Is AI ready to deliver its promise in the Retail Sector? The answer is a resounding yes! Gen AI in the retail sector has gotten beyond product recommendations. It is now a popular technology that operates in various areas of business focusing on increasing profits.
According to research by Publicis Sapient, 87% of shoppers have used Gen AI and are excited about the shopping experience. Whereas 27% of shoppers are impressed with Gen AI’s ability to enhance real-time price comparisons.
Well, the retail industry has been undergoing a profound transformation majorly attributed to factors such as shifts in customer behavior, and technological advancements.
Complex operations, disparate applications, and inefficient processes were all part of the retail of yesteryear. Functional systems like warehouse management, inventory control systems, point of sale systems, etc. each functioned in their silos, rarely integrating, or communicating with each other. The retail sector was burdened with a range of inefficiencies such as compatibility issues, real-time analytics of demand and supply, lagging business insights, scalability to deal with data explosion, data security of customers, and compliances. However, a range of technologies like AI, Gen AI, ML, Computer Vision, and more have added to the paradigm shift experienced by the retail sector.
To know more about this paradigm shift, a look into the not-so-recent past, is vital to understand how the retail sector has navigated the competitive market and integrated the technological advancements. This blog details the evolution of the retail sector and the impact of Gen AI in transforming the retail industry.
In the pre-AI era, challenges, and inefficient processes from inventory management to forecasting demand and supply, were all dependent on time-consuming human analysis and intuition.
However, at the turn of the last century, automation created the first creative disruption in the retail sector. Automation along with the deeper reach of the internet brought in online shopping, the biggest retail automation innovation of the millennium. And since then, there has been no looking back.
Automation could help make processes streamlined and faster, and repetitive tasks could be handled more efficiently. But Intelligent Retail Automation, predictive analytics, and market forecasting with personalized experiences led to optimization of inventory, warehousing, product pricing, and better customization of products – resulting into happy and satisfied customers, demanding more!
Now, the customer end of things could be connected to the inventory end and retailers saw their business prospects zooming ahead.
Soon AI and its technologies will leverage the processes and applications used by retailers to be dizzying heights and to the delight of demanding customers. Added to this, the Covid-19 pandemic added fireworks to the already unsettled retail sector. A rapid breakdown of its traditional and legacy systems from brick-and-mortar to an online experience, brought on by the convergence of easy accessibility to the net, accessibility to not just variety but the power to purchase online using digital payments and faster checkout at one’s own time – created a volatile chaotic but exciting playing field in the retail market.
About two decades ago, Amazon.com, one of the early adopters of AI, provided customers with basic product recommendations. It was amazing to see how connections were made, especially if a buyer bought say sports shoes, recommendations for socks were shown at the bottom of the website, or if the buyer purchased a book by an author, of a similar genre but different authors were also recommended.
AI in retail has progressed substantially since those early days providing better and enhanced product recommendations.
Artificial Intelligence has emerged as a game changer for retail. Retailers changed the way they interacted and served their customers. Keeping customers satisfied, engaged, and motivated is the primary motivator for most industries diving into AI for better conversion rates and sales.
According to the Future Market Insights report, AI in retail has been forecasted to witness a 28 percent CAGR between now and 2033 and is expected to surpass US$ 127.09 billion by 2033.
Generative AI (Gen AI) is being booted as one key reason for this major growth and a major disruptor across industries, including retail.
Gen AI opened a host of opportunities for enhancements in the retail sector. To know more details read this blog on relevant Gen AI use cases in retail. As we progress into the era of Gen AI, it goes unsaid that the convergence of AI and retail has become a necessity for staying competitive in the market.
The paradigm shift is transpiring in the retail sector, and Gen AI is the enabler!
Earlier, sales figures and profit margins were the key to measuring success in the retail sector. But today, the Customer Experience (CX) takes center stage. However, AI has impacted the retail industry not only at the customer level but also in its supply chain process. Gen AI not only focuses on delivering business insights in real-time but has helped retail processing capabilities to reach new heights. Gen AI powered solutions have helped reshape the way retailers interact with customers and manage operations.
This paradigm shift has brought in major benefits to every department in retail operations. Every department has undergone a change from leveraging Gen AI and thus staying competitive in the market. Listed below is a list of departments/operations with their challenges and by using Gen AI technologies how they have benefited in the process.
