There’s no topic more widely discussed right now that Artificial Intelligence (AI). Since ChatGPT was launched in November 2022, the hype has not died down. Across sectors, companies were quick to realize the potential in terms of cost savings, increased productivity, improved customer experience and more. With retail being no exception.
When it comes to commerce, AI can be used to completely overhaul the customer experience from end to end. Starting with advertising, to how customers search and purchase on the website or in stores all the way through to order fulfilment and delivery.
Many retailers are already utilizing the technology in creative ways. For example, eBay has introduced a ShopBot that serves as a personal shopping assistant. This helps customers search through the website’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.
The pressure to innovate
However, for most retailers, the reality is that they’re not yet in a position to benefit from AI technology. This is due to a lack of data both in quantity and quality. Boards and markets are putting pressure on technology vendors to launch new AI products. As a result, many of the ‘new AI tools’ we see on the market today are not all that new. They’re existing technology, utilizing machine learning, that have now simply been rebranded as ‘AI Tools’.
Even retail leaders like Amazon have succumbed to these pressures. Having spent years promoting its AI-based checkout-free Just Walk Out technology, it was recently uncovered that this was in fact just cameras reviewed by real people in India. With the pressure to innovate weighing heavy, other retailers are launching new AI products before they’re tested and work the way they should, leading to further problems.
Here are three considerations retailers should be aware of before implementing AI:
1. Clean data
Currently, very few retailers have enough data to use predictive AI. However, predictive AI with bad data (or not enough data) is dangerous. It will do more harm than good as it will lead retailers to make the wrong decisions. However, good, clean data, at the speed and quantity needed, is difficult to get. It often sits in multiple systems in different formats.
The data retailers need will depend on the question they want to ask and the problem they 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?’
2. Trust and privacy
The collection and analysis of large amounts of customer data also raises concerns about privacy breaches and cyber threats. Unauthorized access to personal data through AI can erode trust. With this front of mind, 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 their business, retailers should think about how it will impact the customer experience. It's important to be as transparent as possible with customers about how the business is using AI to improve the shopping experience, while also taking measures to protect customer privacy.
3. The skills challenge
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, not to mention organizational change. The Global Workforce of the Future Report 2023 found around 70% of workers are currently working on Generative AI at their workplace. Yet, half of them don’t have any experience or training in this field.
Retailers need to review their current skills and consider what they’ll need to do in terms of recruitment or upskilling, before they can properly execute on AI. After all, 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. And, using AI tools without the necessary skills is reckless.
The future
The potential for business optimization using AI/ML models for retailers is huge. However, it's important that retailers understand the barriers to innovation and work to get the basics right. The first step is getting their data right based on the problem they’re trying to solve. Then it's considering: Do we have the right organizational skills to execute this effectively?
With so much pressure to innovate, retailers may be overwhelmed by the road ahead. Using modern technology such as Inventory Data and Order Management Systems provides the reliable, accurate data retailers need as the input for their AI/ML models. This is essential if retailers want to ensure ‘Project AI’ is set up for success.
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