Understanding how team-level AI capability development is translated into individual researchers' productivity gains

The gap

Evidence profile

Sourced from the stated research gap and recommendations and future work of the source papers, classified as general, spanning 4 journals.

Supporting evidence — 5 representative gaps

• One capability, many outcomes: unpacking AI dynamic capabilities in scientific teams (2026) · Scientometrics · doi

  The gap in understanding how team-level AI capability development is translated into individual researchers' productivity gains, particularly when expertise and access are unevenly distributed within teams. The lack of a sufficiently precise account of how AI affects research work, how organizations learn, and how capabilities are renewed under change.

  generalstated research gapevidence 5/5
  Keywords: gap understanding team-level capability development translated individual researchers
• Artificial Intelligence, Human Capital, and the Future of Economic Growth: A Multi-Sector Analysis of Productivity, Employment, and Structural Transformation (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  The study findings also allowed drawing some recommendations on efficient integration of artificial intelligence in economic and organizational systems. To begin with, policymakers need to invest in digital education and workforce-training programs in order to help employees get the technical and analytical skills that are needed to efficiently operate with AI technologies. Empowering the human capital will help workers to keep up with the changing technological conditions and minimise the danger of technological unemployment. Second, the organizations ought to apply strategic AI implementation models that incorporate technological innovation programs, as well as staff development programs. To ensure that the employees can operate with intelligent systems successfully, businesses are supposed to invest in constant professional training, digital skills development, and interdisciplinary learning programs. These initiatives will enhance the productivity value of the use of artificial intelligence and the flexibility of the workforce. Third, the governments and other regulatory organizations must come up with enabling policy frameworks to promote responsible AI innovation and at the same time safeguard ethical norms, data privacy, and workforce diversity. Partnership between government and businesses may equally be effective in financing research and development efforts that enhance sustainable technological development and inclusive economic growth.

  generalrecommendationsevidence 5/5
  Keywords: programs technological development workforce artificial intelligence economic systems invest digital training help employees skills operate
• ARTIFICIAL INTELLIGENCE AND WORKFORCE PRODUCTIVITY: A COMPREHENSIVE ANALYSIS OF TRANSFORMATION, OPPORTUNITIES, AND CHALLENGES IN THE MODERN WORKPLACE (2026) · Research · doi

  AI strategy, human-AI collaboration, governance Upskilling, adaptability, AI literacy Regulation, reskilling SCIENTIFIC CULTURE, Vol. 12, No 2.1, (2026), pp. 7140-7160 7159 ARTIFICIAL INTELLIGENCE AND WORKFORCE PRODUCTIVITY programs, inclusive adoption 7.3 Future Research Directions The study has pointed out, in a very transparent manner, the different prospective areas for research these being: • Originally designed longitudinal researches to closely monitor AI adoption and meant productivity as they come to exist one after the other. • Causal analysis at the firm level that separates the impact of AI from other changes in the organization. • Comparisons across different sectors and institutional and countries to see how much regulatory differences influence. REFERENCES • Research focused on well-being and equity that will look at how the productivity driven by AI is distributed among the stakeholders. 7.4 Final Reflections in force time not a deterministic AI presents itself as a transformative but at the same the workplace. Its power to double up the output comes from the deliberate plan, human–AI partnership, and flexible organizational ecosystems. On the one hand, technology, along with the policies that support equal opportunities the researchers’ supervision will be the tools to carve out a more productive, satisfying, and fair work practice that will establish the future of labour in knowledge-driven economies. for all and Acemoglu, D., & Restrepo, P. (2020). Artificial intelligence and jobs. Journal of Economic Perspectives, 34(3), 30– 50. Aghion, P., Jones, B. F., & Jones, C. I. (2019). Artificial intelligence and economic growth (NBER Working Paper No. 23928). National Bureau of Economic Research. Autor, D. (2019). Work of the past, work of the future. AEA Papers and Proceedings, 109, 1–32. https://doi.org/10.1257/pandp.20191110 Autor, D. H. (2015). Why are there still so many jobs? Journal of Economic Perspectives, 29(3), 3–30. https://doi.org/10.1257/jep.29.3.3 Autor, D., Mindell, D. A., & Reynolds, E. B. (2022). The work of the future: Building better jobs in an age of intelligent machines. MIT Press. Bessen, J. E. (2019). AI and jobs: The role of demand (NBER Working Paper No. 24235). National Bureau of Economic Research. Brynjolfsson, E., & McAfee, A. (2014). The second machine age. W. W. Norton & Company. Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company. Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work. Quarterly Journal of Economics, 139(1), 1–48. https://doi.org/10.48550/arXiv.2304.11771 Brynjolfsson, E., Rock, D., & Syverson, C. (2021). The productivity J-curve: How intangibles complement general-purpose technologies. American Economic Journal: Macroeconomics, 13(1), 333–372. Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute. Cowgill, B., & Tucker, C. E. (2020). Algorithmic fairness and economics. Columbia Business School Research Paper. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 Dell’Acqua, F., et al. (2023). Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity. Harvard Business School Working Paper. Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). Gpts are gpts: An early look at the labor market impact potential of large language models. arXiv preprint arXiv:2303.10130, 10. Evelson, B. (2023). Augmented analytics. Forrester Research. Frank, M. R., Autor, D., Bessen, J. E., Brynjolfsson, E., Cebrian, M., Deming, D. J., ... & Rahwan, I. (2019). Toward understanding the impact of artificial intelligence on labor. Proceedings of the National Academy of Sciences, 116(14), 6531–6539. SCIENTIFIC CULTURE, Vol. 12, No 2.1, (2026), pp. 7140-7160 7160 KULJEET KAUR Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? 254–280. Change,

