Business benefits of artificial intelligence in retail

The retail industry is going through a period of major upheaval. AI is transforming the landscape at a rapid pace. Grand View Research evaluated the market value at USD 5.79 billion in 2021 and this is expected to grow at a 23.9% compound annual growth rate (CAGR) from 2022 to 2030. For retailers, this translates into a need to adapt to an entirely new paradigm of customer expectations. 

As customers continue to become more discerning and margins shrink, to remain profitable, retailers are looking towards accelerated digital transformation and new technologies that can improve efficiency and enable differentiation. Innovation is taking many forms, such as virtual dressing rooms, IoT adoption, improved support for mobile e-commerce and, perhaps most crucially of all, artificial intelligence.

AI/ML has numerous applications in the retail industry, from driving personalised customer experiences, to forecasting or inventory tracking – which is particularly valuable for BOPIS strategies and other cross-channel buyer journeys. AI is enabling new levels of operations optimisation through the reduction and automation of repetitive tasks, and unprecedented insight into problems like machine malfunctions. 

Benefits of AI/ML in retail: which use cases are driving value?

As early as 2018, Infosys reported that 87% of retailers surveyed were using some form of AI or automation technology to guide human decision-making. Even at this early stage, the value of AI was already apparent, with 49% of companies surveyed reporting cost savings as a result of AI adoption, 44% reporting improved productivity, and 43% reporting increased revenue. (source). 

The sections that follow cover the most popular and value-driving use cases. 

Personalised customer experience

In the past, retailers have used business intelligence solutions such as Qlik and Tableau to make educated assumptions based on a broad, macro view of their data. Now, AI/ML enables businesses to examine their data with a laser pointer, leading to a far more nuanced understanding of trends, demographics and buying patterns. And while traditional business intelligence tools were limited to simple data types, AI/ML can take advantage of today’s growing data lakes that include images, video and text. 

This insight can be achieved at scale without compromising productivity or time-to-market, and it can deliver insights right down to the level of individual shoppers. This approach empowers retailers to create bespoke shopping experiences tailored to the needs of each consumer – personalised e-commerce product recommendations being the classic example – leading to greater customer satisfaction and spending.

Improved forecasting

With a more detailed understanding of historic data also comes the ability to make accurate predictions on future activity. AI/ML models can take into account a wide array of data points – such as weather, public holidays, seasonal trends and many more – to produce relatively precise predictions that can help retailers optimise decision-making and spending. Fashion trends, customer demand, foot traffic and even equipment health can all be forecast with AI/ML. 

For example, businesses that use freezers to store produce can feed power utilisation data into an AI/ML model. By looking at the power fluctuations, the model can predict when a freezer is about to fail, enabling preventative maintenance that drives significant savings through reduced spoilage.

The future of AI/ML in retail

Looking ahead to 2023 and beyond, trends in the retail industry indicate that artificial intelligence and machine learning are set to become even more crucial within the retail industry. The push towards omnichannel is continuing unabated, which in turn is opening up new data sources. And as data volumes grow, the potential for AI/ML projects to provide a competitive advantage increases as well. 

AI/ML already offers immense benefits to retailers, and the data that businesses are working with today represents just a fraction of the quality and depth that will be available 18-24 months from now. The sooner organisations invest in AI/ ML, the sooner they will reap the benefits, and the better positioned they will be to make the most of their data moving forwards.

Further reading

• AI on Ubuntu
• [Blog] AI/ML in retail: how shopping experience has changed
• [Webinar] Artificial intelligence in retail: new use cases for brick & mortar 
• [Whitepaper] A guide to MLOps
• [Solution brief] Enterprise AI at scale with NVIDIA and Canonical
• [Blog] From data-centric to model-centric MLOps
• Ubuntu AI on Medium