Why Generative AI is a financial asset Generative Artificial Intelligence (GenAI) is revolutionizing industries, even at this moment. According to McKinsey, the impact of GenAI in the global economy could add up to US$4.4 trillion. We are looking at up to US$660 million of additional positive impact in the Retail and CPG sector alone. At the 2023 Gartner Symposium, surveys showed that 74% of CIOs plan to increase their AI/ML investment. But how can they make it a success and avoid common pitfalls? In this blog, we will delve into the transformative power of GenAI in RTL and best practices to succeed in these industries. Unlocking opportunities with GenAI Which domains of applications should be targeted in RTL? Experience shows that the simplest approach is usually to start with productivity improvement, followed by customer engagement, and then disruptive innovation. Let’s take a look: 1. Operational efficiency In retail, AI-powered automated checkouts can eliminate cashiers and long queues, while personalized shopping assistants enhance customer service. Eviden’s own experience is backed here by McKinsey figures, which state that retailers using AI have seen forecasting errors fall by up to 50%. Similarly, in logistics, automated warehousing and smart hubs can ensure precise parcel tracking. 2. Enhanced customer engagement With AI, retailers can create immersive and interactive shopping experiences. Virtual fitting rooms allow online shoppers to try out items. In transport and logistics, AI enables better customer engagement via real-time tracking and personalized delivery options. 3. Innovation and new business models Innovation never stops! GenAI can help develop new products, services and business models more rapidly. Thanks to this; product customization, on-demand and flexible delivery services have become the new norm. Predicting the impact of Generative AI in RTL In these three domains, what are the typical use cases? Based on our experience on multiple GenAI projects for RTL players, we’ve found five key areas with substantial and rapid ROI potential: Retail Highly targeted marketing and advertising With GenAI, marketing can be ultimately tuned to reach each market in a highly personalized and engaging way. Specific content can be automatically created for each persona and even individual customer, driving much higher conversion rates. Personalized shopping experiences Thanks to GenAI-powered hyper-personalization and predictive analytics, an end customer can be offered a highly customized experience based on preferences and behaviors, thereby enhancing satisfaction, loyalty and engagement by 30% or more. Gartner predicts that up to 80% of retail interaction will be steered with AI by 2026. Inventory management With intelligent inventory systems, it becomes possible to predict demand much more accurately, by taking into account all variables, including the state of the economy, seasonal changes and even social media trends. This typically leads to increased efficiency, and stock reduction by up to 20%. Transport and Logistics Supply chain optimization By analyzing data from various sources in real time, GenAI can optimize supply chain operations by streamlining demand forecasting, supplier selection and risk management. For example, digital transformation partner Eviden helped several retailers achieve more than 80% time savings with AI-based product recognition for shelf monitoring and replenishment. Route optimization and fleet management With dynamic route optimization and fleet-management adjustments based on traffic and weather, GenAI enhances planning and reduces fuel consumption, improving delivery times and enabling precise tracking. The ROI is significant. Through Eviden’s Smart Predictive Maintenance solution, the mean time to repair for fleets has also seen an improvement of 20%. Deloitte surveys confirm this ROI potential. Navigating challenges: Pitfalls to avoid With real-world applications and opportunities, are there any pitfalls to avoid? Here are three prime factors for your consideration: Data privacy and security: A robust data governance framework is essential for privacy and security concerns. Bias and fairness: When improperly managed, GenAI can inadvertently perpetuate biases present in their source data. Regulatory compliance: Navigating regulations such as PCI DSS, GDPR, HIPAA (in the case of retail pharmacy) and NIS2 takes careful planning and execution. Avoiding these pitfalls is only possible through a comprehensive understanding of the security and regulatory landscape for GenAI, notably the recent EU AI Act, in addition to data protection and industry-specific regulations, such as environmental rules or consumer protection laws. Leveraging GenAI requires a combination of strategic vision, technology solutions expertise and ethical AI practices. Regardless of where RTL organizations are in their GenAI journey, identifying impactful use cases and designing scalable AI architectures — all along the cloud continuum, from the hybrid cloud to the edge and IoT — are key. Empowering RTL industry companies in the age of GenAI: Eviden at work Now, how can you combine all these requirements into projects? A prime example is the Heureka Group, Europe’s largest price comparison website and online shopping advisor with over 23 million visitors per month. DataSentics, an Eviden business, collaborated with Heureka to develop an AI shopping assistant built on a state-of-the-art generative AI model. Its objective was to understand customer needs, leverage blog posts and descriptions to create intuitive questions, and translate complex numerical data into easily understandable customer-centric answers. From the start, the project leveraged all the best practices — putting the user at the heart, operating in a scalable way for millions of connections and preventing any possible regulatory flaw. The results were amazing, automatically generating sales-effective content for various product categories, ensuring that recommendations were up-to-date and relevant. This translated into a user rating of 4.2 out of 5 points, a 4% increase in revenue and a user engagement of around 26% per session. A terrific demonstration of how fast ROI can be demonstrated in AI and GenAI projects! The cornerstones of Generative AI So how can we leverage GenAI in RTL? There are three strategic golden rules to abide by: Leverage data-driven insights: Utilize AI to analyze customer behavior and operational data for an optimized CX, inventory management, route efficiency and demand forecasting. Enhance collaboration: Foster collaborative environments where AI tools augment human decision-making and handle routine tasks, enhancing productivity and service quality rather than replacing human tasks. Ensure ethical and transparent AI use: Implement AI systems that adhere to ethical guidelines, ensuring transparency, unbiased decision-making and compliance with regulations. In essence, companies must accelerate GenAI integration to compete with agile counterparts; analyzing the value chain, defining KPIs and investing in infrastructure. Priority AI initiatives include enhancing customer experiences, optimizing operations, security and compliance, and personalizing client interactions. Explore Eviden’s GenAI acceleration program here. Look into the AI crystal ball. Find out more in the latest Eviden white paper: The revolution of autonomous AI agents in business.