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Human–AI co-creation, not technology alone, correlates with higher innovation: a manager survey finds collaborative AI use yields the biggest gains, amplified by ethical leadership, learning cultures and trust. However, results are correlational and rely on self-reported outcomes from early-adopting organizations, so causal claims about AI driving productivity remain tentative.

Enterprise Intelligence 5.0: Human AI Co-Creation Models for Strategic Leadership, Innovation, and Competitive Advantage
Sohrab Khan Magsi, Muhammad Ahmad, Muhammad Amoon Khalid, Muhammad Irfan Syed, Jafar Ali, Maham Fazal · January 04, 2026 · Inverge Journal of Social Sciences
openalex correlational low evidence 7/10 relevance Full text usable extracted full text DOI Source PDF

Structured author observations

Linked only from stored provider relations; the raw author line above is never matched by name.

OpenAlex

Latest observation:

  1. Sohrab Khan Magsi provider ID
  2. Muhammad Ahmad provider ID
  3. Muhammad Amoon Khalid provider ID
  4. Muhammad Irfan Syed provider ID
  5. Jafar Ali provider ID
  6. Maham Fazal provider ID

Semantic Scholar

Latest observation:

  1. Sohrab Khan Magsi provider ID
  2. M. Ahmad provider ID
  3. Muhammad Amoon Khalid provider ID
  4. Muhammad Irfan Syed provider ID
  5. Jafar M. H. Ali provider ID
  6. M. Fazal provider ID
A cross-sectional survey of managers finds that human–AI co-creation is most strongly associated with higher innovation and competitive advantage, with leadership orientation, organizational learning, trust in AI and AI adoption also positively related.

Citation observations

Cumulative provider counts captured on specific dates; providers are never combined.

