Artificial intelligence innovation: Leading innovators in autonomous delivery management for the retail industry

The retail industry continues to be a hotbed of innovation, with activity driven by a number of factors including supply chain optimisation, risk mitigation, and the growing importance of technologies such as autonomous delivery management and robotic inventory management. In the last three years alone, there have been over 133,000 patents filed and granted in the retail industry, according to GlobalData’s report on Artificial Intelligence: Autonomous delivery management

However, not all innovations are equal and nor do they follow a constant upward trend. Instead, their evolution takes the form of an S-shaped curve that reflects their typical lifecycle from early emergence to accelerating adoption, before finally stabilising and reaching maturity.

Identifying where a particular innovation is on this journey, especially those that are in the emerging and accelerating stages, is essential for understanding their current level of adoption and the likely future trajectory and impact they will have.

70+ innovations will shape the retail industry

According to GlobalData’s Technology Foresights, which plots the S-curve for the retail industry using innovation intensity models built on over 128,000 patents, there are 70+ innovation areas that will shape the future of the industry.

Within the emerging innovation stage, autonomous vending, skin care recommender systems, and AI-assisted surveillance are disruptive technologies that are in the early stages of application and should be tracked closely. Autonomous delivery vehicle navigation system, autonomous delivery management, and planogram modelling are some of the accelerating innovation areas, where adoption has been steadily increasing. Among maturing innovation areas are virtual contact centre automation and checkout object identification, which are now well established in the industry.

Innovation S-curve for artificial intelligence in the retail industry