Artificial intelligence (AI) has been a topic of interest in the retail industry in Canada and beyond as of late. Now that it’s here, many retailers are looking at how they can adopt and integrate AI into their businesses in an effort to build efficiencies while optimizing operations where possible.
As the benefits of AI become more apparent, the industry is expected to continue to adopt the technology rapidly. Already, retailers are finding ways to utilize AI in day-to-day operations.
AI Brings Opportunities for Enterprise Retailers
Retailers are already finding that AI offers exceptional capabilities and tremendous possibilities, along with some risks. ChatGPT is a particularly buzzworthy topic, with some experimenting with its capabilities. While it might be tempting for retailers to begin utilizing new tech, retailers should consider the maturity of each specific tool before implementing them.
Customer service chatbots are an excellent example of AI in a mature state — many companies use AI chatbots to handle customer service inquiries such as returns, given that it’s more cost-effective than relying on human agents.
Automating manual data processes with machine learning (ML) is another way retailers already use AI to gain efficiency. Pivotree recently benchmarked its ML applications, like Pivotree DIVE, against the ChatGPT app and determined that the contextual learning of DIVE proves to be more effective at discerning product data attributes.
AI Improves the Frictionless Commerce Experience
It’s known in the industry that personalization is crucial to frictionless commerce, but achieving hyper-personalization is an ongoing challenge for retailers. Segmentation is a time-consuming endeavour and requires extensive content for individual personas.
It’s the reason why segmentation is a compelling use case for AI for retailers, as the tech allows them to build out personas extensively. ChatGPT-like technology can also help retailers create merchandising content at scale — including easily translating for specific languages or geographies.
The ability to deliver personalized content and AI-driven product recommendations to specific personas could help retailers significantly improve order frequency and average order value, as well.
Ways Retailers Should Apply AI to Gain Backend Efficiency
Efficiency and cost savings are where AI’s benefits are among the strongest. This is good news for retailers that face labour constraints, tight product life cycles, supply chain challenges, and shrinking windows for delivery.
Machine learning for data processes
Rapid product attribution is one example of how AI can help retailers manage expansive product catalogs. It works by automating areas such as mapping unstructured data, product classification, data normalization, and generating product recommendations.
Previously, enterprises would focus on merchandising the top 10-20% of their catalog, leaving the rest untouched for the most part. It’s been shown that AI enables a deeper dive into the catalog by repurposing data or recognizing what might be true for similar products. The model starts to learn from corrections, which allows for better classification over time.
Pivotree DIVE is an example of using ML to ensure content, SKUs, and products are optimized in a highly efficient way. Companies can auto-classify 80-90% of their product data to quickly launch products and gain hundreds of hours in time savings.
Sales and demand forecasting is another area where retailers can adopt AI to improve backend fulfillment. By leveraging historical, seasonal, and economic data, retailers and other businesses can optimize inventory levels. This is particularly important because having too little inventory could result in missed revenue, while having too much stock could lead to losses from selling the excess at a deep discount – something many retailers have struggled with.
AI robotics in warehouses
Retailers can also use advanced picking algorithms and AI robotics to fill orders as quickly as possible. The goal is to satisfy customer demand for fast delivery and compete with companies such as Amazon that offer same-day or next-day delivery.
Pivotree offers this AI technology through a composable supply chain model and technology partners like GreyOrange. Advanced robotics can be deployed to pick orders more efficiently. And AI is being used to lay out warehouses in the most efficient way possible. These technologies help reduce friction and cost, while also enabling businesses to fulfill orders faster to meet delivery expectations.
Should retailers be concerned about the risks associated with implementing AI?
ChatGPT is the fastest-growing app of all time, but there are certainly limitations. OpenAI notes that ChatGPT sometimes writes “plausible-sounding but incorrect or nonsensical answers.” And ‘hallucinations’ are a well-documented complaint from users.
Retailers have also voiced concerns about data privacy, potential integration issues, and difficulty determining what type of AI technology to prioritize.
It’s been shown that the best way to navigate this complexity is to start with experience strategy. With AI having so many applications, it is paramount that one does their due diligence on what problems one wants to solve and how AI can help.
Next, one must consider infrastructure. Layering AI onto monolithic platforms can be difficult and increase one’s risks related to cost, security, and stability. On the other hand, a composable commerce model can help retailers compile best-of-breed solutions to solve specific problems — many of which will leverage advanced AI technology that can significantly improve one’s retail business.
AI has the potential to revolutionize the retail industry, and enterprises should prepare to capitalize on the innovation coming to market. A business’ competitive advantage depends on it.
Thus, careful planning and infrastructure considerations can mitigate risks and help you harness the power of AI for your retail business.
Joel Farquhar, Vice President of Engineering at Pivotree
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