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How Nike is Using AI to Transform Product Design, Customer Experience, and Operational Efficiency

Nike's strategic use of AI and Gen AI has dramatically improved the consumer experience, optimised the supply chain, and fueled product innovation. Nike maintains its competitive advantage in the global sportswear industry through smart acquisitions and cooperation with cutting-edge technologies.

Nike, Inc., a global leader in athletic footwear, apparel, and equipment, has been at the forefront of integrating artificial intelligence (AI) and generative AI (Gen AI) into its operations. This article delves into Nike’s history, its strategic use of AI, key leaders involved, investments, and collaborations with AI companies.

Overview of Nike

Nike was founded on January 25, 1964, as Blue Ribbon Sports by Phil Knight and Bill Bowerman. The company officially became Nike, Inc. on May 30, 1971. Headquartered in Beaverton, Oregon, Nike has grown into a global powerhouse in the sportswear industry, with a market presence in the Americas, Asia-Pacific, the Middle East, Africa, and Europe.

AI and Gen AI Initiatives

1. Nike Fit (2019)

Technology: Computer vision, machine learning, data science, augmented reality, and recommender models.

Function: According to Nike, 60% of individuals wear shoes that don’t fit correctly, and over 500,000 people admit to purchasing the wrong size each year. Nike blames the problem on obsolete, two-dimensional shoe sizing procedures. In a May 2019 news release, the business defined shoe sizing as a “gross simplification of a complex problem”. Nike Fit scans clients’ feet with a smartphone camera to offer exact shoe size suggestions. This technology seeks to minimise return rates and increase customer satisfaction by tackling the problem of inaccurate shoe sizing. It captures 13 visual data points to create an accurate 3D model of the foot, which aids in selecting the optimum size for future purchases.

2. Personalized Customer Experience (2018-Present)

Technology: Data science, machine learning, AI algorithms.

Function: Nike gathered consumer data from its app ecosystem, enterprise data, and supply chain to enhance its global marketing strategies. However, extracting actionable insights was challenging. To address this, Nike implemented the Nike Direct strategy, part of its Consumer Direct Offense announced in 2017, focusing on direct-to-consumer sales.

Key opportunities included personalizing shopping experiences and optimizing inventories. To achieve these goals, Nike acquired four data science and analytics firms since 2018. John J. Donahoe, Nike’s President and CEO, highlighted the acquisition of Datalogue, which used machine learning to automate the translation of raw data into actionable insights in real-time at an enterprise scale.

Nike utilizes customer data from various sources to offer personalized product recommendations and customized shopping experiences. This includes leveraging data from its app ecosystem, enterprise data, and supply chain data to drive a more personalized experience for customers.

3. Generative AI for Product Design (2024) & Athlete Imagined Revolution (A.I.R.) Project (2024)

Technology: Proprietary generative AI model, large language models (LLMs), Generative AI, 3D printing, computational design.

Function: Nike’s chief innovation officer, John Hoke, revealed that the company had been developing its own generative AI model to design products using exclusive athlete data. Speaking at a recent Nike event in Paris, Hoke explained that the bespoke large language model (LLM) leverages performance data from athletes, combining it with public data to create a “private garden” of information for training the model.

This AI initiative is part of Nike’s broader strategy to revolutionize product design and manufacturing. Hoke described the integration of AI with other technologies like virtual reality (VR) and 3D printing as a “new alchemy” that drastically expedites the prototyping process, allowing Nike to bring athletes’ visions to life much faster. “What usually takes weeks or months now takes hours,” Hoke said, highlighting the enhanced engagement with athletes through rapid prototyping.

Despite the generative AI’s known issues with errors, or hallucinations, Hoke remained optimistic, calling AI “rocket fuel for creativity.” He emphasized that AI would not replace human creativity but rather amplify it, stating, “It’s an amazing tool guided by a human’s imagination.” Hoke saw these potential errors as opportunities to broaden creative perspectives, further pushing the boundaries of innovation.

In addition to the AI model, Nike showcased the Athlete Imagined Revolution (AIR) project, where generative AI was used to create prototype shoes for top athletes like Sha’Carri Richardson and Kylian Mbappé. These prototypes were based on the athletes’ preferences, input into AI models to generate hundreds of designs, which were then refined using digital fabrication techniques.

While Hoke acknowledged that current regulations might prevent these hyper-personalized shoes from being used in competitions, he hinted at their potential competitive benefits, both physically and psychologically. “It’s an extension of who they are,” he said, suggesting that customized products could provide athletes with a unique edge.

4. AI-Optimized Air Technology (2024)

Technology: AI, computational design.

Function: Nike ramped up product innovation last summer with support from its tech-powered Sports Research Lab. The company tapped into athlete data to inform product development and optimized designs using computational technology. One of their latest innovations was incorporating the Nike Air Zoom unit into the new Pegasus Premium running shoe.

The Research Lab, in collaboration with Nike Air Manufacturing Innovation, utilized digital capabilities to predict product reactions to physical forces. Through projects like the Athlete Imagined Revolution (AIR), where athletes co-created with Nike designers, the company developed a sole that contours to a runner’s foot, enhancing springiness and energy with ZoomX and ReactX foam cushioning.

John Hoke, emphasized their focus on technologies like AI and rapid prototyping through initiatives such as AIR. He highlighted the introduction of new Nike Air Zoom shapes and sensations across various sports, building on insights gained from super-shoe technology.

Nike also utilized its lab to design national federation kits for association football, leveraging 4D motion-capture data and advanced body-mapping technology to personalize kits with pixel-level precision.

5. Nike By You Platform

Technology: AI applications in retail.

Function: Nike’s New York flagship store leverages customer data extensively, gathering insights such as color preferences, favorite sports, and shoe sizes from app users to offer highly personalized experiences. This data guides decisions on stocking sneakers across retail stores and informs broader company strategies.

