AI-retail: The Long Haul's Core Pillar
Explore cutting-edge retail interactions, powered by artificial intelligence, for an updated shopping experience
The future is all about making AI in retail as common as a shopping cart and constructing strategies for its widespread adoption. Retail leaders are banking on AI's potential to boost growth and profits. According to Gartner, AI and machine learning will be among the top game-changers in retail by 2026. Large language models (LLMs) and generative AI are breathing new life into AI's importance in retail. These technologies can uncover hidden consumer insights, generate a 360-degree view, and enable personalized engagement like never before.
Beyond enhancing customer experience, AI's impact extends to retail operations. Streamlining the supply chain, reducing costs, and boosting efficiency are all possible thanks to AI. As AI becomes a mainstay in the retail sector, it fosters innovation, raises productivity, and creates a more dynamic and competitive marketplace.
Data, Analytics, and AI Cloud—Unifying Experiences
AI cloud provides the foundation to train AI models efficiently, manage data more effectively, and ensure scalability. Migrating to the cloud allows retailers to gather all their customer data (online and offline) to craft a unified, 360-degree view. Analytics and AI can then pull out insights that enable brands to engage with customers in real-time, contextually. For instance, Rainbow Shops, a Shopify merchant, integrated Google Cloud's Discovery AI solution into an advanced search feature, fueling a 48% increase in search volumes.
In-store experiences can be improved by incorporating AI as well. BOPIS (Buy Online, Pick-up In-store) combines the benefits of online shopping with the immediacy of in-store pick-up, obliterating shipping time. The global BOPIS market is projected to reach $666 billion by 2028, growing at an impressive CAGR of 11.57%. By integrating AI into BOPIS, brands can optimize operations, tailor experiences, and increase customer loyalty. Advanced AI-driven recommendation engines can prompt consumers and lead to more in-store purchases. Curbside pickup is another area where AI algorithms could be leveraged to anticipate demand and inventory requirements. AI-powered analytics could help consumers and stores manage curbside pickups better by studying traffic conditions and parking availability and optimizing routes for staff to deliver orders to customers' vehicles. Target's Drive Up, for instance, relies on AI to streamline store pick-ups and reduce wait times for consumers.
Supply Chain and Operations—A Match Made in AI
AI helps supply chain and logistics businesses factors numerous variables, such as lead time, shelf life, production capacity, and transportation constraints. The industry is transitioning toward intelligent and autonomous supply chain management. Predictive maintenance and route optimization reduce costs, introduce automation into warehouses and back-offices, and improve efficiency. With the infusion of AI and generative AI, businesses can ensure that relevant products find the right customers, rather than the traditional approach.
Optimizing Resources and the Workforce
Automated scheduling algorithms powered by AI streamline employee shifts, considering factors like foot traffic and sales trends. Gen AI refines communication by generating personalized, context-aware messages for staff, thereby fostering a more engaged workforce. This is especially essential in the quick-service restaurant industry. Video analytics, IoT, and AI aid in reducing cook time, check-out time, and driving time, making the in-store experience as smooth as possible. AI-driven analytics can appraise employee performance to facilitate targeted training programs. Retailers like Walmart leverage AI-powered chatbots to help employees manage HR-related queries efficiently. These optimizations boost operational efficiency and safeguard a more satisfied and productive workforce, ultimately resulting in an enhanced customer experience.
A Clear Road Ahead
With AI becoming ubiquitous, retailers need to define crucial areas to create roadmaps for widespread optimization:
- Data Quality: High-quality training data is vital for AI models to detect patterns accurately. Subpar data can lead to bias, erroneous insights, and poor decision-making.
- Data Integration: Breaking down data silos across various systems is crucial for AI investments to thrive. Democratizing data is essential.
- Talent Expertise: Acquiring skilled professionals and domain experts and training the existing workforce to use the technology effectively is vital.
- ROI: Demonstrating positive ROI necessitates maximizing the use of AI on the cloud while addressing ethical and privacy concerns related to its adoption.
Investing in the AI cloud calls for a strategic approach. One that sets clear business objectives and goals for AI cloud adoption and conducts a readiness and maturity assessment for AI adoption. Choosing the right AI cloud is not a one-size-fits-all affair. Instead, the strategy should align with the stated use cases, considering factors like ROI, integration capabilities, scalability, and AI/ML framework support.
- The goal is to make AI in retail as common as a shopping cart, allowing retail leaders to leverage AI's potential to boost growth and profits.
- By 2026, AI and machine learning are expected to be among the top game-changers in retail, according to Gartner.
- Beyond enhancing customer experience, AI can streamline supply chains, reduce costs, and boost efficiency in retail operations.
- The AI cloud provides a foundation for efficient AI model training, effective data management, and scalability, enabling retailers to craft a unified 360-degree customer view.
- AI-powered analytics can help manage curbside pick-ups by studying traffic conditions and parking availability, optimizing routes for staff to deliver orders to customers' vehicles.
- In the supply chain and logistics industry, AI helps manage various factors like lead time, shelf life, production capacity, and transportation constraints.
- AI can optimize resources and the workforce through automated scheduling algorithms, personalized communication, video analytics, IoT, and AI-driven analytics for employee performance appraisal.
- To successfully invest in the AI cloud, retailers should focus on data quality, data integration, talent expertise, and demonstrating a positive return on investment (ROI), while aligning the strategy with use cases, considering factors like ROI, integration capabilities, scalability, and AI/ML framework support.
