Innovation Management in the Era of Artificial Intelligence and Automation

Pattie Maes

Innovation Management in the Era of Artificial Intelligence and Automation

Author(s)	Pattie Maes
Country	United States
Abstract	Innovation management has become a critical organisational capability in the era of Artificial Intelligence (AI) and automation, enabling businesses to develop new products, optimise processes, enhance customer experiences, and maintain sustainable competitive advantage. Rapid advancements in AI, Machine Learning (ML), Generative AI, robotic process automation (RPA), Big Data Analytics, cloud computing, Internet of Things (IoT), Digital Twin technology, and intelligent decision support systems are transforming how organisations generate, evaluate, implement, and commercialise innovations. Traditional innovation management approaches, often characterised by lengthy development cycles and intuition-based decision-making, are increasingly being replaced by agile, data-driven, and AI-assisted innovation ecosystems that support faster experimentation, predictive market analysis, and continuous improvement. This study investigates innovation management in the era of AI and automation using a qualitative and analytical research methodology based on secondary data collected from peer-reviewed journals, industry reports, innovation case studies, and international policy documents. The study examines the role of AI-driven innovation strategies, intelligent automation, digital transformation, knowledge management, organisational learning, predictive analytics, and collaborative innovation platforms in improving business performance and organisational resilience. The findings indicate that AI and automation significantly enhance innovation capability by accelerating research and development (R&D), improving product design, optimising business processes, supporting strategic decision-making, and enabling personalised customer experiences. Machine Learning algorithms facilitate opportunity identification, demand forecasting, idea evaluation, and risk assessment, while Generative AI assists in content creation, software development, product prototyping, and knowledge generation. Integration with Digital Twins, IoT, blockchain, cloud computing, and explainable AI further strengthens organisational agility, innovation efficiency, and operational excellence. Despite these benefits, challenges including ethical AI governance, workforce transformation, cybersecurity risks, intellectual property concerns, organisational resistance to change, data privacy, and regulatory compliance continue to influence AI adoption. The study concludes that organisations adopting responsible AI, continuous learning, interdisciplinary collaboration, and strategic innovation management practices will be better positioned to achieve sustainable growth and long-term competitiveness in the digital economy.
Keywords	Innovation Management, Artificial Intelligence, Automation, Digital Transformation, Machine Learning, Generative AI, Business Innovation, Industry 5.0, Organisational Learning, Intelligent Automation.
Field	Engineering
Published In	Volume 3, Issue 1, January-February 2021
Published On	2021-02-21

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Innovation Management in the Era of Artificial Intelligence and Automation

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