Customers can scan items in-store and request specific clothing sizes to be sent to fitting rooms, enhancing convenience and satisfaction. Nike’s AI initiatives, highlighted in IBM’s 2019 publication, focus on custom-designed sneakers through the “Nike Maker Experience.” This system enables customers to design and receive their personalized sneakers within hours, using voice commands to select colors and graphics.

Nike’s “Nike By You” lifestyle program extends customization to all aspects of product design, offering customers virtually limitless options for personalization. The brand collaborates with various ambassadors to promote this feature, which integrates seamlessly with social media sharing capabilities, enhancing user satisfaction and engagement.

Through initiatives like the “Nike+” program, Nike continues to prioritize customer-centric experiences, offering exclusive previews of new releases and personalized equipment recommendations facilitated by AI-based individualization.

6. AI-Driven Advertising Campaigns (2024)

Technology: Generative AI, large language models (LLMs).

Function: Nike Inc., launched an advertising campaign in South Korea using Naver Corp.’s generative artificial intelligence (AI) ad platform, HyperCLOVA X. This platform, part of Naver’s CLOVA for AD service, employed a large language model (LLM) to enhance customer engagement.

When users searched for Nike running shoes on Naver’s website, they encountered a chatbot-style search service powered by generative AI. This AI recommended specific Nike models, provided detailed product explanations, and offered links for purchasing. It acted akin to a brand manager in an offline store, handling all stages of the sales process, from product recommendations to purchase assistance.

According to Naver, interactions with the AI significantly increased user engagement and purchase intent. During pilot operations, the ad on CLOVA for AD achieved a 20% higher click-through rate compared to traditional banner ads. Additionally, three out of ten users who engaged with the AI chats visited Nike’s website to explore the products further.

AI Investments and Acquisitions

Nike has made several strategic acquisitions to bolster its AI capabilities:

Zodiac: Acquired in March 2018, Zodiac is a consumer data analytics company that helps Nike understand customer behavior and preferences. Nike purchased Zodiac Inc., a consumer data analytics firm, to improve its digital transformation and emphasis on customer lifetime value. Zodiac, built with predictive analytics tools from Wharton School Professor Peter Fader, was created to supplement Nike’s Consumer Direct Offence approach. This method attempted to speed up product creation by personalising at scale and strengthening the direct-to-consumer contact. Zodiac’s capabilities were supposed to strengthen consumer interactions, particularly among NikePlus members. Nike CFO Andrew Campion had noted that the company will invest in its data analytics team as well as add talent via acquisitions. “We already have made significant investments in building our NIKE membership team and data and analytics capabilities and are fortunate to have some great talent that’s joined our company over the past several years, and bringing on teams like that at Zodiac and some other teams that we’ve been in discussion with are additive,” said Campion.

Invertex: Acquired in April 2018, this Israeli company specializes in computer vision and 3D scanning technology, which has been integrated into the Nike Fit tool. This acquisition played a crucial role in the development of Nike Fit, a revolutionary tool launched in 2019. Nike Fit uses computer vision, machine learning, data science, and augmented reality to scan customers’ feet using a smartphone camera, providing precise shoe size recommendations. By collecting 13 visual data points to build an accurate 3D model of the foot, Nike Fit has significantly improved customer satisfaction and reduced return rates due to incorrect sizing.

Celect: Acquired in August 2019, Celect is a Boston-based predictive analytics company that helps Nike anticipate consumer demand and optimize inventory management. This acquisition enhanced Nike’s ability to anticipate consumer demand and optimize inventory management. Celect’s technology has been integrated into Nike’s mobile apps and website, enabling better forecasting of consumer buying patterns for specific styles. This has led to reduced out-of-stock rates and improved inventory management across various sales channels, supporting Nike’s direct-to-consumer strategy.

Datalogue: Acquired in February 2021, Datalogue focuses on digital sales and machine learning technology to translate raw data into actionable insights in real-time. Nike further expanded its AI capabilities with the acquisition of Datalogue, a New York-based data integration platform. Datalogue’s proprietary machine learning technology automates data preparation and integration, allowing Nike to seamlessly combine data from various sources, including its app ecosystem, supply chain, and enterprise data. This acquisition has enhanced Nike’s ability to transform raw data into actionable insights in real-time, supporting its Consumer Direct Acceleration (CDA) strategy.

Key Leaders in Nike’s AI Initiatives

Several key leaders have been instrumental in driving Nike’s AI and Gen AI initiatives:

Jason Loveland: Serving as the VP of Enterprise AI/ML Engineering, Loveland has led the development and productionalization of AI and machine learning models at Nike. His efforts have focused on demand sensing, optimization, personalization, and athlete performance engines.

Bikram Barman: As the India Site Leader and Senior Director at Nike India Technology Center, Barman leads the vision and strategy for Nike’s digital transformation and AI initiatives in India.

Taj Pirzada: An executive leader at Nike, Pirzada has been involved in expanding Nike’s AI footprint and delivering Gen AI solutions.


Nike’s strategic use of AI and Gen AI has dramatically improved the consumer experience, optimised the supply chain, and fueled product innovation. Nike maintains its competitive advantage in the global sportswear industry through smart acquisitions and cooperation with cutting-edge technologies. Key executives such as John Donahoe, Jason Loveland, John Hoke, Bikram Barman, and Taj Pirzada have played critical roles in propelling these efforts ahead, assuring Nike’s continued leadership in technology innovation.

Picture of Anshika Mathews
Anshika Mathews
Anshika is an Associate Research Analyst working for the AIM Leaders Council. She holds a keen interest in technology and related policy-making and its impact on society. She can be reached at
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