The AI-Augmented Orchestration:Project Managers as Strategic Actors in Collaborative Innovation

1. Introduction and Research Motivation<br/>Collaborative innovation has become a dominant mode of innovation in technology-intensive<br/>industries, where firms increasingly rely on networks of partners, suppliers, and research<br/>institutions to access distributed knowledge and share innovation risks (Bäck and Kohtamäki,<br/>2015). While collaboration expands innovation potential, it also increases coordination<br/>demands, interdependence, and strategic alignment challenges for Project Managers (PMs).<br/>PMs are often caught in a "coordination trap" where traditional administrative duties impose an<br/>overwhelming cognitive load, preventing them to engage in higher-level strategic activities and<br/>align diverse resources toward long-term innovation objectives.<br/>Meanwhile, Artificial Intelligence (AI) is transforming innovation management by enabling<br/>real-time data integration, predictive analytics, and advanced decision support (Zong and Guan,<br/>2024). Despite growing investments in AI, we lack a clear explanation of how AI translates into<br/>improved collaborative innovation performance.<br/>Existing research treats AI in two distinct ways: as automation, where AI replaces human tasks,<br/>and as augmentation, where AI complements managers to enhance performance including<br/>innovation (Raisch and Krakowski, 2021). This study addresses the tension between<br/>technological potential and managerial realization by conceptualizing the PM as a Strategic<br/>Orchestrator, whose agency is unlocked by AI augmentation, in collaborative innovation<br/>projects. We argue that AI does not automatically improve innovation; rather, it functions as a<br/>"cognitive buffer" that absorbs operational overload, enabling a fundamental reallocation of<br/>attention toward the strategic activities that drive innovation performance (Nambisan et al.,<br/>2017).<br/>2. Theoretical Background and Research Gap<br/>This research is grounded in the synthesis of Resource Orchestration Theory (ROT) and the<br/>Attention-Based View (ABV). While the traditional resource-based view focuses on resource<br/>possession, ROT posits that innovation performance is realized through the active managerial<br/>routines of structuring (acquiring/divesting), bundling (integrating), and leveraging (deploying)<br/>resources (Sirmon et al., 2011). In collaborative innovation settings, these actions involve<br/>aligning interdependent partners and integrating distributed knowledge across organizational<br/>boundaries. However, the literature often implicitly assumes that managers possess unlimited<br/>cognitive capacity to perform these complex routines. The ABV provides a necessary<br/>correction, acknowledging that managerial attention is a finite and situated resource (Ocasio,<br/>1998).<br/>In collaborative innovation projects, characterized by high interdependence and coordination<br/>intensity, PMs face a "coordination bottleneck" where the high cognitive load of administrative<br/>duties prevents the allocation of attention toward the strategic orchestration of project resources.<br/>Although research on AI highlights its efficiency and decision-support benefits, we lack a<br/>theoretical explanation of how AI reshapes managerial attention allocation and, in turn, enables<br/>strategic orchestration in collaborative innovation contexts (Bäck and Kohtamäki, 2015). This<br/>study bridges these perspectives by examining how AI-augmented orchestration serves as the<br/>primary mechanism linking technological integration to innovation novelty and relational<br/>value.<br/>3. Conceptual Model and Hypotheses<br/>Current literature suggests that both organizations and PMs benefit from AI augmentation in<br/>areas such as project planning and new product development (Müller et al., 2024). We propose<br/>a model where AI augmentation enhances collaborative innovation performance through its<br/>positive effect on PMs’ strategic orchestration. Grounded in the ABV, the model suggests that<br/>AI mitigates the cognitive load of routine project management, allowing PMs to redirect<br/>attention towards the strategic dimensions of ROT.<br/>H1a-c: AI augmentation is positively associated with managers’ ability to structure (a), bundle<br/>(b) and leverage (c) resources in innovation projects.