AI—retail’s cornerstone for the long haul
2024 and beyond will be about making AI adoption in retail as mainstream as possible and designing the strategies and guardrails around its adoption. Retail leaders are bullish about AI’s role in improving growth and profitability. According to Gartner, AI and machine learning will be among the top game-changing technologies to be implemented in retail by 2026. The emergence of large language models (LLMs) and generative AI rekindled AI’s importance in retail. LLMs and generative AI can unearth hidden consumer insights, create a 360-degree view, and enable hyper-personalized engagement. Beyond customer experience, AI’s influence in retail extends to operations—streamlining the supply chain, reducing cost, and increasing efficiency. AI's integration into the retail sector catalyzes innovation, increases productivity, and fosters a more competitive and dynamic marketplace.
Experiences—unified by analytics, data, and AI cloud
Get started with AI cloud.
AI cloud provides the infrastructure to train AI models and manage data more efficiently. It also provides flexibility and scalability, enabling seamless integration of AI into various retail operations. Moving to the cloud helps retailers bring all their customer data (online and offline) together for a unified, 360-degree view. Analytics and AI can cull out insights that help retail brands connect with customers in the moment in a contextualized way. For example, Shopify’s merchant Rainbow Shops built an advanced search feature integrated with Google Cloud’s Discovery AI solution, increasing their search volumes by 48%.
Enhance the in-store experience with AI.
BOPIS (Buy Online, Pick-up In-store) combines the ease of online shopping with the immediacy of picking up goods in-store, thereby eliminating shipping time. The global BOPIS market will be worth $666 billion by 2028, growing at a CAGR of 11.57%. By integrating AI into BOPIS, brands can optimize operations, tailor experiences, and increase loyalty. Advanced AI-driven recommendation engines can nudge consumers and lead to more purchases in-store. Curbside pickup is another area where retailers could leverage AI algorithms to forecast 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 bring orders to customers’ vehicles. Target’s Drive Up is a curbside pickup service that utilizes AI to streamline store pickups and wait times for consumers.
Streamline supply chain and operations, reduce costs and wastage.
AI helps supply chain and logistics businesses factor in multiple variables such as lead time, shelf life, production capacity, and transportation constraints. The industry is moving toward intelligent and autonomous supply chain management. Predictive maintenance and route optimization reduce costs, introduce warehouse and back-office automation, and improve efficiency. With the infusion of AI and Gen AI, businesses can ensure that relevant products find the right customers rather than the traditional way around.
Optimize resource utilization and the workforce.
Automated scheduling algorithms powered by AI streamline employee shifts, considering factors like foot traffic and sales trends. Gen AI enhances communication by generating personalized, context-aware messages for staff, fostering a more engaged workforce. This is particularly relevant in the quick-service restaurant industry. Video analytics, IoT, and AI help reduce cook time, check-out time, and driving time, making the in-store experience seamless. AI-driven analytics can assess employee performance, aiding in targeted training programs. Retailers like Walmart deploy AI-powered chatbots to assist employees in navigating HR-related queries efficiently. This optimization boosts operational efficiency and ensures a more satisfied and productive workforce, ultimately contributing to a seamless and enhanced customer experience.
Charting the course ahead
With AI becoming ubiquitous, retailers must understand critical areas to create clearly defined roadmaps for widespread optimization.
- Data quality: Poor training data makes it difficult for AI models to learn patterns accurately. Inaccurate data may lead to bias, flawed insights, and poor decision-making.
- Data integration: Siloed data across disparate systems may prevent the integration and harmonization of data—democratizing this data is crucial for AI investments to bear fruit.
- Talent expertise: Hiring skilled professionals and domain experts and training the existing workforce to leverage the technology effectively is vital.
- ROI: Implementing AI cloud and its associated infrastructure requires significant investment. Demonstrating healthy ROI stems from maximizing the use of AI on the cloud while addressing the ethical and privacy concerns around its adoption.
Investing in the AI cloud requires a strategic approach. One that clearly articulates the business objectives and goals that the business hopes to achieve with AI cloud adoption. Furthermore, conducting a readiness and maturity assessment toward AI adoption is imperative. Choosing the right AI cloud is not a one-size-fits-all approach. Instead, the strategy must align with the stated use cases and other factors such as ROI, integration capabilities, scalability, and AI/ML framework support.