Too much AI could hurt corporate innovation

A new study warns that excessive dependence on AI may ultimately weaken corporate creativity and trap firms in cycles of technological imitation. Researchers analyzing more than 25,000 firm-year observations from Chinese manufacturing companies found that AI adoption improves innovation only up to a critical threshold, after which its benefits begin to decline as firms increasingly rely on standardized AI-driven solutions that reduce differentiation and originality.

The study, titled "The Inverted-U Relationship Between AI and Corporate Innovation Performance," published in Systems, reveals a statistically significant inverted-U-shaped relationship between AI and corporate innovation performance, with innovation gains peaking at a specific AI adoption threshold before diminishing returns emerge.

Researchers identify an "AI empowerment trap" as excessive AI adoption reduces innovation quality

AI has fundamentally reshaped modern innovation systems by accelerating knowledge processing, improving resource allocation, enhancing predictive capabilities, and optimizing research and development processes. AI systems are increasingly integrated into product development, manufacturing design, industrial automation, supply-chain optimization, and decision-making systems across global manufacturing industries.

Researchers found that moderate AI adoption significantly improves innovation performance because AI expands firms' knowledge boundaries, supports knowledge coupling, and increases efficiency in innovation-related activities. Machine learning and deep learning systems allow companies to process large datasets, identify technological opportunities faster, reduce trial-and-error costs, and optimize R&D pathways.

AI technologies now support innovation across nearly every stage of industrial activity, from problem identification and product design to commercialization and production optimization. This broad integration enables firms to improve both innovation speed and operational efficiency simultaneously. However, the positive impact of AI does not continue indefinitely. Instead, firms eventually reach a turning point beyond which additional AI adoption begins to suppress innovation performance.

Using fixed-effects econometric models and U-tests, the researchers identified a clear inverted-U-shaped relationship between AI adoption and innovation outcomes. The linear AI coefficient remained significantly positive, while the quadratic term became significantly negative, confirming the non-linear relationship statistically. The estimated turning point of the curve was calculated at 2.948, representing the level at which AI's innovation-enhancing effect reaches its maximum.

According to the researchers, firms operating below this threshold benefit from AI-driven innovation gains, while firms exceeding it begin experiencing diminishing returns and eventually declining innovation performance. The study describes this phenomenon as an "AI empowerment trap," where firms become excessively dependent on AI-generated solutions and gradually lose differentiated innovation capabilities. Researchers argue that overreliance on AI may lead companies to adopt increasingly similar technological pathways, reducing originality and weakening competitive advantage.

To examine this mechanism directly, the researchers conducted patent text similarity analysis using Sentence-BERT vectorization and cosine similarity measurements. Firms with high levels of AI adoption exhibited significantly greater similarity in their technical language and patent content than firms with lower AI adoption. The average similarity score among high-AI firms reached 0.324 compared with 0.187 among low-AI firms, confirming that extensive AI usage was associated with increasing technological homogenization.

The widespread use of standardized AI systems may lower technological entry barriers but simultaneously encourage convergence around similar innovation solutions. As more firms depend on the same AI architectures, algorithms, datasets, and optimization methods, innovation diversity may decline across industries.

AI systems depend heavily on data availability and computational resources. When firms rely excessively on AI-generated recommendations or predictive systems, they risk weakening their own independent problem-solving capabilities and reducing long-term creative adaptability.

The turning point identified in the study is context-dependent rather than universal. The threshold may shift across industries, policy environments, technological maturity levels, and institutional contexts. Nevertheless, the findings suggest that AI investment strategies must be managed carefully rather than treated as an unlimited source of innovation gains.

Absorptive capacity: a key mechanism shaping AI-driven innovation

Organizational absorptive capacity plays a primary role in determining whether AI adoption translates into successful innovation outcomes. Researchers define absorptive capacity as a firm's ability to recognize the value of external knowledge, assimilate that knowledge internally, and commercialize it effectively. Firms with strong absorptive capacity can integrate AI technologies more efficiently because they possess stronger internal learning systems, adaptive organizational structures, and better knowledge transformation capabilities.

The study measured absorptive capacity using entropy-weighted composite indicators based on R&D expenditure intensity and R&D personnel ratios. The results showed that absorptive capacity partially mediates the relationship between AI and innovation performance. AI adoption significantly improved absorptive capacity initially, but this effect also followed an inverted-U-shaped pattern.

