AI in Commerce: Why retailers need to beware

Whether it's predictive or generative, you can't escape the hype about AI. There are so many examples of how AI can be used across sectors to save time, improve productivity, improve customer experience and sell more. And the retail sector is no exception.

When it comes to commerce, AI can be used to create descriptions for products, promotional content for social media and other content that improves SEO and customer engagement. Using generative models, AI can suggest new or alternative products to customers that they might be interested in based on their buying history and preferences. For example, eBay's ShopBot serves as a personal shopping assistant, helping customers search through eBay's listings to discover the most attractive deals. Customers can engage with the ShopBot using text, voice, or even by sharing a photo to indicate what they're searching for.

However, the reality is that most retailers are not yet in a position to benefit from AI technology due to a lack of data both in quantity and quality. Indeed, many of the 'new AI tools' are not 'new'; they are existing machine learning tools that have simply been rebranded as 'AI Tools'.

Currently, boards and markets are putting pressure on technology vendors to launch new AI products, which means that some of them are being launched before they're tested and work the way they should.

Clean your data
Today, very few retailers have enough data to use predictive AI. Predictive AI with bad data (or not enough data) is dangerous. It will do more harm than good as it will lead you to make the wrong decisions. Good, clean data, at the speed and quantity needed, is difficult to get. It often sits in multiple systems in different formats.

The data you'll need will depend on the question you want to ask and the problem you want to solve. For example, to optimise inventory and order management, some questions could be; 'What locations are at risk of going out of stock?' 'What is the optimal safety stock level for each SKU?' 'How often am I shipping from the 'ideal' location?' What percentage of orders are rejected by stores due to labour capacity constraints?' 'What was the average order processing time at each location?' 'What are the 10 items with the highest excess inventory at each location?'

Consider your technology and internal skill sets
According to the Global Workforce of the Future Report 2023, around 70% of workers are currently working on Generative AI at their workplace. Half of them don't have any experience or training in this field.

When it comes to generative AI, most businesses don't have the skills or the money to train generative AI engines. The investment needed is significant in both time and money as well as organisational change.

Jumping on trends without a clear understanding of their potential benefits, challenges and skills required can be costly. Investing in tools without having people who know how to use them is wasteful. Using those tools without the necessary skills is reckless.

Take responsibility
Of course, the collection and analysis of large amounts of customer data raises concerns about privacy breaches and cyber threats. Unauthorised access to personal data through AI can erode trust.

The National Retail Federation (NRF) has released its Principles for the Use of Artificial Intelligence in the Retail Sector. According to the NRF, the principles encourage appropriate and effective governance of AI, promote consumer trust, and facilitate ongoing innovation and the beneficial use of AI technologies.

When incorporating AI into your retail business, think about how it will impact the customer experience. Be as transparent as possible with customers about how your business is using AI to improve their shopping experience.  And take measures to protect their privacy.

The future
The potential for business optimisation using AI/ML models for retailers is huge. But the first step is getting your data right based on the problem you're trying to solve.
A modern Order Management System, like Fluent Order Management provides the reliable, accurate data you need as the input for your AI/ML models.  This is essential if retailers want to ensure 'Project AI' is set up for success.