment

Although prior studies suggest that AI, when deployed within a mature Industry 5.0 framework, could generate synergistic economic and sustainability value through circular, human-centered, and digitally augmented production systems [16,50], these gains are contingent on entrepreneurs, corporate leadership, and policymakers jointly addressing ethical, infrastructural, and skills bottlenecks, and on reorienting business models from short-term profit maximization driven by neo-capitalist ideology toward long-term socio-ecological resilience.
AI-based Industry 5.0 configurations can materially improve both economic performance and environmental outcomes. A structured comparison of Industry 4.0 and 5.0 reports better resource utilization and lower carbon emissions when AI is aligned with the circular economy [4]. AI-enabled circular business models, through digital optimization and autonomous solutions, enhance resource efficiency and create new revenue streams through service-based offerings and lifecycle data monetization [29]. At the firm level, AI adoption can improve corporate sustainability and ESG performance, with productivity and information transparency mediating these effects and proactive AI policies reinforcing economic sustainability [14,15].
Industry 5.0-oriented entrepreneurship literature highlights that sustainability has become a central research insight. AI, blockchain, and digital twins can boost innovative and sustainable entrepreneurship across sectors [8,31,54]. Human-centric management and leadership are emphasized as conditions for leveraging AI ethically while generating hyper-personalized offerings, loyalty, and long-term competitive advantage for business practice [27]. Further, energy system reviews underscore the augmented role of AI in smart grids, demand response, and digitalized energy management, supporting entrepreneurial and innovative solutions for low-carbon, resilient industrial operations and the broader energy transition by optimizing effective adoption of renewable and sustainable energy [55,56]. Generative AI in adaptive manufacturing has demonstrated the ability to reinforce multiple sustainability dimensions through resource optimization, inclusivity, and ethical governance mechanisms [30,35].

2.5. Human-Centered, Ethical AI and Climate Risk Governance

Multiple reviews stress that to sustain economic and social value, AI in Industry 5.0 must be trustworthy, ethical, and human-centric, covering privacy, cybersecurity, algorithmic bias, liability, and governance [7,27,28,57].
Trustworthy AI requirements, namely transparency, accountability, and robustness, are not only ethical constraints but enablers of adoption, market acceptance, and long-term entrepreneurial and business sustainability [33]. Entrepreneur-focused studies reveal dilemmas around collaborative robots, safety protocols, responsibility, and societal-level governance, framing ethics and regulation as central to entrepreneurial strategy rather than peripheral compliance [35,54]. While startup companies at their stage of development are actively seeking revenue generation and expansion opportunities, more mature technology firms are now obliged to consider ethical business practice pertinent to AI applications [58,59].
For instance, in upholding climate risk governance, which is crucial to social and environmental sustainability, AI can enhance climate resilience by improving hazard prediction, informing adaptation planning, and supporting resilient infrastructure and systems [60,61]. These initiatives apply machine learning and deep learning to adaptation in agriculture, water, disaster management, and urban systems. AI is particularly deployed for sustainability risk assessment, evaluating and implementing adaptation options [60,61].

2.6. Business Model for Continuous Innovation, Organizational Intelligence and Sustainability

In light of these potentials, AI under Industry 5.0 can create mutually reinforcing economic and sustainability values when entrepreneurs, firms, and policymakers reckon with human–AI co-intelligence, governance, and inclusivity, and when business models intentionally embed circular and human-centric design driven by absorptive capacity within an organization [62,63]. Nonetheless, with reference to the existing literature, several knowledge gaps in the business model for continuous innovation and organizational intelligence driven by the applications of AI for effective knowledge management and solution deployment that remain to be addressed are articulated as follows:
  • Limited empirical, longitudinal evidence on financial returns and full cost–benefit of AI under Industry 5.0, especially for SMEs and among emerging economies [4,28,32,54].
  • Underdeveloped models linking entrepreneurial ecosystems, policy instruments, and AI-enabled sustainable entrepreneurship in a unified Industry 5.0 framework [27,31,49].
  • Lacking integration of human strategic agency and sustainability configurations into AI-circular economy transitions, beyond narrow efficiency goals [7,10,30].
  • Quest for building up absorptive capacity for sustainability among organizations [62,63].
  • Need for operational frameworks and metrics to implement trustworthy, human-centric AI that simultaneously advances profit, people, and planet in entrepreneurial settings [2,28,33].