This study examined Enterprise Intelligence 5.0 as a human–AI co-creation paradigm where artificial intelligence functions as a strategic partner, not a substitute. A quantitative design investigated how human–AI co-creation, leadership orientation, organizational learning, and trust in AI influence innovation performance and competitive advantage. Data from a structured survey of managers in AI-enabled organizations revealed that human–AI co-creation exerted the strongest positive effect on innovation, followed by leadership orientation, organizational learning, and trust. Innovation increased significantly with higher AI adoption, showing Enterprise Intelligence 5.0 enhances exploratory capability, creativity, and strategic agility. The findings indicate AI’s value is realized not through technology alone, but via quality human–AI collaboration supported by ethical leadership, a learning culture, and governance. Theoretically, the study frames Enterprise Intelligence 5.0 as a socio-technical system of augmentation, not automation. Practically, it emphasizes leadership commitment, transparency, AI literacy, and responsible governance to sustain innovation. Future research should adopt longitudinal and mixed methods to explore evolving co-creation dynamics. A key insight is the importance of iterative feedback loops allowing humans to refine AI, boosting accuracy and trust. Organizations with co-learning environments and psychological safety reported higher adoption and innovation. Integrating AI into cross-functional workflows accelerated decision-making and data-driven experimentation. Successful deployment relies on ethical oversight and inclusivity, aligning AI with organizational values. Early-adopting sectors like healthcare and finance saw gains in personalization and risk management. Thus, Enterprise Intelligence 5.0 is more about strategic human-machine alignment than technological sophistication. Sustaining advantage requires continuous skill development, interdisciplinary collaboration, and governance frameworks balancing innovation with accountability. Future studies should explore sector-specific barriers and AI's long-term impact on workforce dynamics and organizational resilience. References Akpan, I. J., Soopramanien, D., & Kwak, D. H. (2022). Cutting-edge technologies for small business and innovation in the era of COVID-19 global health pandemic. Journal of Small Business & Entrepreneurship, 34(2), 123–140. https://doi.org/10.1080/08276331.2020.1799294 Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making. Journal of Information Technology, 35(1), 37–57. https://doi.org/10.1177/0268396219862972 Aroles, J., Mitev, N., & Vaujany, F.-X. de. (2019). Mapping themes in the study of new work practices. New Technology, Work and Employment, 34(3), 285–299. https://doi.org/10.1111/ntwe.12146 Benbya, H., Pachidi, S., & Jarvenpaa, S. L. (2021). Special issue editorial: Artificial intelligence and organizing. Journal of Management Information Systems, 38(2), 403–408. https://doi.org/10.1080/07421222.2021.1912908 Calvard, T. S., & Jeske, D. (2022). Working with AI? Collaboration, coordination and control in human–AI interaction. Journal of Business Research, 145, 627–636. https://doi.org/10.1016/j.jbusres.2022.03.030 Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. (2021). Hybrid intelligence. Business & Information Systems Engineering, 63(3), 305–321. https://doi.org/10.1007/s12599-021-00681-z Dubey, R., Gunasekaran, A., Childe, S. J., Wamba, S. F., Roubaud, D., & Foropon, C. (2022). Big data analytics and AI-enabled healthcare supply chain. Annals of Operations Research, 302, 1–25. https://doi.org/10.1007/s10479-021-04049-z Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.101994 Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642 Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of intelligent machines. Information and Organization, 28(1), 62–70. https://doi.org/10.1016/j.infoandorg.2018.02.005 Fosso Wamba, S., Queiroz, M., & Trinchera, L. (2023). Dynamics between artificial intelligence capabilities and firm performance. Information Systems Frontiers, 25, 2337–2367. https://doi.org/10.1007/s10796-022-10324-5 Ghosh, A., Sanyal, S., & Singh, K. (2023). AI adoption and organizational agility. Journal of Enterprise Information Management, 36(5), 1530–1551. https://doi.org/10.1108/JEIM-08-2021-0362 Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057 Guo, H., Yang, Z., Huang, R., & Guo, A. (2023). Digital transformation and firm performance. Technological Forecasting and Social Change, 189, 122316. https://doi.org/10.1016/j.techfore.2023.122316 Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review and research agenda. Journal of Innovation & Knowledge, 6(3), 253–269. https://doi.org/10.1016/j.jik.2020.11.002 Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30–50. https://doi.org/10.1007/s11747-020-00737-0 Ivanov, S. (2020). The impact of automation on tourism and hospitality jobs. Tourism Management, 81, 104311. https://doi.org/10.1016/j.tourman.2020.104311 Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human–AI symbiosis in organizational decision-making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007 Le, P. B., & Lei, H. (2019). The mediating role of trust in knowledge sharing and organizational learning. Leadership & Organization Development Journal, 40(1), 68–84. https://doi.org/10.1108/LODJ-06-2018-0215 Leyer, M., & Schneider, S. (2023). Human trust in AI-based decision support systems. European Journal of Information Systems, 32(1), 80–101. https://doi.org/10.1080/0960085X.2022.2117879 Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629–650. https://doi.org/10.1093/jcr/ucz013 Ly, P. T. (2023). Digital leadership and AI adoption readiness. Journal of Business Research, 156, 113455. https://doi.org/10.1016/j.jbusres.2022.113455 Makarius, E. E., Mukherjee, D., Fox, J., & Fox, A. (2020). Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization. Journal of Business Research, 120, 262–273. https://doi.org/10.1016/j.jbusres.2020.07.045 Mariani, M., & Nambisan, S. (2023). Innovation analytics and AI. Research Policy, 52(1), 104635. https://doi.org/10.1016/j.respol.2022.104635 Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Investigating the effects of big data analytics capability on firm performance. Information & Management, 57(2), 103169. https://doi.org/10.1016/j.im.2019.103169 Newman, A., Round, H., Bhattacharya, S., & Roy, A. (2020). Ethical climates in organizations. Journal of Business Ethics, 162(2), 357–373. https://doi.org/10.1007/s10551-018-3996-5 Paschen, J., Wilson, M., & Ferreira, J. J. (2020). Collaborating with AI. Journal of Business Research, 120, 136–145. https://doi.org/10.1016/j.jbusres.2020.07.045 Rafiq-uz-Zaman, M. (2025). Use of Artificial Intelligence in School Management: A Contemporary Need of School Education System in Punjab (Pakistan). Journal of Asian Development Studies, 14(2), 1984-2009. https://doi.org/10.62345/jads.2025.14.2.56 Rafiq-uz-Zaman, M. (2025). Between Adoption and Ambiguity: Navigating the AI Policy Vacuum in Pakistani Higher Education. Research Journal for Social Affairs, 3(6), 877-885. https://doi.org/10.71317/RJSA.003.06.0523 Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072 Romero, D., & Molina, A. (2023). A co-creation framework for human–AI systems. International Journal of Computer Integrated Manufacturing, 36(5), 547–561. https://doi.org/10.1080/0951192X.2023.2166272 Schlagwein, D., & Hu, M. (2023). Practicing with AI. MIS Quarterly, 47(1), 403–429. https://doi.org/10.25300/MISQ/2023/16943 Shankar, V. (2018). How artificial intelligence (AI) is reshaping retailing. Journal of Retailing, 94(4), vi–xi. https://doi.org/10.1016/j.jretai.2018.10.006 Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance of AI. International Journal of Human–Computer Studies, 146, 102551. https://doi.org/10.1016/j.ijhcs.2020.102551 Shrestha, Y., Ben-Menahem, S., & von Krogh, G. (2019). Organizational decision-making structures in the age of AI. California Management Review, 61(4), 66–83. https://doi.org/10.1177/0008125619862257 Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution. Journal of Personal Selling & Sales Management, 38(1), 9–22. https://doi.org/10.1080/08853134.2017.1400953 Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabili

Summary

Main Finding

Enterprise Intelligence 5.0—framed as a socio-technical, human–AI co-creation paradigm—boosts innovation performance and competitive advantage primarily when organizations pair AI capability with leadership support, organizational learning, trust, and governance. In the authors’ survey of managers in AI-enabled firms, human–AI co-creation was the strongest predictor of innovation (standardized β ≈ 0.36), followed by leadership orientation (β ≈ 0.28), organizational learning (β ≈ 0.22) and trust in AI (β ≈ 0.19). The paper argues that AI’s value is realized through augmentation (co-creation) rather than substitution.