| Department/Process | Challenges | Use of AI systems | Outcome |
| Inventory & Supply Chain management | Overstocking or under-stocking of products | Analysis of historical sales data within minutes predicting demand patterns. Track and manage inventory automatically & route optimization | Improved operational efficiencyReady stock Minimizes transportation costs and delivery timesLess wastage |
| Receive goods and Inspection | Manual errors, Missed defective products | Computer vision helps to automate the inspection process | Improved AccuracyDecrease in defective goodsLess wastage |
| Warehousing & Storage | Inefficient warehouse management & identification of correct goods | AI powered robots and Intelligent Automation provides for proper identification of goods and even rerouting when required | Optimization of product placementAutomating order processing |
| Pricing dept. | Adjusting prices as per market condition | AI algorithms, dynamic pricing is made possible in real-time responding to market conditions & thus, actively tailor product offerings and pricing strategies | Enhance Sales & ProfitabilityPrice OptimizationReal-time dynamic pricing possible |
| Logistics | Delays and deliveries had riddled the retail market | Real-time tracking and monitoring of goods and vehicles Identify delays and provide for route optimization | Timely deliveries & customer satisfaction |
| Marketing and Advertising | Lack in business insight, badly designed campaigns & missed targets | Analysis of vast customer data in real-time, evaluation of sentiment analysis, and analysis of historical campaign performance | Customer segmentation for targeted and effective campaigns Personalized product recommendationsBoosts customer engagement |
| Sales | Delays in responses and predicting demand | Chatbots and Virtual Assistants provide for accurate and precise information, are available 24×7, give a personalized interaction | Demand prediction accuracy improved. Ensures retail has the right product when required. Immersive & personalized experience enhancing customer satisfaction. |
| Maintenance | Equipment failure Delayed Production schedules | ML algorithms analyze equipment sensor data, enable proactive servicing, and minimize downtime | Optimized resource allocation and production schedules Operational costs minimized. Ensures seamless supply chain and logistics operations. |
According to IDC Europe researchers, “Most retailers expect to explore Large Language models (LLMs) and Foundational Models (FMs) applications in marketing, sales and customer engagement. Nearly 40 percent of worldwide retailers and brands are in the experimentation phase of GenAI, trying to figure out the most relevant field of applications and use cases, while 21 percent are already investing in the implementation of GenAI technologies… Nearly 50 percent of them will prioritize GenAI marketing use cases over the next 18 months.
Understanding the growth and impact of Gen AI in the retail industry, it is necessary to explore the software architecture that is essential to implement AI services for various enterprises.
Gen AI has three essential characteristics that both retailers and customers could leverage for an experience that benefits both which includes intelligence, contextual awareness, and always-online presence. However, before one plunges headfirst into incorporating Gen AI, a robust software architecture as shown in the image below is essential for a successful AI implementation in retail. Data structure, algorithms, and overall infrastructure are part of this software architecture. Let us explain how this works, in short.
Data Structures:
Data is the core from which Gen AI technologies deliver business insights. To facilitate the seamless flow of how data is handled and use of AI models to gain insights, the following layering and processing with appropriate AI models and Gen AI tools is required.
Data Ingestion: This involves customer interactions and their transactions that are ingested from various touchpoints, including online channels, mobile apps, and in-store sensors.
Data Processing: This ingested data is then processed in real-time using stream processing frameworks like Apache Kafka, that enable the extraction of relevant features and signals for analysis.
Storage of Data: Data is stored in cloud or data lakes, warehouses, and streaming platforms. Although cloud storage is often touted as the best storage for businesses today, for retail, edge computing which has matured, ensures not just operational efficiency, reliability, and enhanced customer experiences. This supports data-driven decision-making, better security, and enhanced personalized services!
Processing and Component Analysis: Using AI and Gen AI advanced techniques such as edge computing tools, retailers can ensure the seamless flow of critical data for optimal business performance. Using AI for analytics helps optimize warehousing operations thereby improving storage space and efficiency.
Retailers can thus efficiently manage vast amounts of structured and unstructured data, enabling advanced analytics and AI-driven insights.
AI Model–Serving:
Use of AI models to deliver insights in real-time is very crucial to the retail business. These are crucial for customer segmentation, product recommendations, and dynamic pricing. Having this layer for model serving and encompassing components for AI model deployment, maintaining versions, monitoring & scaling is essential. Containerization technologies like Docker and Kubernetes ensure efficient deployment and management of AI models across the distributed environment. This enables seamless integration with applications and workflows.
Feedback Loop:
Customer feedback and interactions are continuously fed back into the system, enabling model retraining and refinement over time.
Overall Infrastructure:
Given the above architecture, certain IT infrastructure needs to be in place, especially a scalable infrastructure that can accommodate evolving customer expectations and data volumes which would need faster processing times and responses.
Retailers need to thus invest in the right infrastructure and disruptive technologies to stay fully competitive, harnessing the power of Gen AI.
Advanced technologies such as AI and Machine Learning (ML) algorithms are enabling the retail sector to extract insights from huge databases. These technologies are helping drive more informed decision-making and providing personalized customer experiences. Further, the Internet of Things (IoT) devices are impacting inventory management and supply chain operations, all working towards better efficiency and reduced costs.
However, unlike traditional retail AI solutions, which rely on historical data to detect patterns in new information and deliver intelligent recommendations, Gen AI retail systems go much further and simulate market conditions and scenarios and stress-test supply chain models. All the above requires great computing power for the volumes of customer data to provide real-time analysis.
Integrating GenAI into legacy systems has many advantages, especially to automate repetitive tasks and unlock new insights and capabilities, thus ensuring older platforms within the organization are still functional without incurring huge costs and still deliver superior customer experiences in an increasingly digital world.
Calsoft with 25+ years of experience, understands the challenges with legacy systems and infrastructure. We support and enable organizations to adapt to market dynamics delivering seamless experiences by bridging the gap between traditional and innovation. Our Gen AI services provide Product Development & User Experience Design; Testing & Quality Engineering; and integrations and Plugin Development to Integrate Products and Platforms with Generative AI Tools.
