10 Present and Future Use Cases of Artificial intelligence in Retail industry venkat k Follow Mar 5 · 4 min read

10 Present and Future Use Cases of Artificial intelligence in Retail industry

Artificial intelligence in retail is being applied in new ways across the entire product and service cycle from assembly to post-sale customer service interactions, but retail players need answers to important questions

In this article, we cover a variety of examples in which AI is being integrated in the retail industry, broken down into the following categories

Sales and CRM Applications

Customer Recommendations

Manufacturing

Logistics and Delivery

Payments and Payment Services

Also Read: Top Use Cases of Artificial Intelligence in Retail Industry

Sales and CRM Applications

In 2010, Japan’s telecom operations partnered with a robotic manufacturer to develop a humanoid robot that can interact with customers and perceive human emotions. In Japan, where it is used as customer service and representative in mobile stores.

Additionally, the retailer experienced a 98% increase in customer interactions, a 20% increase in foot traffic and, a 300% increase in revenue.

While not the only retail robot in use, in-store robots appear to at least initially boost store interest and sales. This is a great effect that will wear off once retail robots become the norm remains to be determined.

Customer Recommendations

It’s no longer a secret that AI is providing a slew of order management and customer engagement capabilities to eCommerce retailers.

Companies have also adopted AI in cognitive computing technology to help consumers determine what jacket is best for them, based on variables like location and gender preference. It’s safe to say that similar systems can be built with simple if-then rules and no machine learning whatsoever.

The advantage of using machine learning in such a recommendation Q-and-A interface, it can potentially run tens of thousands of consumers through this conversational engine.

At a certain volume of customer interactions, the system might be expected to some important insights and patterns on suggestions that “work” (high take-rate to purchase, or high cart value), and those that don’t — allowing the company potentially gain higher and higher conversions over time.

Manufacturing

Companies set in manufacturing robots speed up “smart” operations on a specific task, originally designed for spot-welding and assembly lines. In 2016, the goal of accelerating deep learning in robots, learn a manufacturing task through the use of a sensor that collects and stores data.

The robots’ ability to adjust to real-time environmental conditions and adjust motion can result in up to 15 percent cycle-time improvements in spot welding. Once the robot’s learning process is complete, the sensor is removed and the trained robots are then able to complete a task autonomously.

The robots’ learning is paired with vibration control in the form of an accelerator that reads the robot’s motion and provides increased movement stability. One well-known car company has employed about 600 robots, to speed manufacturing efforts for the next slated delivery.

Logistics and Delivery

In addition to some requirements, prototype delivery robot can keep food and drinks at the appropriate temperature, the sensors help to navigate the best travel path for delivery and integrate robotics technology previously used for military combat training.

Companies doesn’t provide dates and they might be rolled out on a commercial scale, but it seems that robotic delivery of food — and other goods — could be a burgeoning reality within the next decade.

Payment Services

Some companies employ check-out-free technology that allow customers to shop and leave. Customers use app to check in, but the entire shopping experience is designed to be automated.

Sensors track which objects customers pick up and put in their basket, and the customers’ accounts are automatically charged after exiting the store.

The intended launch hasn’t been without its barriers, and sources close to the retail giant announced that it is delaying the opening of its convenience stores while it worked out “technology glitches” in the automated shopping and purchasing process.

Conclusion

In many of cases, retail-focused AI vendor companies, we’re told that “big box” retailers are extremely slow to adopt cutting-edge technologies. Because it’s mostly large companies that have the budgets and data volume required to make the most of many of today’s best AI technologies, we outright surmise that an “AI revolution” in the retail space is unlikely. It may be another three to five years before most large retailers have substantial, business-critical AI applications in manufacturing, supply chain logistics, or customer service.