June 19, 2024

AI Redefining Product Development: Netflix, BMW, and PepsiCo

Artificial Intelligence (AI) is changing product development, cutting development times by up to 50% for early adopters. Keep reading to discover how AI enhances speed, efficiency, and innovation across the product development cycle.

6 min read

Meet our Editor-in-chief

Paul Estes

For 20 years, Paul struggled to balance his home life with fast-moving leadership roles at Dell, Amazon, and Microsoft, where he led a team of progressive HR, procurement, and legal trailblazers to launch Microsoft’s Gig Economy freelance program

Gig Economy
  • The pressure to accelerate innovation is increasing, with top-tier companies earning nearly 2X as much revenue from new products and services that didn’t exist a year before.

  • AI enhances decision-making and reduces errors, leading to higher ROI and streamlined product development, as seen with companies like Corbion.

  • Companies like Netflix, BMW, and PepsiCo use AI to improve product strategies and customer engagement through personalized recommendations, efficient vehicle production, and innovative product designs.

Paul Estes

Dell, Microsoft, Amazon, and several venture-backed startups

They can understand how a software programmer creates software, a designer creates a design, or an artist creates art. These models are beginning to understand the thinking behind the creation, which is both an exciting and scary part of it. But this applies to pretty much all stages of product development because you can now supercharge the human creativity component.” – Deepam Mishra, Amazon Web Services (AWS) Senior Advisor to Startups and AI expert. Source

Companies are under immense pressure to constantly innovate and deliver products that captivate their customers and improve their ROI. A McKinsey survey reveals that 84% of executives agree that innovation is the key to growth. Interestingly enough, top-tier companies earned nearly twice as much revenue from products and services that didn’t exist a year before. The data shows constant innovation is critical to thriving in a competitive market. 

However, traditional product development methods often need help to keep pace with rapidly evolving market demands and consumer expectations. Around 40% of all new products fail. Annually, this leads to $215 billion in lost innovation expenses in the US, even without factoring in the potential revenue from failed products. This is where artificial intelligence (AI) can step in. AI can help make product development more efficient, from brainstorming ideas to designing and manufacturing. As a result, products reach the market faster, are more innovative, and of higher quality. It offers businesses a powerful tool to streamline processes and gain a competitive edge. 

According to a Forbes survey, 44% of businesses say that AI improves decision-making, and 48% said AI helps avoid mistakes, contributing to a higher business ROI. For example, food and biochemical companies like Corbion can use AI to improve their product development. As Ashley Robertson, Global Marketing Director at Corbion, explains, “Artificial intelligence has the potential to significantly impact how we identify and select ingredients for new product development. By using simulation and predictive analysis, AI can expedite the formulation process. This would enable faster iterations based on feedback, which could streamline the journey from concept to market.”

In this article, we'll examine how businesses use AI to accelerate product innovation, boost growth, and increase their return on investment (ROI). We will also review case studies to examine how companies have successfully implemented these strategies to improve their ROI.

Solving Product Innovation Challenges with AI

Companies across different industries are grappling with the challenge of accelerating time-to-market for their products. The ones that actively promote the culture of innovation are 3.5 times more likely to outperform their peers. In contrast, companies needing to innovate faster miss out on opportunities and risk losing market share.

For instance, Nike has faced criticism for needing to be faster to innovate, impacting their sales. JD Sports Fashion Plc pointed out that Nike's lack of new products contributed to a sales slump in their UK retail chain. JD Sports’ CEO Régis Schultz explained, "Nike has been so successful but they just stopped a little bit bringing in new stuff." This led to consumer boredom, while competitors like Adidas and New Balance thrived by consistently introducing new and exciting products. Schultz went on to say, "Shoppers get bored very quickly. If you don’t bring in new stuff, product, innovation, or color, I think the demand is suffering."

In addition to innovation challenges, businesses face hurdles when they attempt to leverage data-driven insights to enhance efficiency, reduce manual efforts, and maintain cost-effectiveness while staying competitive. Agile product development often involves analyzing vast amounts of data to gain insights into customers' wants and needs. However, managing and using this data efficiently can take time and effort. According to various surveys, up to 85% of business data projects fail or run into serious trouble. 

AI has the potential to transform product development by addressing these challenges head-on. Using machine learning, natural language processing, and advanced analytics, AI can speed up the entire development process, from ideation to launch. In fact, companies that use AI in their product development processes can reduce the time to market by 20-40% and cut development costs by 20-30%. Next, we'll explore case studies to see how companies have successfully used AI to improve and speed up their product development processes.

AI-Powered Production: BMW’s Strategy for Faster Time-to-Market

BMW Group, a leader in the automotive industry, is strategically using AI in its vehicle assembly process. By increasing production efficiency, accuracy, and quality, they have significantly reduced the time to market for new models. Their AI innovations like Car2X and AIQX have been key to these achievements.

BMW's Car2X technology enables real-time communication and interaction between vehicles and the production system during assembly. Another AI-driven platform, AIQX, automates quality assurance using sensor technology and AI. By deploying camera systems and sensors along the assembly line, AIQX conducts visual and acoustic inspections, detects anomalies, and provides real-time feedback to employees. At BMW’s Spartanburg plant, AI technologies manage the placement of metal studs by robots, ensuring precision and reducing human intervention.

The image above illustrates how cameras are placed on cars in the BMW production line for quality assurance using AI. Source

The implementation of AI in the assembly process has not only increased precision but also significantly reduced the time required for quality inspections. “We’re achieving five times what we thought was possible before with what AI is achieving now,” says BMW Group Manager Curtis Tingle. The AI-driven stud correction system alone has been a game-changer, saving BMW over $1 million annually and eliminating the need for six assembly line workers. 