3. Cultivating AI-Driven Innovation in Service Sector Under Industrialization

3.1. Transforming Service Innovation

AI-driven innovations are increasingly observed not only in manufacturing but also service-oriented sectors. Especially for the health care sector considered under industrialization [64], innovative systems are augmented by AI to support the shift toward predictive, personalized, and preventive medicine by combining continuous data capture with adaptive robotic assistance, for instance, at the point of care, thereby assisting the delivery of quality service and the pursuit of social sustainability [65,66].
AI-enabled service robots are increasingly embedded in hospitals, ICUs, long-term care, and home care, where machine learning, computer vision, and natural-language systems allow robots to sense, decide, and act in more autonomous and personalized ways by transforming service innovation through an intelligent organization [66,67,68].

3.2. Clinical and Care Robotics Applications

In institutional care, AI service robots have been used for logistics, disinfection, vital-sign monitoring, telepresence, and nursing assistance, helping to prevent infection, reduce human error, and extend staff capacity, especially highlighted during COVID-19 [69,70,71]. In ICUs, AI robots are being trialed for therapeutic support, rehabilitation, telepresence, and logistics, aiming to deliver more consistent, intelligent, and even personalized care while reducing the workload for critical care staff [66].
For older adults and long-term care residents, AI-powered assistive and companion robots use motion control, affective computing, and dialogue systems to support mobility, cognitive stimulation, safety operations monitoring, and social connection, to age-in-place and relieve pressure on understaffed facilities [65,67,72,73,74]. In surgical and interventional settings, AI enhances robot guidance, perception, and decision support, improving precision, reducing complications, and enabling more minimally invasive care [68,75,76].

3.3. Human Experience and Ethics in AI Applications

Studies of long-term care and staff perspectives emphasize that perceived technical complexity, doubts about usefulness, safety concerns, fear of job loss, and risks of depersonalized care are major barriers to adopting AI-enabled robots [77,78,79,80]. Consumer-centric work shows that users value robots as supportive resources that expand autonomy and emotional regulation, but do not experience them as true substitutes for human care [73]. Reviews of embodied AI in mental health and broader AI–robotics ecosystems highlight unresolved issues around privacy, bias, responsibility, equitable access, and the long-term impact of non-human agents on notions of illness and care [66,81].
In light of the identified concerns in the development of such AI applications, the underlying pursuits for AI-driven service innovation are summarized in Table 2.

4. Discussion

4.1. Potentials of AI-Driven Entrepreneurial Firms

Amid the ongoing opportunities and challenges of deploying sustainable AI-driven solutions under Industry 5.0, the rapid emergence of venture capital-funded entrepreneurial firms has spurred the development of new products and services through innovative business models. While AI-enabled entrepreneurial firms are now considered engines of economic growth, firms that strategically embed AI with proven human-centric solutions can amplify innovation, scalability, and competitive advantage, turning AI into a core driver of business growth and an engine of financial returns for venture capital, rather than merely a support tool. AI can serve as a general-purpose technology that boosts firm-level growth primarily through continuous product improvement and business model innovation rather than pure cost cutting [11,82]. For startups and SMEs, AI can function as a force multiplier, automating analysis, supporting decision-making, and enabling small teams to deliver [11,82]. AI can also improve decision quality, risk mitigation, and competitive positioning in entrepreneurial ventures, significantly contributing to venture success [33,83].
Mature firms investing in AI exhibit rapid growth in sales, employment, and market value, driven by a greater number of trademarks and more frequent product updates [11]. Organizations that adopt AI effectively may enhance revenue gains, along with substantial cost reductions and productivity improvements, while influencing their market dominance [84,85].
These prior studies reveal great expectations for entrepreneurial firms to leverage AI over the lifecycle of an organization:
  • Opportunity discovery and strategy: predictive analytics and market intelligence reveal underserved segments and inform market-entry and pricing strategies [33,83,85].
  • Continuous product and service innovation: machine learning and generative AI accelerate ideation, prototyping, personalization, and iterative refinement, reducing time-to-market and increasing product quality [11,82,86].
  • Scalability for business growth: AI-driven automation, customer analytics, and supply-chain optimization lower marginal costs and support rapid scalable expansions, especially for digital and IT startups [82,83,87].
Overall, early AI adopters could benefit from accelerated learning, richer data assets, and stronger customer personalization, reinforcing their competitiveness through winner-take-all dynamics and making late imitation difficult [11,85]. However, their ability to overcome remaining concerns about sustainability, as reflected in the current literature, has not yet been fully addressed.

