Citation:

Sufia Parveen, 2026. "AI Augmented Teams: Redefining the Future of Work with Salesforce Copilots and Agentforce Grid", ESP Journal of Engineering & Technology Advancements  6(2): 47-56.

Abstract:

Artificial intelligence is no longer an isolated automation tool, but part of enterprise platform infrastructure. This trend has stimulated the emergence of academic interest in AI-enhanced teams, i.e. work arrangements in which human expertise is combined with machine intelligence through both conversational systems and more autonomous software agents. The focus of this literature review is to consider AI-enhanced teams through the lens of enterprise copilots and grid-based coordination of agents, using Salesforce Copilots and Agentforce Grid as recent examples of these broader trends. The review discusses such core themes as human-AI complementarity, designing with digital assistants, trust and explainability, automation bias, anthropomorphic interface design, and redesigning the organization. According to the reviewed literature, AI augmentation can improve the speed of information processing, the quality of decisions, and coordination of workflows, in particular, in knowledge-centric and service-based environments. Meanwhile, the literature also emphasizes ongoing issues related to transparency, excessive reliance, accountability, and workforce misalignment. A conceptual framework is suggested to connect technological structure, patterns of team interaction, and organizational performance. Comparative analysis brings out dominant methodological practices and approaches in the field of measurement. Significant gaps remain in longitudinal evidence, multi-agent enterprise research, cross-functional team performance measurement, and governance of agentic systems across networked business processes. This is a research field of growing significance for management research, information systems design, and the study of digitally mediated work.

References:

[1] M. H. Jarrahi, Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making, Bus. Horiz. 61(4) (2018) 577–586.

[2] D. Dellermann, P. Ebel, M. Söllner, and J. M. Leimeister, Hybrid intelligence, Bus. Inf. Syst. Eng. 61(5) (2019) 637-643.

[3] S. Raisch and S. Krakowski, Artificial intelligence and management: The automation-augmentation paradox, Acad. Manage. Rev. 46(1) (2021) 192–210.

[4] Y. R. Shrestha, S. M. Ben-Menahem, and G. von Krogh, Organizational decision-making structures in the age of artificial intelligence, Calif. Manage. Rev. 61(4) (2019) 66–83.

[5] M. O. Riedl, Human-centered artificial intelligence and machine learning, Hum. Behav. Emerg. Technol. 1(1) (2019) 33–36.

[6] I. Seeber, E. Bittner, R. O. Briggs, T. de Vreede, G. J. de Vreede, A. Elkins, R. Maier, A. B. Merz, S. Oeste-Reiß, N. Randrup, G. Schwabe, and M. Söllner, Machines as teammates: A research agenda on AI in team collaboration, Inf. Manage. 57(2) (2020) 103174.

[7] A. Maedche, C. Legner, S. Morana, A. Benlian, B. Berger, T. Hess, H. Gimpel, O. Hinz, and M. Söllner, AI-based digital assistants: Opportunities, threats, and research perspectives, Bus. Inf. Syst. Eng. 61(4) (2019) 535–544.

[8] M.-H. Huang and R. T. Rust, Artificial intelligence in service, J. Serv. Res. 21(2) (2018) 155–172.

[9] J. M. Logg, J. A. Minson, and D. A. Moore, Algorithm appreciation: People prefer algorithmic to human judgment, Organ. Behav. Hum. Decis. Process. 151 (2019) 90–103.

[10] J. D. Lee and K. A. See, Trust in automation: Designing for appropriate reliance, Hum. Factors 46(1) (2004) 50–80.

[11] A. Adadi and M. Berrada, Peeking inside the black-box: A survey on explainable artificial intelligence (XAI), IEEE Access 6 (2018) 52138–52160.

[12] W. Wang and I. Benbasat, Recommendation agents for electronic commerce: Effects of explanation facilities on trusting beliefs, J. Manage. Inf. Syst. 23(4) (2007) 217–246.

[13] N. Pfeuffer, A. Benlian, H. Gimpel, and O. Hinz, Anthropomorphic information systems, Bus. Inf. Syst. Eng. 61(4) (2019) 523–533.

[14] K. Sowa, A. Przegalinska, and L. Ciechanowski, Cobots in knowledge work: Human-AI collaboration in managerial professions, J. Bus. Res. 125 (2021) 135–142.

[15] D. Siemon, Elaborating team roles for artificial intelligence-based teammates in human-AI collaboration, Group Decis. Negot. 31(5) (2022) 871–912.

[16] S. Bankins, A. C. Ocampo, M. Marrone, S. L. D. Restubog, and S. E. Woo, A multilevel review of artificial intelligence in organizations: Implications for organizational behaviour research and practice, J. Organ. Behav. 45(2) (2024) 159–182.

[17] I. Rahwan, M. Cebrian, N. Obradovich, J. Bongard, J.-F. Bonnefon, C. Breazeal, J. W. Crandall, N. A. Christakis, I. D. Couzin, M. O. Jackson, N. R. Jennings, E. Kamar, I. M. Kloumann, H. Larochelle, D. Lazer, R. McElreath, A. Mislove, D. C. Parkes, A. Pentland, M. E. Roberts, A. Shariff, J. B. Tenenbaum, and M. Wellman, Machine behaviour, Nature 568(7753) (2019) 477–486.

[18] K. Goddard, A. Roudsari, and J. C. Wyatt, Automation bias: A systematic review of frequency, effect mediators, and mitigators, J. Am. Med. Inform. Assoc. 19(1) (2012) 121–127.

[19] J. vom Brocke, W. Maaß, P. Buxmann, A. Maedche, J. M. Leimeister, and G. Pecht, Future work and enterprise systems, Bus. Inf. Syst. Eng. 60(4) (2018) 357–366.

[20] L. Qiu and I. Benbasat, Evaluating anthropomorphic product recommendation agents: A social relationship perspective to designing information systems, J. Manage. Inf. Syst. 25(4) (2009) 145–182.

Keywords:

Artificial Intelligence, Digital Assistants, Future of Work, Human-AI Collaboration, Intelligent Agents.