Key Points

  • Concept: “Enterprise Intelligence 5.0” = enterprise-level socio-technical system in which humans and AI co-create strategic decisions and innovations.
  • Empirical result highlights: human–AI co-creation has the largest positive effect on innovation outcomes; leadership, learning culture, and trust are significant enabling factors.
  • Descriptive means (5‑point Likert): leadership orientation 4.12; competitive advantage 3.98; human–AI co-creation 3.94; innovation performance 3.87 — respondents generally positive about AI-enabled transformation.
  • Organizational practices linked to better outcomes: AI literacy, ethical leadership, transparency, governance frameworks, psychological safety, co-learning, cross-functional AI-integrated workflows, and iterative feedback loops for model refinement.
  • Sectors cited as early winners: healthcare and finance (personalization, risk management).
  • Theoretical stance: reframes AI adoption as augmentation and dynamic capability—sustained advantage requires continuous learning, skills development, and governance.
  • Limitations noted by authors: cross-sectional design, purposive sampling of managers, self-reported measures; they call for longitudinal and mixed-methods follow-ups.

Data & Methods

  • Design: Quantitative cross-sectional survey of managers/senior professionals in organizations that had implemented AI/data-driven technologies.
  • Sampling: Purposive sampling via professional networks, emails, LinkedIn; no population frame provided.
  • Instrument: Self-administered online questionnaire using validated Likert scales (1–5) adapted to Enterprise Intelligence 5.0 constructs (human–AI co-creation, leadership orientation, innovation performance, organizational learning, trust in AI, competitive advantage).
  • Pilot testing: Conducted (pilot sample size unspecified); items with Cronbach’s alpha < 0.70 were revised or dropped.
  • Analysis: Descriptive statistics, correlation checks, multiple regression (and/or SEM) to test hypothesised relationships; significance evaluated at α = 0.05.
  • Main regression coefficients predicting innovation performance (standardized β):
    • Human–AI co-creation: ~0.36
    • Leadership orientation: ~0.28
    • Organizational learning: ~0.22
    • Trust in AI: ~0.19

Implications for AI Economics

  • Measurement & Identification
    • Move beyond binary measures of AI adoption: include measures of human–AI co-creation (task allocation, human oversight intensity, feedback loops), managerial orientation, and governance to explain productivity and innovation differences across firms.
    • Self-reported survey measures are informative but limited for causal claims—economists should seek administrative/transactional data (decision logs, product cycles, R&D outputs) or natural experiments to identify causal effects.
  • Human capital and complementarity
    • Evidence supports skill complementarities: firms realize innovation gains when AI augments human capabilities. Models of labor demand should incorporate complementarities between AI capital and worker skills (AI literacy, learning orientation).
  • Role of management and institutions
    • Leadership and organizational learning materially condition returns to AI. Economic models of diffusion and firm heterogeneity should include managerial practices and governance quality as moderators of technology returns.
  • Governance, trust, and externalities
    • Trust, transparency, and ethical governance shape adoption and realized gains. Policymakers crafting AI regulation should balance enabling co-creation (innovation) with safeguards (bias mitigation, accountability) to avoid reducing productive adoption.
  • Dynamics & long-run competitiveness
    • Sustained advantage requires ongoing investment in skills, model updating, and organizational learning—implying dynamic complementarities and path dependence. Longitudinal economic studies could estimate persistence of AI-driven productivity differentials.
  • Research agenda suggestions for economists
    • Use panel/longitudinal firm data to estimate causal impacts of human–AI co-creation on productivity, innovation counts (patents/new products), and market outcomes.
    • Exploit instrumentation/natural experiments (e.g., staggered AI rollout, regulation changes) to identify effects of governance and leadership interventions.
    • Quantify heterogeneity across sectors, firm sizes, and tasks to map where augmentation yields highest returns versus where automation or displacement dominates.
    • Model labor market implications: task reallocation, wage premiums for AI-complementary skills, and retraining policy cost–benefit analyses.
    • Develop objective co-creation metrics (decision reversal rates, human override frequency, human feedback volume) to link microbehavior to macroeconomic outcomes.

Limitations of the paper (for economists to keep in mind): cross-sectional, purposive managerial sample, reliance on self-reported outcomes and perceptions, and limited reporting of pilot/sample-size detail—so findings are suggestive rather than definitive causal evidence.