4.2. Knowledge Gaps on Organizational Sustainability

Despite the vast potential of AI-driven entrepreneurial firms for foreseeable business benefits, there are still ongoing debates and arguments about AI governance, regulatory mechanisms, and overall sustainability, given their unprecedented scale, pace of growth, and exposures. As reflected in the literature, the applications of AI could be a double-edged sword, particularly when information asymmetries are instigated by low-quality AI systems, creating a “market for lemons” [39,42]. Considering the literature review of prior studies, which remains largely fragmented among the pertinent themes, Table 3 summarizes proposed future studies to explore the issues related to the sustainability of organizations identified in prior studies. A conceptual framework for tackling long-term organizational sustainability under AI-Driven Industry 5.0, which requires an augmented AI-governance model, is illustrated in Figure 1. This conceptual framework highlights the centrality of the proposed AI governance model, which depicts the dynamics among the economic viability of AI applications, energy sustainability, climate resilience, business model innovation, organizational sustainability policies, long-term growth and development based on human–AI co-intelligence, and accountability for sustainability [88,89,90,91]. This model informs the development of the necessary organizational intelligence to support robust and sustainable management decisions, which remains to be better understood in the era of AI.

5. Concluding Notes

5.1. Organizational Intelligence Built on Human Competency

Organizational intelligence built on augmented human competency is crucial to the survival and sustainability of future organizations. First, the prospects for AI-enhanced ESG performance are imminent under Industry 5.0 [14,15,16,44]. AI is seen as a force in transforming labor demand for both emerging and existing organizations, amid growing scrutiny of such performance, requiring a redefined competency model that prepares the workforce for creative destruction [20]. Recent research finds that educational systems and business jointly shape AI-related competencies, with AI career preparedness the strongest predictor of labor-market alignment; recommended reforms include AI literacy, interdisciplinarity, ethics, and continuous learning [92]. Besides the importance of absorptive capacity for sustainability among organizations emphasized by [62,63], national AI strategies also stress workforce reskilling, human resources-oriented curricula, and inclusive access to AI training resources [93].
Multiple reviews and empirical studies converge on a human competency mix: technical AI literacy, data mining, integrity and analytics skills, and prompt engineering, combined with higher-order capabilities such as critical thinking, creativity, problem-solving, judgment, and ethical reasoning [94,95,96,97,98]). Regarding critical thinking, evidence suggests that without scaffolding, AI tools risk overreliance and shallow cognition, especially at lower levels of Bloom’s taxonomy [95,97,99]. Nonetheless, structured frameworks for engaging with AI-generated text (stepwise questioning, evaluation, and synthesis) significantly improve critical thinking performance when rigorous, repeated training is provided. Similar results appear in business and marketing education, where revised taxonomies integrate AI-specific competencies (reflection, melioration, ethical reasoning) to enhance higher-order thinking in AI-rich contexts [99,100].

