The global AI in the retail market is expected to grow at a CAGR of 34.4% from 2020 to reach US$19.9 billion by 2027. Increasing necessity for superior surveillance and monitoring at a physical store, growing awareness and application of AI in the retail industry, enhanced user experience, improved productivity, return on investment (RoI), mainlining inventory accuracy, and supply chain optimization are some of the key factors fuelling the growth of this market.
With the advent of artificial intelligence in retail market ecosystems, advanced data and predictive analytics systems have been introduced to assist companies in making business decisions using databases. Most retailers have realized the potential to leverage new technologies like the internet of things (IoT) and artificial intelligence (AI) to innovate retail operations and customer experiences.
The adoption of artificial intelligence in retail is transforming the industry in a new era of how companies track their operations to augment business strategies, provide better outcomes, and engage with customers in the digital world. The growth in the global artificial intelligence in the retail market is driven by factors such as perpetually growing internet users and smart devices, increasing awareness about AI and big data and analytics, and government initiatives towards digitization. Furthermore, the adoption of the multichannel or omnichannel retailing strategy, untapped opportunities to increase sales efficiency, enterprises' demands for streamlining the business process, and growing need to enhance end-user experience and to take advantage of market dynamics are also contributing to the growth of the global artificial intelligence in the retail market.
Artificial intelligence is driving faster business decisions in marketing, e-commerce, product management, and other business areas by decreasing the gap from insights to action. Machine learning and deep learning technologies are majorly used technologies for AI. Organizations in the retail industry use machine learning and deep learning technology to offer a more personalized experience to the end-users and provide an interactive environment for them.
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