PUBLISHER: Stratistics Market Research Consulting | PRODUCT CODE: 1848430
PUBLISHER: Stratistics Market Research Consulting | PRODUCT CODE: 1848430
According to Stratistics MRC, the Global AI-Powered Local Commerce Market is accounted for $11.9 billion in 2025 and is expected to reach $51.2 billion by 2032 growing at a CAGR of 24.3% during the forecast period. AI-Powered Local Commerce refers to the use of artificial intelligence by local businesses to personalize customer experiences and optimize operations. This includes AI that analyzes purchase history to offer personalized promotions, dynamic pricing for services like ride-sharing, and inventory management systems that predict local demand. It enhances the relevance of marketing, improves delivery logistics, and helps brick-and-mortar stores compete with online giants by creating a more efficient, data-driven, and customer-centric local shopping ecosystem.
According to the MIT Technology Review, AI-driven platforms are transforming local commerce by personalizing recommendations, automating inventory, and enabling hyper-targeted promotions for small businesses and neighborhood retailers.
Growth of hyperlocal retail platforms
The AI-Powered Local Commerce Market is driven by the rapid expansion of hyperlocal retail platforms that connect nearby retailers with consumers efficiently. Rising demand for quick, convenient, and personalized shopping experiences is propelling AI adoption. Retailers are increasingly using machine learning for demand prediction, inventory optimization, and targeted promotions. Additionally, urbanization and smartphone penetration have accelerated digital transactions, encouraging AI integration. Collectively, these factors are fueling the deployment of AI solutions to enhance local commerce operations worldwide.
Limited AI adoption by small retailers
The market faces restraints due to low AI adoption among small and traditional retailers. Limited technological expertise, lack of awareness, and budget constraints prevent smaller players from leveraging AI tools effectively. Many retailers continue relying on manual inventory management, customer engagement, and marketing strategies. Additionally, the upfront costs of AI-enabled platforms, along with concerns about data privacy, further restrict adoption. These limitations reduce the overall penetration of AI solutions in hyperlocal commerce ecosystems, especially in emerging regions.
Integration with delivery and logistics platforms
Integrating AI-powered local commerce solutions with delivery and logistics platforms presents a major growth opportunity. Real-time route optimization, predictive demand planning, and automated order fulfillment enhance operational efficiency. Collaboration with third-party delivery providers and cloud-based logistics systems improves customer satisfaction and scalability. Additionally, AI-driven analytics enable personalized promotions, reducing inventory waste and enhancing profitability. These integrations allow local retailers to compete with larger e-commerce players and expand reach while maintaining cost-effective and efficient delivery operations.
Competition from global e-commerce giants
The market faces significant threats from large global e-commerce platforms that leverage advanced AI and big data analytics. These companies benefit from extensive infrastructure, brand recognition, and economies of scale. Their ability to offer faster delivery, dynamic pricing, and personalized recommendations challenges smaller local commerce platforms. Furthermore, the dominance of multinational players can reduce market share and limit opportunities for independent AI-powered solutions, creating a highly competitive environment that necessitates continuous innovation for smaller regional players.
The COVID-19 pandemic accelerated the adoption of AI-powered local commerce platforms as consumers increasingly preferred contactless, online shopping. Hyperlocal delivery networks and digital marketplaces became critical for essential goods, groceries, and retail items. Retailers rapidly adopted AI for demand forecasting, inventory management, and customer engagement to meet surging demand. Post-pandemic, consumer habits favor convenience and personalization, sustaining AI adoption in local commerce. Consequently, COVID-19 acted as a catalyst, permanently transforming retail operations and AI integration strategies globally.
The solutions segment is expected to be the largest during the forecast period
The solutions segment is expected to account for the largest market share during the forecast period, owing to the increasing demand for AI-driven tools for inventory management, demand prediction, and personalized customer engagement. Retailers seek comprehensive software solutions that integrate analytics, recommendation engines, and operational management. This segment offers scalability, adaptability, and continuous updates, enabling businesses to optimize performance and respond to dynamic market trends efficiently, solidifying its dominance in the AI-powered local commerce ecosystem.
The cloud-based segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the cloud-based segment is predicted to witness the highest growth rate, reinforced by its flexibility, scalability, and cost-efficiency. Cloud platforms allow retailers to deploy AI applications without heavy infrastructure investment, supporting real-time data processing and analytics. Integration with mobile apps and logistics networks enhances operational efficiency and customer experience. The ease of remote access and continuous software upgrades further drives adoption, making cloud-based solutions a preferred choice for AI-powered local commerce platforms globally.
During the forecast period, the Asia Pacific region is expected to hold the largest market share, ascribed to rapid e-commerce growth, widespread smartphone adoption, and a dense urban population. Countries like China, India, and Southeast Asian nations are witnessing a surge in hyperlocal retail platforms. Investments in digital infrastructure, rising consumer preference for fast delivery, and regional startup ecosystems contribute to the dominance of AI-powered local commerce solutions in the region.
Over the forecast period, the North America region is anticipated to exhibit the highest CAGR associated with strong technological adoption, advanced retail infrastructure, and high consumer expectations for personalized shopping experiences. Retailers are leveraging AI for predictive analytics, dynamic pricing, and logistics optimization. The presence of major technology providers and AI startups fosters innovation, while supportive regulatory frameworks encourage platform growth. This combination positions North America as a rapidly expanding hub for AI-powered local commerce solutions.
Key players in the market
Some of the key players in AI-Powered Local Commerce Market include Marico Limited, Adani Wilmar Limited, Wilmar International Ltd, Olam International Limited, Archer Daniels Midland Company (ADM), Bunge Limited, Cargill, Incorporated, The Hain Celestial Group, Inc., Coconuts India Pvt. Ltd., NOW Foods, Nutiva, Inc., La Tourangelle, Inc., Borges International Group, Nutraj (VKC Nuts Pvt. Ltd.) and Dabur India Ltd.
In August 2025, Marico reaffirmed its growth ambitions: it expects double-digit domestic growth in upcoming quarters, driven by core brands and expansion of new business lines.
In April 2025, Dabur India Ltd. announced it is weaving AI across operations: using conversational bots for consumer engagement, improving supply chain efficiency via AI forecasting, and leveraging AI to decode its Ayurvedic knowledge base to assist new product formulation.
In Feb 2025, Marico Ltd. unveiled the LoSorb Technology and other innovations at World Food India 2025, showcasing new R&D capabilities (hybrid extrusion, DOC valorisation) to push healthier and differentiated food portfolio offerings.
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.