Bootstrap mediation analysis confirmed that absorptive capacity acts as an important but incomplete transmission mechanism. The indirect effect remained statistically significant, suggesting that firms capable of transforming AI-generated knowledge into internal organizational learning gain stronger innovation benefits from AI integration.

According to the researchers, absorptive capacity operates across three progressive dimensions: identifying technological value, transforming knowledge internally, and applying innovation operationally. Firms with stronger absorptive capacity can adapt more quickly to AI systems and extract more value from technological investments.

AI transformation is not simply a matter of introducing new technologies into the workplace, the study insists. Instead, successful AI integration requires organizations to redesign internal learning processes, strengthen knowledge management systems, and improve cross-functional coordination.

Researchers also found that AI-driven innovation depends heavily on organizational flexibility and strategic resource allocation. Firms capable of dynamically adjusting internal structures and workflows can better integrate AI into research, production, and commercialization processes.

The researchers also stressed that absorptive capacity alone cannot prevent firms from falling into the AI empowerment trap. Other mechanisms, including organizational restructuring, process optimization, and innovation governance, also shape how AI influences long-term innovation performance.

Industry-academia collaboration is also crucial in strengthening absorptive capacity. Researchers recommend that firms establish AI-focused training programs, joint laboratories, internal AI think tanks, and proprietary knowledge systems to improve their ability to adapt to rapidly evolving AI technologies.

The paper further states that enterprises should prioritize customized AI models tailored to their own technological needs rather than relying exclusively on standardized off-the-shelf systems. Proprietary AI development may help firms avoid homogenization pressures while maintaining differentiated innovation capabilities.

Market concentration and ownership structure strongly influence AI's innovation effects

The study found that external market structures significantly influence the relationship between AI adoption and innovation performance. Researchers used the Herfindahl-Hirschman Index to measure industry concentration and found that market concentration negatively moderates the inverted-U relationship between AI and innovation performance. As industry concentration increases, the beneficial effects of AI decline more rapidly and the innovation turning point shifts leftward.

In highly concentrated markets dominated by a small number of large firms, companies tend to rely more heavily on standardized AI solutions designed to preserve existing competitive positions rather than pursue breakthrough innovation. This accelerates technological convergence and weakens incentives for differentiated innovation.

On the other hand, firms operating in more competitive industries face stronger pressure to innovate and differentiate themselves. Competitive markets therefore encourage more effective use of AI for experimentation, adaptation, and technological diversification.

The researchers argue that market competition itself acts as a counterbalance to homogenization because firms in fragmented markets must continuously search for novel innovation pathways to survive.

Ownership structure also produced significant differences in AI's impact on innovation. The study found that non-state-owned enterprises exhibited a higher AI innovation threshold than state-owned enterprises. The turning point for non-state-owned firms reached 2.917 compared with 2.212 for state-owned enterprises.

According to the researchers, state-owned enterprises often benefit from greater policy support and resource access during the early stages of AI adoption. However, bureaucratic rigidity and administrative constraints may lead to inefficient AI investment and redundant technological deployment, causing returns to decline earlier.

Non-state-owned firms, while facing higher initial experimentation costs, tend to adopt more targeted AI investment strategies because of stronger resource constraints and market pressures. This enables them to sustain AI-driven innovation gains over longer periods.

The study also revealed substantial heterogeneity across industries. High-tech firms demonstrated significantly greater resilience to the AI empowerment trap than low-tech firms. The turning point for high-tech enterprises reached 3.875, compared with only 1.131 for low-tech firms.

Researchers explain that high-tech firms are more likely to develop proprietary algorithms, customized AI architectures, and specialized innovation systems that reduce dependence on generic AI tools. Low-tech firms, by contrast, often rely more heavily on standardized AI applications, increasing the risk of technological convergence and imitation.

Policymakers should focus on promoting open data systems, algorithmic transparency, and differentiated AI governance frameworks to reduce innovation risks associated with technological concentration and data monopolies. The study also calls for stronger government support for customized AI model development, secure data-sharing systems, and industry-specific AI innovation ecosystems capable of reducing homogenization pressures across sectors.