PepsiCo's AI-Powered Innovation: Designing the Perfect Cheetos

PepsiCo, a significant food and beverage industry player, has turned to generative AI and deep reinforcement learning to explore design possibilities and optimize product features. They’ve created innovative solutions that appeal to customers. By using AI, PepsiCo perfected the shape and flavor of Cheetos. AI allowed them to experiment with various combinations and precisely control product characteristics. 

PepsiCo uses a "machine brain" to ensure consistent product quality during production. This AI agent autonomously controls extruder settings, adjusting parameters like temperature and moisture to maintain the desired quality. Kevin McCall from Microsoft Research explained that this process involves creating a digital simulation model of the extruder and training the AI agent using deep reinforcement learning. Subject matter experts, like chemical engineers at PepsiCo, define the control objectives and connect them to the simulation model. The AI agent learns to make optimal decisions through practice.

Denise Lefebvre, PepsiCo SVP Foods R&D, stated, "We’ve trained a machine ‘brain’ to measure the Cheetos, look at them, and detect when adjustments are needed. This is the ideal application for AI because we know really well what consumers love and value about Cheetos, and we can train a system to understand that.”

PepsiCo also uses GenAI in its marketing campaigns. They’ve significantly reduced the campaign cycle from 6-9 months to 3-4 months, enabling faster market entry. Athina Kanioura, PepsiCo’s Chief Strategy and Transformation Officer, spoke about how GenAI achieved Cheetos’ “perfect shape, perfect flavor” with relevant customer feedback. Feedback-driven adjustments to the shape and flavor of Cheetos have led to a business ROI with a 15% increase in market penetration. It shows how AI can directly impact product success by aligning offerings more closely with consumer desires and optimizing design to enhance customer satisfaction.

Data-Driven AI: The Engine Behind Netflix's Phenomenal Growth

Netflix stands out in the highly competitive entertainment industry. It is available in 190 countries and has grown its revenue from $1.36 billion to over $26 billion in just 13 years. The secret to Netflix's success is its strategic adoption of AI, which has significantly improved the user experience on its streaming platforms.

Netflix uses AI to analyze vast amounts of data, including customer feedback, viewing habits, market trends, and product performance. Netflix’s data-driven approach enables it to derive actionable insights that guide its product development strategies. They can process data from their 223 million paid subscribers and identify patterns and behaviors using machine learning (ML) techniques. ML insights enable more accurate predictions and personalized recommendations, directly impacting user satisfaction and engagement.

The core of Netflix’s AI innovation is its personalized recommendation system. By analyzing users' viewing habits and the behavior of similar users, Netflix’s AI suggests content tailored to individual preferences. Here are some of the key elements that are analyzed:

  • Customer Feedback: Ratings, reviews, and direct feedback help refine recommendations and understand user satisfaction.
  • Viewing Habits: Netflix tracks what content users watch, how long they watch it, the time of day, and their interactions with the content (e.g., pausing, rewinding).
  • Market Trends: Analysis of popular genres and emerging trends helps Netflix stay ahead of viewer preferences.
  • Product Performance: Metrics like completion rates and rewatch rates gauge content effectiveness.

Netflix processes this data using machine learning techniques like collaborative filtering, content-based filtering, and deep learning models. Collaborative filtering analyzes similarities between users and their viewing habits, while content-based filtering looks at the characteristics of the content itself. Deep learning models, including neural networks, help capture more complex patterns in the data.

Additionally, Netflix personalizes the images, or "artwork," you see for each movie and show. Instead of showing everyone the same picture, Netflix shows different photos that you like. For example, if you often watch romantic movies, you might see an image for "Good Will Hunting" featuring a romantic scene with Matt Damon and Minnie Driver. If you prefer comedies, you might see an image with Robin Williams - highlighting the movie's funny moments.

Netflix does this using a technique called contextual bandits, which is a type of machine learning. This technique helps Netflix quickly learn and adjust to show the best images for each user. It continuously tests different photos to see which ones get the best reactions, ensuring you see the most likely to grab your attention.

The image above illustrates Netflix using AI to personalize Images. Source

According to CEO Ted Sarandos, while AI won't replace jobs, those who apply AI effectively will gain a competitive advantage: “AI is not going to take your job. The person who uses AI well might take your job.” The global video streaming market is expected to grow to $223.98 billion by 2028. Netflix's use of AI has provided a significant business ROI by capturing a substantial market share. Their user base has grown to over 260 million engaged users, contributing to a total revenue of $33.724 billion in 2023.

AI's Impact on Product Development: Speed, Innovation, and ROI

Integrating AI into product development has many benefits, such as speeding up time-to-market, boosting innovation, increasing customer satisfaction, optimizing resource allocation, and providing a competitive edge. By 2027, it's estimated that around 30% of manufacturers will adopt generative AI to improve their product development efficiency, and 53% of companies are already using AI to improve their production processes.

According to Laura LaBerge, an expert at McKinsey, "Top innovators invest more in R&D and digital technology, with a focus on tech that enables them to develop strategic differentiation." This strategic investment enhances their innovation ability, leading to significant business impacts. Companies that include AI in their product development cycles gain a significant competitive advantage by delivering innovative, customer-focused products more quickly and cost-effectively than their competitors. AI is a powerful tool that boosts your business ROI and secures long-term success in a competitive market.

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