5.2. Implications for Competency Development

Higher education institutions and their faculties are cast as central orchestrators of competency development and employability. They are inevitably expected to redesign curricula around AI literacy pathways, embed critical thinking and ethics across disciplines, adopt interdisciplinary and hands-on approaches, and work closely with industry to keep competency models aligned with new fast-moving technological frontiers such as quantum computing and the space economy [96,101,102,103].
Conceptual work at the AI–entrepreneurship nexus considers AI as a “super tool” that, when combined with entrepreneurial judgment, can redistribute skills, enable new governance mechanisms, and expand prosocial purposes of technology [104]. AI-enabled individual entrepreneurship studies argue that AI allows solo or small founders to achieve scale and sustainability previously reserved for large corporations, aligning ventures with SDGs on inclusion and resource efficiency while highlighting ethical and regional access risks [105,106]. Data science-driven models for entrepreneurship education and ecosystems similarly stress AI-supported opportunity recognition, critical thinking for creativity, and audience nurturing, framing entrepreneurship itself as a science-informed, data-intensive practice [107,108]. Key future competency gaps for the AI era are highlighted in Table 4.
Finally, co-intelligence competency for ESG performance is conceived as the ability to collaborate effectively with AI, using it as an agent or even a partner to enhance human productivity, creativity, and learning, rather than merely treating it as a tool or allowing it to replace human thinking. This concept of co-intelligence emphasizes a symbiotic relationship in which humans provide context, judgment, and ethical oversight while AI brings speed, scale, and synthesis [89,90]. Taking climate resilience and decarbonization into consideration, business leaders need to make ultimate human judgments and rational executive decisions for their organizations, while exercising critical thinking to balance short-term economic interests with sustainable development goals, beyond maximizing the repatriation of financial returns for venture capital. Incumbents need to develop AI-driven organizational intelligence for social and environmental sustainability initiatives, which could help them better embrace and advance these initiatives, affecting stakeholders beyond shareholders in the long term. Against these conflicting objectives, tertiary education institutions, especially business schools, need to swiftly rethink and redesign their curricula so that students can relearn managerial economics, ethics, and organizational resource allocation strategies for sustainability solutions in the challenging era of AI that could exacerbate information asymmetry.
As expressed by the former Google CEO Eric Schmidt, “We thought that we were adding stones to a cathedral of knowledge that humanity had been constructing for centuries, but the world we built turned out to be more complicated than we anticipated… In the years after I graduated, no one sat down and resolved to build technology that would polarize democracies and unsettle a generation of young people. That was not the plan, but it happened.” [109].

6. Limitations

This review paper does not offer any empirical data or hypotheses to test the proposed theoretical framework. Future studies may consider the identified knowledge gaps and examine the range of underlying issues that emerge amid the ongoing transformation of the industrial and service sectors. Case studies of incumbent organizations can offer insights into how they navigate the double-edged nature of AI applications.

