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Revolutionizing Retail: AI and Automation for your operations

AI and automation are at the forefront for many retailers these days.  

Look no further than how frequently the topics were mentioned at Shoptalk last month or how many retailers are highlighting automation, particularly in areas such as fulfillment, to enable them to better serve customers. 

Amidst all the buzz and fanfare, though, if you’re unclear where to start with regards to AI & automation, here’s a quick, three-step process to identify opportunities where these solutions can deliver high impact. 

1. Identify pain points and opportunities in current operations. 

Too often, the conversation about AI and automation starts with a solution in search of a problem, rather than the other way around, which leads to misplaced efforts and ineffective implementations. 

Instead, identify areas with pain points in your current operations that might be an opportunity for AI or automation to solve. This could involve conducting a thorough analysis of existing processes and identifying areas where inefficiencies exist. 

  • What are opportunities to improve your customer experience that are difficult to tackle today? 
  • Where are your operations having a hard time keeping up with customer demand? 
  • What area has a high degree of operational complexity with many handoffs and, thus, has increased chances for error and opportunity for improvements? 

As mentioned, a classic area for this is ecommerce order fulfillment where digital order demand continues to surge, and the default solution has been to add headcount to pick and pack orders to send to customers.  

However, labor shortages and rising wages, coupled with advances in technology, have made automated fulfillment solutions attractive to retailers to better meet increasing demand while bypassing the typical operational bottleneck of adding more associates.  

Chewy is a great example of a retailer that has put this into practice. 

Opportunities for AI and automation are not limited to any one part of the retail business. Marketing, merchandising, procurement, and others present opportunities to help solve key pain points – and create a better customer experience – as retailers continue to grow.  

2. Implement AI and automation solutions to address pain points and opportunities. 

Once the pain points and opportunities have been identified, you can implement AI and automation solutions that address these areas. This may involve in-depth evaluation of different solutions – including through a request for proposal (RFP) process – to select the best fit for your specific needs. 

Fulfillment aside, some common areas to deploy automation solutions include: 

  • Improved customer experience: According to a study by Accenture, 75% of consumers are more likely to buy from a retailer that recognizes them by name, recommends options based on past purchases, or knows their purchase history. AI-powered personalization can help you deliver these experiences to customers. 
  • Increased operational efficiency: A study by McKinsey found that you can achieve up to 60% cost savings by implementing automation technologies such as autonomous vehicles, drones, and robotics in their supply chain and warehousing operations. 
  • Enhanced inventory management: You can leverage AI and machine learning to optimize inventory management through improved forecasting. Capgemini research on these tools, cites inventory cost reductions of up to 20% and improved product availability of 30%. 
  • Fraud detection and prevention: Theft and fraud in retail is an increasing issue for retailers. AI and machine learning can help retailers identify and prevent fraudulent transactions 

3. Iterate and improve: monitor processes and results to maximize the benefits of AI & automation technologies.  

Much like any other implementation of a solution, it is rare that deployment of a technology is perfect or fully optimized on the first try.  

AI & automation are no different. 

To maximize impact, identify the measures of success or KPIs during the discovery process alongside pain points to understand how solving the challenges will result in long-term improvements. 

As an example, when thinking about using automation for your fulfillment operations, measuring improvements to the number of orders fulfilled per hour (capacity/output) and/or picks per hour (efficiency) is critical since these are central to any business case for automation technology. 

In the world of marketing and retail media networks, an example might be using AI to improve target audiences. You would then evaluate key measures of success, such as customer engagement and, ultimately, return on ad spend (ROAS) for any specific campaign using AI to optimize audience(s). 

Identifying measures of success and then ensuring a strong measurement plan in place to monitor and iterate is as important as the technology itself.  

AI relies on continuous data and feedback to improve. Your processes should as well. 

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