AI-driven design management: enhancing organizational productivity and innovation in design-oriented companies

What the paper says

Purpose Amid accelerating technological advancements and global competition, Artificial Intelligence (AI) technology is reshaping the foundational logic of design. As design-oriented enterprises typically operate as project-based organizations, AI redefines value creation in design projects and contributes to enhancing efficiency and innovation. This paper examines AI's impact on productivity and innovation from the perspective of project-based design enterprises. Design/methodology/approach The study uses panel data from A-share-listed design-oriented enterprises in China between 2014 and 2023. An AI lexicon was generated via natural language processing, and enterprise-level AI indicators were constructed through text analysis of annual reports and patents. A multi-dimensional empirical framework combining text and regression analysis was employed. Findings The study finds that integrating AI technologies significantly enhances Total Factor Productivity (TFP) and strengthens innovation. Acting as a bridge between project management efficiency and creativity, AI automates design project workflows, freeing teams for high-value tasks. Heterogeneity analysis indicates that state-owned and high-tech enterprises with robust digital infrastructure benefit most, underscoring the critical role of absorptive capacity. Research limitations/implications The study relies on data from Chinese A-share listed design enterprises, potentially limiting generalizability to SMEs or other geographic regions. Utilizing TFP as a proxy for project efficiency aggregates data at the firm level, lacking granular, micro-level insights into specific project workflows or design iteration logs. Future research should employ mixed-method approaches or case studies to validate how macro-efficiency gains manifest in daily operations. Additionally, while methods like Propensity Score Matching were used, future studies could leverage exogenous policy shocks to establish stricter causality regarding AI's impact on innovation. Practical implications This study guides managers in reconciling the tension between rigid project controls and creative iteration. Firms should prioritize “Generative Design” and “Predictive Analytics” to automate critical-path tasks and optimize resource allocation. Success requires a “Data-First” strategy, necessitating the digitization of historical assets to build robust infrastructure. Crucially, governance must shift from monitoring labor hours to curating algorithmic outputs. Managers should establish cross-functional teams combining data scientists and designers, transforming the workforce from pure creators to “AI curators” who refine intelligent outputs to maintain aesthetic quality. Social implications The findings frame AI as a tool for “augmentation” rather than substitution, highlighting the urgent need for workforce upskilling in AI literacy. While efficiency gains are evident, organizations must proactively address risks of technological displacement by investing in training that empowers designers to control the creative process as “co-pilots.” Furthermore, AI-enabled cost reductions could democratize access to premium design services for broader society. Ultimately, preserving the “human touch” within this high-efficiency framework is vital for ensuring a sustainable, ethically responsible human-AI collaborative ecosystem. Originality/value This study highlights the transformative potential of AI in design project management. It offers practical insights for firms aiming to integrate AI into their design project workflows, presenting pathways to enhance efficiency and innovation. Crucially, it bridges the gap between AI adoption and project management theory, providing valuable implications for managing the tension between efficiency and creativity in design projects.