95% of New Product Launches Fail. Here’s How AI Can Help You Succeed

The Data 

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   Our analysis found that product innovation and product launches came up in 46% of earnings calls in 2024. However, 95% of new product launches fail.

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   In our AI Hub, 10% of case studies pertain to product development, product strategy, and AI innovations.

The Road to Successful Product Launches Is Paved with AI 

Few companies have mastered the art of the product launch. In fact, the late Clayton Christensen, a professor at Harvard Business School, once claimed that 95% of product launches fail. It takes strong orchestration, innovation, customer data, and supply chain execution to make new products fly, and it’s a challenge leaders are looking to solve. 

Zero100 analysis has found that supply chain leaders are focused on upstream activities that will successfully increase their product offering. Our data scientists found that 46% of 2024 earnings calls across 150 brands included discussion of product innovation or new product launches. For example, the Estée Lauder Companies is looking to accelerate its speed-to-market capabilities, while Nike has expressed its dedication to innovation, mentioning Nike Air technology ahead of the Paris Olympics. 

For supply chain leaders, successful product launches include upstream strategy and capabilities. And as demand chain innovations continue to outpace supply-side capabilities, it uncovers an opportunity for leaders to lean into a data-centric strategy – on that involves leveraging AI to harness the consumer data explosion of the past few years. 

Zero100’s AI Hub contains more than 650 AI case studies across all Loop functions. Ten percent of the use cases in the AI Hub are focused on product development, product strategy, or product innovation.

Let’s dive deeper into a few examples to see how AI can help increase the success rate of new product launches.  

AI in Action 

Unilever uses AI to enhance product strategy and identify market opportunities. The company partnered with Commerce.AI to analyze nearly half a million Amazon US reviews across 130,000 products. The AI-driven analysis identified gaps in Unilever’s Dove product portfolio, informed new product launches, and guided acquisition strategies. This system enables Unilever to leverage large-scale, structured data from customer feedback, resulting in faster and more accurate product decisions. 

Arcelor Mittal has increased the productivity of its product development by combining patent research and data extraction into R&D processes. Machine learning and natural language processing tools structure data from patent research into Excel and a database platform, allowing the R&D team to review categorized and filtered research based on priorities and relevance. The result? Ninety-four percent data extraction categorization accuracy and a shortened research timeline (from months to minutes). 

Nestlé uses AI and computer vision to gain insights from customers’ facial expressions. For new product releases, it runs sensory panels across product formulations to gauge customer sentiment. This system monitors and organizes data from participants to determine their true feelings towards a product in terms of its taste, impact on health, and sustainability. AI accelerates the speed of product testing, plus increases accuracy, which reduces the amount of overhead it takes to run large sensory panels, especially in different geographies. 

The Takeaway  

Take control of the demand-driven revolution and use AI to organize data into meaningful insights that accelerate innovation. Start by defining your data strategy around consumers and how it will influence your upstream capabilities. Then, collaborate with teams cross-functionally to understand how the new data can guide your next product launch.  

Reach out to us at hello@zero100.com to learn more about our AI frameworks and the Zero100 AI Blueprint, which is how we help companies mold their data-centric supply chain strategies and prioritize AI investments. 

Methodology  

Zero100’s proprietary data and analytics are a combined effort between our data scientists and research analysts. We provide data-first insights matched with our own research-backed points of view and bring this analysis to life via real-world case examples being led by supply chain practitioners today.   

For this study, we analyzed 542 supply chain organizations and 600+ unique references of brands scaling AI implementations from The Zero100 AI Hub. We’ve also analyzed 2024 earnings calls for 150 brands pulling relevant insights and categorizing keywords, phrases, and sentiments. 

Further Reading

• Research Report Preview: Getting Practical with AI: Unveiling The Zero100 AI Hub
• The Zero100 Podcast: What’s a TikTok Supply Chain?
• The Signal: Mesoeconomics: A Data-Centric Approach to Supply Chain Strategy