The future of shopping is AI

In both retail arenas — the physical and the virtual — AI-driven tools are improving the customer experience and back-office functions through a growing number of applications. Yet, many organizations haven’t tapped into its full potential.

The retail landscape has been undergoing a dramatic shift in recent years. Consumer goods, food services and even packaging companies have faced rising inflation rates as well as production, manufacturing and distribution costs. What’s more, eCommerce has disrupted the sector completely. In the US alone, online shopping surged past in-store purchases in 2023, with online spending accounting for 63% of the total compared to 37% for in-store shopping.¹ Now, slow adopters of online shopping will often go out of business.²

The retail and consumer goods industry is fiercely competitive. Consumer trends shift like sand, where they crave personalization, convenience and efficiency. While customers expect these things seamlessly woven into their shopping experience, remaining profitable, let alone competitive, goes beyond just personalization. This is where digitalization comes in. It’s no longer an option, it’s a necessity for survival. By 2025, 80% of retail executives expect their companies will use intelligent automation technologies while 40% already use some form of it.³

However, retailers can’t just plug in AI and expect it to magically solve things. To fully benefit from these emerging technologies, an organization must implement it correctly. They need to be ‘AI ready.’  So, what does that really mean?

The rise of AI in retail

It’s clear that every area of the consumer value chain could benefit from AI technology. Certain tools are already improving the customer experience through a growing number of applications, transforming the way we shop.

For example, retailers can use AI to analyze a range of data points, such as a consumer’s past purchases, preferences and browsing history to curate a personalized shopping experience to better drive brand loyalty. Moreover, it can be used to support price optimization too, analyzing vast amounts of data on trends, customer behavior and competitor pricing to optimize prices and promotions.

AI’s impact on the consumer extends further, with the introduction of Conversational AI, which is becoming a big part of online experiences, customer services and after-sales support. This isn’t a simple chatbot or even a ChatGPT prompt either, it’s much more than that, as explored in our previous blog. Interacting with Conversational AI feels as natural as chatting with another colleague or customer. Imagine effortlessly completing orders, getting complex questions answered and streamlining operations, all through intuitive, conversational interactions. It helps provide a seamless and personalized interaction between every point of the consumer value chain.

Back-office operations

Behind the scenes, AI is working tirelessly to streamline operations and boost profitability for retailers too.

It’s helping to support effective inventory management as maintaining an accurate inventory can be a major challenge for retailers. AI helps optimize stock levels by anticipating customer demand with greater accuracy. And since the COVID-19 pandemic, coupled with rising geopolitical tensions, companies have been pressured to accelerate the digitization of supply chains.

Through Machine Learning (ML), AI can reduce costs, minimize supply peaks and troughs and ensure supply availability year-round. It does this by examining past sales data, current market conditions and emerging trends to generate accurate demand predictions for certain products. It can, therefore, help optimize the supply chain and improve planning processes, leading to improved efficiency and profitability.

However, for a sector that’s constantly evolving, retail businesses capitalizing on the technology available to them are few and far between. Over half of retail and consumer packaged goods executives plan to invest in AI for marketing and forecasting, with significant interest in customer service and supply chain applications.⁴ The key word here is ‘plan.’

Getting ready for the AI revolution

While planning for AI adoption is a positive step, simply having the intention isn’t enough. To truly unlock the transformative potential of AI, retailers need to be ‘AI-ready.’ This involves a proactive approach that addresses several internal pillars.

Firstly, investing in infrastructure that can support AI is crucial. Cloud storage offers a scalable and cost-effective solution for storing the massive datasets required for AI training and operation. Retailers, with their wealth of customer data across the value chain, have a unique advantage to better train AI models effectively. 

However, the technology can only deliver exceptional results if it’s trained on high-quality data, no matter how sophisticated your AI models are or how concrete your AI strategy may be. Data management is essential and should be considered in tandem with planning out an AI strategy. And having data that’s clean, accurate and well-organized is key.

Retailers, therefore, need to implement robust data management practices to ensure data accuracy and security. This includes establishing clear data collection protocols, ensuring data governance and having measures in place to protect sensitive customer information, all while adhering to the latest data privacy regulations.

However, being ‘AI ready’ often requires a cultural shift within the organization too. Employees need to be on board with the adoption of AI and understand how it can complement, not replace, their roles. This can be achieved through effective communication and training to ensure a smooth transition.

1. https://www.enterpriseappstoday.com/stats/online-vs-in-store-shopping-statistics.html#Introduction ↩︎
2. https://www.g-co.agency/insights/retail-industry-analysis ↩︎
3. https://www.analyticsinsight.net/intelligent-automation/80-of-companies-will-adopt-intelligent-automation-by-2025 ↩︎
4. https://www.retaildive.com/news/retailers-ai-marketing-financial-forecasting-survey/704720/ ↩︎