nd external factors (like weather and events) retailers can predict future demand much more accurately. Machine learning models continuously improve predictions by identifying patterns and trends, helping retailers optimize inventory and stock replenishment, reducing overstock and stockouts. Demand forecasting also helps meet customer needs more effectively, driving retention and loyalty. If popular products are consistently in stock, retailers can provide a much more reliable and positive shopping experience and avoid losing customers to competitors. In one successful example, a major consumer goods company used AI to predict future store and online sales. By consolidating historic sales and web traffic data into a single data lake, they built models to forecast sales. This approach increased the accuracy of sales predictions by 47 per cent, demonstrating AI’s capability to streamline backend processes and improve decision-making. 2. Automation Another way in which AI is improving backend processes is through automation. Automating repetitive and mundane tasks, such as data entry, invoice processing and customer service queries, greatly relieves the admin burden on staff as well as being more accurate. This can reduce headcount or can be used to shift existing staff to focus on more interesting and high-value tasks where human input is most needed, such as customer support and relations. Goldman Sachs is one company using automation to improve efficiency. It has implemented complex algorithms with machine-learning capabilities to streamline its trading operations. By automating programs supported by computer engineers, the bank reduced its US cash equities trading desk from 600 traders to just two. 3. Real-time fraud and cyber detection Cybercrime is an issue for every industry. But in business-to-consumer (B2C) sectors such as retail, consumer trust and confidence are paramount – adding extra pressure. Retailers collect massive amounts of confidential customer data which they are under increasingly strict regulatory requirements to protect. A breach can be catastrophic: legally, financially and reputationally. Securing networks is also complicated due to the number of third parties involved in the supply chain, from manufacturers and suppliers to logistics providers and payment processes. These represent multiple points of vulnerabilities. Using AI, retailers can more easily and quickly detect unusual transaction patterns that may indicate fraud and put a swift stop to it. AI can also monitor complex networks much more holistically and in real-time, isolating any infected parts of the chain to protect against data theft and loss. 4. Supporting and training staff AI chatbots can also accelerate staff onboarding, particularly valuable in a higher staff turnover industry like retail with its seasonal hiring patterns. Adidas uses a generative AI (GenAI) solution that enables its community of engineers to find information and answers from our knowledge base through a single conversational interface, covering everything from getting started to highly technical questions. Similarly, Walmart uses a chatbot for employees called “Ask Sam” which answered 1.3 billion questions in 2022, from helping staff answer customer queries to getting information on their own schedules or company policies. It is also harnessing the power of GenAI to better understand customers and help them find their desired products quickly and make confident purchase decisions. While large retail brands such as Walmart, Amazon and Ebay have typically been the earliest pioneers of AI, the technology is now accessible to retailers of all sizes. AI and GenAI can help drive down costs and improve efficiencies in a range of areas. It’s the reason that data strategy, a prerequisite for AI and generative AI implementation, will become the greatest differentiator for retail players in the next decade.