<br/>H2: AI augmentation is positively associated with project managers’ strategic orchestration in<br/>innovation projects.<br/>H3: Strategic orchestration by project managers is positively associated with innovation<br/>performance.<br/>4. Research Design and Methodology<br/>To test these hypotheses, this study adopts a quantitative, survey-based research design. Data<br/>will be collected from PMs (including engineering managers) and innovation professionals<br/>involved in collaborative projects across technology-intensive industries. Established and<br/>adapted multi-item scales will be measured using a five-point Likert scale. Structural Equation<br/>Modeling (SEM) will be employed to assess the direct, mediating, and moderating effects<br/>proposed in the model.<br/>5. Expected Contributions and Implications<br/>First, this study advances ROT by introducing attention reallocation as a mechanism enabling<br/>strategic orchestration in collaborative innovation contexts. By integrating the ABV, we<br/>demonstrate that AI augmentation relaxes attentional constraints, allowing PMs to more<br/>effectively structure, bundle and leverage distributed resources (Helfat and Peteraf, 2015).<br/>Second, the study contributes to human–AI research by conceptualizing AI as an attentionshaping<br/>and coordination-enabling technology rather than a mere automation tool (Raisch and<br/>Krakowski, 2021). Rather than directly driving performance, AI enhances managerial judgment<br/>by freeing cognitive capacity for higher-order strategic activities in collaborative innovation<br/>(Nambisan et al., 2017).<br/>Third, the study contributes to collaborative innovation research by establishing strategic<br/>orchestration as the central mechanism linking AI augmentation to performance (Dhanaraj and<br/>Parkhe, 2006). By clarifying how AI-enabled attention reallocation strengthens managers’<br/>ability to align partners and integrate knowledge, we provide a process-based explanation of<br/>how technological potential is transformed into collaborative innovation performance<br/>(Randhawa et al., 2016).<br/>Managerial Implications: organizations should view AI implementation not only as a<br/>productivity initiative but as a strategic enabler of managerial attention. By reducing routine<br/>coordination burdens, AI may allow PMs to focus on higher-level orchestration activities that<br/>are particularly critical in complex collaborative innovation projects.<br/>References<br/>Bäck, I. and Kohtamäki, M. (2015), “Boundaries of R&amp;amp;D collaboration”, Technovation,<br/>Vol. 45–46, pp. 15–28, doi: 10.1016/j.technovation.2015.07.002.<br/>Dhanaraj, C. and Parkhe, A. (2006), “Orchestrating Innovation Networks”, Academy of<br/>Management Review, Vol. 31 No. 3, pp. 659–669, doi: 10.5465/amr.2006.21318923.<br/>Helfat, C.E. and Peteraf, M.A. (2015), “Managerial cognitive capabilities and the<br/>microfoundations of dynamic capabilities”, Strategic Management Journal, Vol. 36 No.<br/>6, pp. 831–850, doi: 10.1002/smj.2247.<br/>Müller, R., Locatelli, G., Holzmann, V., Nilsson, M. and Sagay, T. (2024), “Artificial<br/>Intelligence and Project Management: Empirical Overview, State of the Art, and<br/>Guidelines for Future Research”, Project Management Journal, Vol. 55 No. 1, pp. 9–15,<br/>doi: 10.1177/87569728231225198.<br/>Nambisan, S., Lyytinen, K., Majchrzak, A. and Song, M. (2017), “Digital Innovation<br/>Management: Reinventing Innovation Management Research in a Digital World”, MIS<br/>Quarterly, Vol. 41 No. 1, pp. 223–238, doi: 10.25300/MISQ/2017/41:1.03.<br/>Ocasio, W. (1998), “Towards an Attention-Based View of The Firm”, Strategic Management<br/>Journal, Vol. 18 No. Summer Special Issue, pp. 187–206.<br/>Raisch, S. and Krakowski, S. (2021), “Artificial Intelligence and Management: The<br/>Automation–Augmentation Paradox”, Academy of Management Review, Vol. 46 No. 1,<br/>pp. 192–210, doi: 10.5465/amr.2018.0072.<br/>Sirmon, D.G., Hitt, M.A., Ireland, R.D. and Gilbert, B.A. (2011), “Resource Orchestration to<br/>Create Competitive Advantage”, Journal of Management, Vol. 37 No. 5, pp. 1390–1412,<br/>doi: 10.1177/0149206310385695.<br/>Zong, Z. and Guan, Y. (2024), “AI-Driven Intelligent Data Analytics and Predictive Analysis<br/>in Industry 4.0: Transforming Knowledge, Innovation, and Efficiency”, Journal of the<br/>Knowledge Economy, Vol. 16 No. 1, pp. 864–903, doi: 10.1007/s13132-024-02001-z.