Artificial intelligence product development
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Artificial Intelligence in New Product Development: Applications and Benefits
Artificial intelligence (AI) is rapidly transforming the landscape of product development across industries. Early adopter firms are leveraging AI to accelerate innovation, reduce development times, and improve the success rate of new products, with some reporting up to 50% reductions in development cycles and more effective idea generation and testing processes 235. AI is now being used throughout the entire new product development (NPD) process, from initial idea generation and concept creation to design, engineering, and post-launch analysis 235.
AI-Driven Idea Generation, Design, and Testing
AI tools are increasingly integrated with traditional design methodologies and technological platforms, enabling practitioners to generate novel ideas quickly and test product concepts more efficiently. For example, companies like GE use digital models and twins to rapidly test turbine designs, while BASF employs AI to identify new molecules for customer formulations 235. These applications not only speed up the process but also enhance the quality and appeal of new product ideas 51.
However, challenges remain, such as the need for better control over AI-generated outputs (e.g., images that are not easily parametrically controlled) and the additional work required to translate these outputs into usable 3D models . Despite these hurdles, the integration of AI with design and engineering tools is becoming clearer and more impactful 110.
AI in Product Lifecycle Management and Manufacturing
AI is also playing a significant role in product lifecycle management (PLM), supporting activities from product design and manufacturing to service and recycling. In smart manufacturing environments, AI enables more agile stakeholder interactions, intelligent manufacturing processes, and the use of interactive digital platforms 108. Manufacturing firms benefit from AI by facilitating iterative product innovation, but they must also manage risks such as data leakage, over-reliance on automated decision-making, and unpredictable AI behaviors .
To address these risks, firms are developing guidelines for data management, integrating human expertise with AI analysis, and establishing standards for managing intelligent devices . These practices help ensure that AI-driven innovation remains both effective and secure.
AI-Enabled Product-Service Innovation and Business Models
The adoption of AI is also driving innovation in product-service systems and business models. AI supports data-driven capabilities, enables digitally enabled business model innovation, and facilitates smart design changes focused on sustainability . However, barriers to adoption persist, including high perceived costs, lack of corporate readiness, and ethical concerns 57. Despite these challenges, the literature highlights the transformative potential of AI for achieving competitive advantage and long-term growth in product development 47.
Sector-Specific Insights and Emerging Frameworks
In sectors like FinTech, AI-based innovation is shaped by factors such as organizational agility, data availability, investment levels, and reliance on third-party platforms. Different process configurations and theoretical frameworks are emerging to help firms bridge the gap between concept and product, accelerate innovation, and better leverage AI's potential . These frameworks can serve as diagnostic tools for organizations seeking to optimize their AI-driven product development strategies .
AI in Software Product Development
AI is also revolutionizing software development by automating code creation, reducing repetitive tasks, and increasing overall efficiency. Tools like OpenAI's GPT are being used to generate code, compare AI-generated solutions to manual coding, and streamline the software development process . This shift is making software engineering faster and more adaptable to changing demands .
Conclusion
Artificial intelligence is fundamentally reshaping product development by enabling faster innovation, more effective idea generation, and improved product lifecycle management. While challenges such as data management, integration with existing processes, and adoption barriers remain, the benefits of AI—including reduced development times, enhanced product quality, and new business model opportunities—are driving its rapid adoption across industries. As AI technologies continue to evolve, organizations that embrace these tools and address associated risks will be well-positioned to lead in the next era of product innovation 2357810.
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