Assessment

Paper Typecorrelational Evidence Strengthlow — Findings are based on a cross-sectional manager survey with self-reported outcomes and predictors, so associations are vulnerable to reverse causality, omitted-variable bias, and common-method / single-respondent bias; the design does not support strong causal claims about AI increasing innovation or competitive advantage. Methods Rigormedium — The study appears to use a structured quantitative survey and multivariate statistical modelling grounded in a clear socio-technical theoretical framework, which is appropriate for exploring relationships; however, important methodological details (sample frame and size, response rate, measurement validity/reliability, controls, mitigation of common-method variance) are not reported here and the cross-sectional design limits inference. SampleStructured survey of managers in 'AI-enabled' organizations (early-adopting sectors such as healthcare and finance are explicitly discussed); sample appears to be organizational managers rather than line workers, likely non-probability/convenience sampling, cross-sectional, with self-reported measures of AI adoption, co-creation practices, leadership orientation, organizational learning, trust in AI, and innovation/competitive advantage. Themeshuman_ai_collab innovation org_design adoption governance skills_training IdentificationCross-sectional structured survey of managers with multivariate analysis (likely regression/SEM) to estimate associations between human–AI co-creation, leadership orientation, organizational learning, trust in AI, AI adoption and self-reported innovation/competitive advantage; no experimental or longitudinal strategy and no quasi-experimental identification reported. GeneralizabilityNon-probability/manager-only sample limits representativeness across firms and industries, Early-adopter sectors (healthcare, finance) may not generalize to manufacturing, retail, SMEs or public sector, Cross-sectional, self-reported outcomes limit external validity to objectively measured productivity or firm performance, Cultural or national context not specified — results may not transfer across countries or regulatory environments, Findings reflect current-state AI systems; rapid technological change may alter relationships over time

Claims (12)

ClaimDirectionOutcomeConfidence & EvidenceDetails
Human–AI co-creation exerted the strongest positive effect on innovation, followed by leadership orientation, organizational learning, and trust. Innovation Output positive innovation (innovation performance)
Reading fidelity high
Study strength medium
not reported
0.3
Innovation increased significantly with higher AI adoption, showing Enterprise Intelligence 5.0 enhances exploratory capability, creativity, and strategic agility. Innovation Output positive innovation (exploratory capability, creativity, strategic agility)
Reading fidelity high
Study strength medium
not reported
0.3
AI’s value is realized not through technology alone, but via quality human–AI collaboration supported by ethical leadership, a learning culture, and governance. Organizational Efficiency positive realization of AI value / innovation/performance
Reading fidelity high
Study strength medium
not reported
0.3
Enterprise Intelligence 5.0 should be framed as a socio-technical system of augmentation, not automation (theoretical framing). Other positive theoretical framing (augmentation vs. automation)
Reading fidelity high
Study strength speculative
not reported
0.05
Organizations with co-learning environments and psychological safety reported higher adoption and innovation. Innovation Output positive AI adoption and innovation
Reading fidelity high
Study strength medium
not reported
0.3
Integrating AI into cross-functional workflows accelerated decision-making and data-driven experimentation. Organizational Efficiency positive speed of decision-making and rate of data-driven experimentation
Reading fidelity medium
Study strength medium
not reported
0.18
Successful deployment of AI relies on ethical oversight and inclusivity, aligning AI with organizational values. Organizational Efficiency positive successful AI deployment / sustained innovation
Reading fidelity high
Study strength medium
not reported
0.3
Early-adopting sectors like healthcare and finance saw gains in personalization and risk management from Enterprise Intelligence 5.0. Firm Productivity positive personalization and risk management improvements
Reading fidelity medium
Study strength low
not reported
0.09
Iterative feedback loops allowing humans to refine AI boost AI accuracy and trust. Ai Safety And Ethics positive AI accuracy and trust in AI
Reading fidelity high
Study strength medium
not reported
0.3
The study used a quantitative design (structured survey of managers in AI-enabled organizations) to investigate how human–AI co-creation, leadership orientation, organizational learning, and trust in AI influence innovation performance and competitive advantage. Innovation Output null_result innovation performance and competitive advantage
Reading fidelity high
Study strength high
not reported
0.5
Trust in AI positively influences innovation, but its effect is smaller than that of human–AI co-creation, leadership orientation, and organizational learning. Innovation Output positive innovation (influence of trust in AI)
Reading fidelity high
Study strength medium
not reported
0.3
Sustaining competitive advantage from Enterprise Intelligence 5.0 requires continuous skill development, interdisciplinary collaboration, and governance frameworks balancing innovation with accountability. Firm Productivity positive sustained competitive advantage
Reading fidelity high
Study strength speculative
not reported
0.05

Notes