Navigating Product Obsolescence: Leveraging AI for Innovation
In today's rapidly evolving market, every company must eventually confront the reality that products age and lose their competitive edge. There isn't a fixed expiration date for most offerings, but the subtle signs of obsolescence can quickly turn a market leader into a laggard if not addressed. Despite thriving sales, recognising when to refresh or innovate is crucial, especially with the long development timelines of many modern, complex products.
However, the innovation landscape is fraught with challenges. Corporate innovation has been lagging, with a notable decline in the quality and quantity of scientific output and patenting. Between 1945 and 2010, scientific paper production and patenting fell dramatically, posing significant hurdles for companies striving to keep up with the burgeoning expanse of scientific knowledge.
The Paths to Obsolescence
Several factors can render products obsolete:
Short Product Lifecycles: Breakthrough products like new drugs often have limited windows of profitability before competitors erode market share. For instance, the development of Gleevec in 2001 revolutionised the treatment of Chronic Myeloid Leukemia (CML), but by 2006, other effective inhibitors emerged, challenging its dominance.
Changing Business Contexts: As markets evolve, products must meet new demands. The rise of electric vehicles has increased the need for superior noise-reducing sealants, pushing companies like Cooper Standard to innovate beyond their traditional offerings.
Complex Combinations: The development of new products often involves navigating vast combinations of materials and techniques. Pirelli, for example, has the daunting task of selecting the right materials for tyre manufacturing from over 200 different substances.
The Role of AI in Innovation
In light of these challenges, many companies are exploring how artificial intelligence (AI) can accelerate and enhance innovation. Our research suggests that the effective use of AI within organisations determines its potential as a powerful tool.
AI can manage vast amounts of data, helping firms navigate information overload and predict performance in complex product development scenarios. For example, Moderna used AI to expedite the development of vaccine candidates during the COVID-19 pandemic, significantly reducing research and development timelines.
However, AI's effectiveness varies based on the type of innovation pursued. Our findings suggest that AI is most beneficial for the following:
Recombinative Innovation: AI excels at combining existing technologies in new ways. Firms that focus on process improvement and diverse recombination of technologies tend to benefit the most from AI capabilities. These firms are typically 3 to 7% more productive and generate more patents annually.
Incremental Improvements: AI can enhance incremental innovation by optimising existing processes and products, leading to continuous improvement and efficiency gains.
Conversely, AI is less effective for radical innovation, which often requires human creativity and the ability to interpret sparse data. Using AI inappropriately for such innovation can even hinder performance, as seen in the development of breakthrough drugs like Artemisinin, where human ingenuity plays a pivotal role.
Applying AI to Innovation Processes
To effectively leverage AI for innovation, companies should consider the following:
Fast Follower Strategy: Use AI to improve and recombine existing products and technologies, amplifying existing capabilities.
Data Deluge Management: Employ AI to synthesise information from diverse fields, facilitating broad technological combinations.
Choice Overwhelm: Invest in AI projects that support recombination and hire AI talent to evaluate suggested combinations.
Radical Innovation Dependence: Use AI to build on initial radical innovations, but be cautious about protecting intellectual property to maintain competitive advantage.
Conclusion
AI holds significant promise for driving innovation, but its success depends on strategic implementation. Companies that focus on recombination and process improvement are likely to see the most benefits, while those aiming for radical innovation must carefully balance AI's capabilities with human creativity. Understanding these nuances allows firms to better navigate the complex landscape of product obsolescence and stay ahead of the market.
Disclaimer: This information is for general knowledge and informational purposes only and does not constitute financial, investment, or other professional advice.