AI Usage at a Glance
Mar 26, 2018
Data AnalysisPractice documented: Nike uses a predictive analytics platform, built on technology from its 2018 acquisition of Zodiac, to forecast the future spending behavior and long-term value of individual customers. This system helps Nike decide which customers to target, when, and with what offers.
Practice DocumentedView practice →Mar 26, 2018
RecommendationPractice documented: Nike uses an AI-powered product recommendation system across its website and apps that analyzes each shopper's browsing history, past purchases, fitness activity, and personal preferences to suggest relevant products in real time. This system operates continuously for users of Nike's digital platforms.
Practice DocumentedView practice →May 9, 2019
RecommendationPractice documented: Nike deployed Nike Fit, an AI-powered foot-scanning feature built into the Nike App, starting in July 2019. The tool uses a smartphone camera to scan a customer's feet and recommends the best-fitting shoe size for each Nike model.
Practice DocumentedView practice →May 14, 2019
Data AnalysisNew evidence: How Nike is boosting its direct-to-consumer business with tech acquisitions
Evidence AddedView practice →May 14, 2019
RecommendationNew evidence: How Nike is boosting its direct-to-consumer business with tech acquisitions
Evidence AddedView practice →Dec 9, 2019
Data AnalysisPractice documented: Nike uses an AI-powered demand forecasting system, built on technology from its 2019 acquisition of Celect, to predict which products shoppers will want, in which locations, and at what times. This system helps Nike decide where to position inventory across its global network to reduce waste and improve fulfillment speed.
Practice DocumentedView practice →Sep 23, 2020
Data AnalysisNew evidence: Predictive analytics determine what's in stock at Nike's new Los Angeles warehouse
Evidence AddedView practice →May 7, 2024
Creative GenPractice documented: Nike used generative AI tools in its Athlete Imagined Revolution (A.I.R.) project to create hundreds of concept shoe designs for 13 elite athletes, which were showcased at an exhibition in Paris in April 2024 and again at the 2024 Olympics. Human designers then refined the AI-generated visuals into final prototypes.
Practice DocumentedView practice →Jul 23, 2024
Creative GenNew evidence: Creating the Unreal: How Nike Made Its Wildest Air Footwear Yet
Evidence AddedView practice →Jul 25, 2024
Creative GenNew evidence: Generative AI helps Nike create custom dream shoes for athletes
Evidence AddedView practice →Jul 18, 2025
Data AnalysisPractice documented: Nike uses AI-powered sentiment analysis to process customer feedback from social media, review platforms, and direct communication channels, using the results to inform product development and personalized customer service strategies.
Practice DocumentedView practice →Jul 25, 2025
Customer SvcPractice documented: Nike deployed NikeAI Beta, a conversational AI shopping assistant, to all iOS Nike App users in the United States in August 2025. The tool lets shoppers describe what they need in plain language — like 'running shoes for a race' or 'gear in my favorite color' — and it returns personalized product matches.
Practice DocumentedView practice →Aug 4, 2025
Customer SvcNew evidence: NikeAI's beta release comes with lessons for CTOs
Evidence AddedView practice →Aug 4, 2025
ProductivityPractice documented: Nike deployed two internal AI tools — AgentAutosys and Genius Results — to automate repetitive IT support tasks for its technology operations teams. These tools handle thousands of routine tasks, from detecting and resolving system failures to answering employee IT questions, without human intervention.
Practice DocumentedView practice →Aug 18, 2025
RecommendationNew evidence: Nike's Martech in 2025: Unified CDP, AI-Driven Personalisation & Composable Stack Strategy
Evidence AddedView practice →Oct 9, 2025
RecommendationNew evidence: Ecommerce Trends: How Nike is using AI
Evidence AddedView practice →Oct 9, 2025
Customer SvcNew evidence: Ecommerce Trends: How Nike is using AI
Evidence AddedView practice →Nike uses an AI-powered product recommendation system across its website and apps that analyzes each shopper's browsing history, past purchases, fitness activity, and personal preferences to suggest relevant products in real time. This system operates continuously for users of Nike's digital platforms.
Nike's recommendation engine draws on data collected across its app ecosystem — including the Nike App, SNKRS, Nike Training Club, and Nike Run Club — and uses machine learning to build individual customer profiles. These profiles incorporate browsing patterns, purchase history, size data from Nike Fit, and fitness behavior. The AI input is aggregated customer behavioral and transactional data; the output is a personalized feed of product suggestions and marketing messages. Nike's VP of Marketing Data confirmed the company built 'a unified customer data platform with underlying ML/AI models that power everything from our journey orchestration to how we personalise digital experiences.' The Zodiac acquisition (2018) added customer lifetime value prediction to inform which customers to target and when.
Nike uses a predictive analytics platform, built on technology from its 2018 acquisition of Zodiac, to forecast the future spending behavior and long-term value of individual customers. This system helps Nike decide which customers to target, when, and with what offers.
Nike uses AI-powered sentiment analysis to process customer feedback from social media, review platforms, and direct communication channels, using the results to inform product development and personalized customer service strategies.
Nike uses an AI-powered demand forecasting system, built on technology from its 2019 acquisition of Celect, to predict which products shoppers will want, in which locations, and at what times. This system helps Nike decide where to position inventory across its global network to reduce waste and improve fulfillment speed.
Nike uses an AI-powered product recommendation system across its website and apps that analyzes each shopper's browsing history, past purchases, fitness activity, and personal preferences to suggest relevant products in real time. This system operates continuously for users of Nike's digital platforms.
Nike deployed Nike Fit, an AI-powered foot-scanning feature built into the Nike App, starting in July 2019. The tool uses a smartphone camera to scan a customer's feet and recommends the best-fitting shoe size for each Nike model.
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Nike used generative AI tools in its Athlete Imagined Revolution (A.I.R.) project to create hundreds of concept shoe designs for 13 elite athletes, which were showcased at an exhibition in Paris in April 2024 and again at the 2024 Olympics. Human designers then refined the AI-generated visuals into final prototypes.
Nike design teams interviewed athletes about their preferences and personalities, then submitted detailed prompts to generative AI models to produce hundreds of visual concepts. These AI images served as 'inspiration points'; human designers then narrowed them down and used 3D printing and computational design to build the final prototypes. The AI input was text prompts based on athlete feedback; the output was visual concept images. Nike has not publicly disclosed which specific generative AI tools were used. The project was described by Nike's own newsroom as a 'co-creation process' and is not a commercially available product for consumers.
Nike deployed NikeAI Beta, a conversational AI shopping assistant, to all iOS Nike App users in the United States in August 2025. The tool lets shoppers describe what they need in plain language — like 'running shoes for a race' or 'gear in my favorite color' — and it returns personalized product matches.
NikeAI Beta uses large language models fine-tuned on Nike's product catalog and consumer data to interpret shopping intent expressed in natural language, then recommends matching products. Nike CTO Dr. Muge Erdirik Dogan described it as built using 'best-in-class foundational models' and fine-tuned by domain experts. The tool takes conversational user queries as input and produces product recommendations as output. It was confirmed as a beta product, meaning its long-term general availability status is not yet determined.