Author Contributions

Conceptualization, A.N. and C.F.C.; methodology, A.N.; validation, A.N. and C.F.C.; formal analysis, A.N.; writing—original draft preparation, A.N.; writing—review and editing, A.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. A Conceptual Framework of AI Governance Dynamics for Organizational Sustainability under AI-Driven Industry 5.0.
Figure 1. A Conceptual Framework of AI Governance Dynamics for Organizational Sustainability under AI-Driven Industry 5.0.
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Table 1. Core Economic Sustainability Themes of AI in Industry 5.0.
Table 1. Core Economic Sustainability Themes of AI in Industry 5.0.
Core Research ThemesInsightsSources
Human-centric AI and productivityAI can augment human creativity and robotics applications, enabling efficient, customized, and higher-value outputs.[3,4,9,30]
Resource efficiency and green gainsAI can boost resource utilization and reduce emissions in 5.0 configurations.[3,5,6]
Circular and ESG-oriented modelsAI that underpins the circular economy, traceability can improve ESG performance.[7,49,50]
Entrepreneurial and corporate opportunitiesIndustry 5.0 readiness enhances entrepreneurship, corporate competitiveness, and sustainable growth.[1,2,33,34]
Risks, investments, and governance needsHigh investments (e.g., in AI technological infrastructure and energy utilization), skills gaps, and ethical risks require policy and governance frameworks.[28,51,52,53]
Table 2. Pursuits for AI-driven Service Innovation.
Table 2. Pursuits for AI-driven Service Innovation.
PursuitsConcernsSources
Long-term, real-world impactFew robust longitudinal or large-scale trials on outcomes, safety, and cost-effectiveness across settings[65,67,69,70]
Trust, acceptance, and co-designNeed for deeper study of how staff, residents, and families build trust and how co-design affects adoption[74,78,79,80]
Hybrid human–robot care modelsLimited empirical work on optimal task allocation, workflow redesign, and preserving relational care[10,72,73]
Bias, fairness, and personalizationUnderstudied risks of biased AI in robots that triage, monitor, or coach patients, and how to audit them[68,70,81]
Governance and risk management frameworksNeed for operational models for responsibility, regulation, reimbursement, risk management, climate resilience, energy sustainability, and data stewardship in AI-robot systems[50,52,66,77]
Table 3. Future Studies on Organizational Sustainability of AI-driven Firms.
Table 3. Future Studies on Organizational Sustainability of AI-driven Firms.
Future StudiesSustainability of OrganizationsSources
Entrepreneurial vs. large firms’ economic sustainabilityMost causal evidence on AI and growth comes from larger firms; mechanisms in resource-constrained startups are less quantified.[82,84,85]
Longitudinal impact on survivalLimited long-term studies on how AI affects survival, scaling paths, disruptive innovation, and resource allocation in organizations; co-intelligence of AI and humans in leadership and execution for organizational intelligence; implications for staff development.[59,83,86]
Business-model archetypes and performanceTaxonomies of AI startup models exist, but the links between archetype choice, resource allocation, growth trajectories via continuous innovation, and new forms of intelligent organization remain underexplored.[88,89,90]
AI Governance: Measuring AI return and capability maturityFew methods to quantify AI return on investment, capability maturity, and their relationship to growth in entrepreneurial settings while addressing the potential to mitigate or exacerbate information asymmetry.[36,42,44,53]
ESG Policy implications for the sustainability ecosystem gapsComparative evidence on how ethics and ecosystem factors (infrastructure, energy sustainability, funding, regulation) shape AI-enabled sustainable growth and climate risk resilience.[14,15,16,49,52,91]
Table 4. Future Competency Gaps for the AI Era.
Table 4. Future Competency Gaps for the AI Era.
AreasKey Competency GapsSources
Science-based entrepreneurshipApprehend AI and data science redefining scales, enabling sustainable, prosocial ventures, and augmenting new AI-driven entrepreneurship, business strategies, and economic models.[54,104,105,106]
Employment competenciesDevelop AI-reshaping skills; Improve preparedness and AI literacy driving labor-market fit; Evaluate the co-intelligence management model within organizations.[92,93,97,98,102]
Critical thinking Strengthen critical thinking at risk without scaffolds and ethical reasoning skills; appreciate the effectiveness of structured AI-CT frameworks; develop an enhanced outcome-based education system to address AI issues.[94,95,96,99,100,108]
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Ng, A.; Cheung, C.F. Economics of AI and Sustainability in Industry 5.0: Quest for Entrepreneurial and Organizational Intelligence Under Creative Destruction. Sustainability 2026, 18, 6086. https://doi.org/10.3390/su18126086

AMA Style

Ng A, Cheung CF. Economics of AI and Sustainability in Industry 5.0: Quest for Entrepreneurial and Organizational Intelligence Under Creative Destruction. Sustainability. 2026; 18(12):6086. https://doi.org/10.3390/su18126086

Chicago/Turabian Style

Ng, Artie, and C. F. Cheung. 2026. "Economics of AI and Sustainability in Industry 5.0: Quest for Entrepreneurial and Organizational Intelligence Under Creative Destruction" Sustainability 18, no. 12: 6086. https://doi.org/10.3390/su18126086

APA Style

Ng, A., & Cheung, C. F. (2026). Economics of AI and Sustainability in Industry 5.0: Quest for Entrepreneurial and Organizational Intelligence Under Creative Destruction. Sustainability, 18(12), 6086. https://doi.org/10.3390/su18126086

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