  generalrecommendationsevidence 5/5
  Keywords: future economic artificial intelligence productivity jobs brynjolfsson impact journal autor https working national arxiv business
• ARTIFICIAL INTELLIGENCE AND GLOBAL ECONOMIC TRANSFORMATION: STRATEGIC DYNAMICS, POWER ASYMMETRIES, AND INCLUSIVE DEVELOPMENT IN THE DIGITAL AGE – A COMPREHENSIVE ASSESSMENT (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  Future research should focus on developing inclusive AI frameworks that address global disparities in access and capabilities. Investigating the long-term socio-economic impacts of AI- driven automation on employment, income distribution, and social cohesion will provide valuable insights for policymakers. There is a need to explore ethical AI governance models that balance innovation with privacy, fairness, and accountability, especially in diverse political and cultural contexts. Additionally, research should examine the effectiveness of international cooperation mechanisms in establishing global standards for AI regulation and ethics. Studying VOLUME 74, ISSUE 5, 2026 | https://solovyov-studiesispu.com/ | Page No:31 Solovyov Studies ISPU | ISSN: 2076-9210 https://doi.org/10.5281/Zenodo.20269223 the role of small and developing economies in AI innovation can help identify ways to bridge the digital divide and foster sustainable growth. Moreover, interdisciplinary approaches combining economics, technology, law, and social sciences can better understand AI’s multifaceted impact on society. Overall, future studies should aim to create practical policies and innovative solutions that promote equitable, ethical, and sustainable AI development worldwide.

  generalfuture workevidence 5/5
  Keywords: future developing global social ethical innovation https solovyov sustainable focus inclusive frameworks address disparities access
• Workforce Training in the Age of AI: Perspectives from a School of Professional Studies (2026) · International Journal of Advanced Corporate Learning (iJAC) · doi

  Within higher education, AI-focused curricula are only just emerging, placing more responsibility on L&D teams to engage new and current employees with opportunities to build trust, rethink bottom up approaches, and center AI-Human partnerships. 4.1 Building-trust As the adoption of GenAI increases, so too does an awareness of the dark side of these tools. Concerns about algorithmic biases, factual errors, negative environmental impact, and fear of job replacement can prevent adaptation across an organization. A 2024 report by the Adecco Group found that 40% of workers are worried about long term job security [58]. Companies must address employees’ fears that AI will advantage certain worker groups and replace others. L&D teams are essential to building the trust that is necessary for companies to guide their workforce to engage AI tools strategically. Particular when the investment in AI tools is substantial, it is not enough to make tools available and hope for productive adoption. “The value of AI comes from rewiring how companies are run” [59], and L&D teams can help organizations develop AI strategies and adoption principles that are grounded in trust-building. iJAC | Vol. 19 No. 2 (2026) International Journal of Advanced Corporate Learning (iJAC) 71 Farakish et al. To begin, it can be helpful for L&D teams to consider trust-building in two areas for company-wide deployment: 1) communicating/demonstrating a commitment to reskilling workers and 2) demystifying the technology. Developing a framework for AI adoption that centers trust through clear communication, bounded experimentation, and a commitment to reengineer work to partner with GenAI is key. This framework can build trust by repositioning investment in GenAI technology as an investment in employee capacity and future-readiness. In addition to communicating a responsibility to reskill employees, providing ongoing dialogue to develop confidence in the quality and trustworthiness of AI-produced work is equally important. Only 46% of workers reported feeling confident in their leadership’s AI skills and awareness of AI risks [58]. Emphasizing human supervision and transparent decision criteria that employees can review as GenAI tools evolve is essential for trust-building. L&D teams can develop “awareness training” opportunities to address the legal, ethical, and responsibility concerns of employees at every level of an organization. “Achieving this state of trusted AI takes not only a shift in mindset toward more purposeful AI design and governance, but also specific tactics designed to build that trust” [60]. 4.2 Bottom-up approach Employees’ training and development in the U.S. has traditionally emphasized standardization and structured offerings. GenAI adoption, however, is disrupting this mod

  generalrecommendationsevidence 5/5
  Keywords: trust employees teams building adoption genai tools responsibility build awareness workers companies investment develop engage

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