Artificial Intelligence (AI) has emerged as a transformative force in various fields, including leadership and management. AI represents a distinct form of intelligence, particularly in its ability to share knowledge across multiple systems instantly—something humans cannot do. By leveraging AI, leaders can enhance decision-making, improve organizational efficiency, and foster a more innovative work environment wherein the continuous development and implementation of new ideas flourish. This, in turn, can lead to further innovations (Schumpeter 1934) and competitive advantages (Porter 1980). Innovations are essential to sustaining long-term organizational success.
AI and data are becoming key drivers for innovation and significantly impact the innovation process and innovation management aspects (Tekic & Füller 2023). AI has changed and continues to influence decision-making and innovation within organizations (Pietronudo et al. 2022). Advanced AI systems, such as predictive AI, generative AI, and agentic AI, enable leaders to anticipate trends, optimize strategies, and automate complex decision-making processes. Predictive AI involves applying statistical models, machine learning, and historical data analysis to forecast future events or outcomes. Generative AI refers to algorithms designed to produce original content, like text, code, images, video, or audio, by learning patterns from existing data. (Božić 2023.) Agentic AI refers to systems capable of autonomously pursuing complex goals with minimal human supervision, often through multi-step decision-making and adaptive behavior. (Shavit et al. 2023.) Business innovation in the coming era will be driven by the orchestration of these and other AI systems working in concert.
Moreover, AI-driven collaboration tools facilitate cross-border innovation, allowing geographically dispersed teams to work together effectively with limited resources (Enkel et al. 2020; Kanakaris et al. 2019). The increased need for both data and new technology, combined with a shrinking workforce due to demographic shifts, necessitates the adoption of open, AI-augmented collaborative innovation processes. AI-powered knowledge graphs, intelligent automation, and NLP-based virtual assistants are becoming integral to knowledge management and organizational learning. AI-powered knowledge graphs are structured representations of entities and their relationships, enhanced by artificial intelligence techniques that support their automated construction, enrichment, and reasoning capabilities. (Hogan et al. 2021.) AI-powered intelligent automation refers to the combination of robotic process automation and artificial intelligence, enabling more advanced, adaptable, and efficient process automation across organizations. (Ng et al. 2021.) NLP-based virtual assistants are AI-driven systems that use natural language processing to interpret user input and generate contextually relevant responses, enabling interactive, human-like communication. (Park 2023; Hoy 2018.) Together, these AI-driven technologies are accelerating digital transformation by enhancing organizational agility, streamlining collaboration, and unlocking a sustained competitive advantage in the race for innovation.
AI-Enhanced Innovation Management & Challenges
As AI becomes embedded in innovation teams, it plays a crucial role in accelerating R&D, enhancing product design, and streamlining operations (Siakas et al. 2016; Makkonen et al. 2023; Brusila-Meltovaara et al. 2024). However, despite these benefits, organizations often fail to fully harness AI-driven innovation due to technical and organizational barriers. These challenges are presented in Table 1.
| Challenge | Effect on AI-Driven Innovation |
| Data silos and integration issues | Limits AI’s ability to access and analyze comprehensive, high-quality data, reducing the accuracy and usefulness of insights. |
| Skill gaps in AI and data science | Prevents effective development, deployment, and maintenance of AI systems, which slows innovation and adoption. |
| Ethical and regulatory constraints | Introduces compliance hurdles; demands explainability, fairness, and transparency, which can delay implementation or limit certain use cases. |
| Ineffective collaboration with external AI experts | Leads to missed opportunities for knowledge transfer, innovation, and capability building, resulting in underutilized AI potential. |
To overcome these barriers, organizations must invest in AI literacy, foster interdisciplinary collaboration, and build AI governance frameworks to ensure responsible and effective AI use in innovation management (Hoy 2018). Inclusion of the aforementioned in the curricula of university students provides the basis for equipping future professionals with the necessary skills and knowledge to navigate and leverage AI in their respective fields. By fostering a comprehensive understanding of AI and interdisciplinary collaboration, universities can prepare students to drive responsible and innovative AI applications in their careers, ultimately contributing to the advancement and ethical management of AI technologies in various industries.
In parallel, industry must implement structured onboarding platforms that align employee engagement with organizational AI governance strategies. These platforms should operationalize principles and policies that foster digital trust, ensuring that AI adoption is not only technically proficient but also ethically grounded. By embedding governance into practice from the outset, organizations can create a culture of accountability and stewardship, reinforcing responsible innovation and public confidence in AI-enabled systems.
Authors
Kristiina Brusila-Meltovaara works as a principal lecturer in Faculty of Technology at LAB University of Applied Sciences.
Christine Thomson of Zitiso Inc. is an AI consultant and advisor to Fortune 500 companies, supporting their adoption and implementation of AI solutions.
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