The rise of Artificial Intelligence (AI) has accelerated the digital transformation for enterprises across industries. With the global AI market size projected to reach $266.92 billion by 2027, it’s clear that AI is one of the fastest developing technologies today with the most diverse applications that are relevant across industries. A Microsoft commissioned Economist Intelligence Unit report that surveyed over 400 senior executives working in various industries states that over one in four respondents say their organizations have incorporated AI into key processes and services, while another 46% have one or more AI pilot projects underway.

The writing on the wall is clear: AI is here and it’s here to stay. While the impact of AI is being observed across all industries including retail and CPG, only huge strides made by industry leaders in the space (like Amazon or Alibaba) have had the resources to successfully harness the true prowess of AI to generate paramount business value. This creates a wider divide between AI leaders and the majority of retailers still struggling to capitalize on AI. And while retailers across the world have made some progress in adopting AI, most still have a long way to go.

What can small to medium-sized retailers do to stay ahead of the game? One compelling route is the adoption of AI, which is a necessity due to the COVID-19 outbreak. Due to the naturally reduced consumer spending during the global pandemic, retailers are required to embrace AI and enjoy its benefits. From using computer vision to customize promotions in real-time to applying machine learning for inventory management, retailers must learn to tap into the potential of AI to connect with their customers and operate more efficiently. Furthermore, AI enables retailers of all shapes and sizes to capture value at the enterprise level and reduce operational costs.

However, even today retailers face significant challenges in adopting smart AI-powered solutions. Let’s explore some of the key barriers to AI adoption for retailers.

Data Privacy: The innate need for AI-powered systems to penetrate any and every aspect of retail business’ data carries threats involving consumer data security and privacy issues.

Data Quality: The limited quality/accuracy of data is often cited as a limiting factor to AI adoption. AI is data-heavy and without the infrastructure to secure strong, clean, accurate, and relevant data, AI becomes incapable of performing the desired tasks.

Resistance to Change: AI brings forth the promise of productivity, engagement, and ground-breaking efficiency. But as with any sort of workflow change, retailers require strong organizational change management and communication initiatives in place to derive value from AI.

Cost Constraints: The implementation of AI exhausts a significant amount of resources and time. Business leaders need to determine their business goal and build an AI integration strategy around it. However, the ROI amounts to more than the spending at the early stages of AI adoption.

Scarcity of Skilled Personnel: Implementing AI and extracting value from it can be remarkably complex. As a result, professionals with the right expertise and tools need to be drawn in to help facilitate changes and capitalize on AI-driven opportunities.

Unclear Business Case: A common perception exists that several AI solutions on the market today are not able to make a significant influence on business growth and generate ROI, which slows down AI adoption.

Evaluating the Right Vendors: Failing to identify leaders that help facilitate AI-led development and transformation can lead to huge losses, both in terms of money and time spent. To solve this dynamic, have your vendors tackle a small business problem first before taking on ambitious plans. This will help you determine whether the vendors you choose understand your business, have the right skills, and know-how to address specific pain points.

AI in the retail industry has a host of opportunities for retailers in the future. Let’s explore some of the core use cases and benefits of AI in the retail and CPG industry.

Merchandising Automation: AI-enabled merchandising automation helps vendors significantly increase sales and meet never-ending and fast-changing customer demands. Merchandising automation also enables retailers to leverage data from across channels and ensure that each customer receives the right promotions based on their preferences.

Fraud Prevention: The retail industry has been struggling with rapidly increasing in-shop frauds over the past decade. But modern retailers worldwide, with the help of the right vendors, are leveraging a combination of beacons, smart detectors, machine learning, and video analytics to help catch odd behavioral patterns that may suggest the likelihood of theft or fraud.

Customer Journey Mapping: Customer journey mapping is considered an effective way to improve customer experience among retailers worldwide. Retailers leverage data and AI to develop a more accurate view of their customer’s journey. This helps them observe an uptick in revenue and a reduction in the marketing budget.

Product Recommendations: Product recommendations powered by search data help retailers offer relevant and personal shopping experiences. By processing publicly available data, buyer history, and live data like real-time location and weather, retailers produce a recommendation optimized for a particular customer.

Dynamic Pricing: Machine learning algorithms are used by retailers to seamlessly identify patterns from the data and predict prices based on the data—ranging from competitors’ pricing, purchase histories, and seasonal demands. This allows retailers to accurately forecast the price that will suit users at a particular moment.

Chatbots: Chatbots are vital for retailers as they are the key to providing personalized, consistent, and engaging CX that customers around the globe seek. Chatbots leverage NLP and computational linguistics that allow retailers to reach new customers, answer customer queries, gain insights into customer behavior, and in some cases, increase sales.

Customer Behavior Prediction: AI and ML technologies in retail stores empower retailers to understand and analyze the buying behavior of the customers. This helps retailers not only understand customers better but also provide a hyper-personalized in-shop experience to customers.

Real-Time Inventory Management: Retailers today leverage AI and historical purchase data to gain a complete picture of what’s occurring with inventory, enabling them to react quickly to supply chain needs. Real-time inventory management also helps retailers to find irregularities in sales and product volumes, leading to overall efficiency in business.

Demand Forecasting: AI-powered demand forecasting strategies help inventory managers to maintain products with respect to demand and perform accurate demand prediction. This also helps top-performing retailers replenish stocks on the go and at scale.

Automated Sales Order Generation: Sales order automation is identified as a proven means to enable modern retailers to keep up with their company’s growth. AI-enabled sales order automation helps businesses replace time-consuming manual processes and incorporate a simple and more efficient way to drive business growth. Integrating sales order automation solutions also help terminate human errors that can incur huge costs for correction and resolution.

Technology is at the disposal of many new-age retailers today but the use of AI hasn’t been democratized to more minor and local retailers. Quantiphi is one of the first AI-driven organizations to address this gap. Our differentiated solutions across the retail value chain create new opportunities for retailers to be better prepared for the rapidly evolving future. Recently, Quantiphi partnered with CONA Services (Coke One North America) to build a breakthrough AI solution called OrderSmart™ for seamlessly automating sales order generation. OrderSmart™ is a predictive ordering solution that uses proprietary AI and ML algorithms to transform the manual sales-order generation process of retailers and distributors worldwide.

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Partnerships similar to Quantiphi and CONA Services are vital to enable retailers of all shapes and sizes to transform digitally by leveraging a healthy blend of advanced analytics, cutting-edge AI, and machine learning techniques. Correspondingly, Quantiphi has been at the forefront of empowering retailers with AI-powered intelligent automation solutions at every step of the way—to ensure more efficient inventory allocation, improved operations, and an enhanced, personalized shopping experience. To learn more about Quantiphi’s capabilities, visit https://quantiphi.com/ or reach out to our experts