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Retail Intelligent Automation: Use Cases & Case Studies in 2024

As competition increases, so does the value of leveraging digital technologies and automation solutions in retail. According to McKinsey, 30% to 40% of retail tasks related to processes such as merchandise planning or the supply chain can be automated.

Intelligent automation, also called cognitive automation or hyperautomation, which is the combined use of automation technologies with AI methods such as ML, NLP, OCR, conversational AI, and computer vision can help retailers automate end-to-end processes through bots with decision-making capabilities.

We have listed several use cases and case studies of intelligent automation in the retail industry.

Use cases

Customer service

RPA bots with conversational AI capabilities can handle repetitive customer service tasks and can enable retailers to provide a better and personalized customer experience by:

  • Interacting with customers throughout the shopping process, from searching for products and placing orders to tracking packages and answering FAQs,
  • Providing recommendations according to their browsing and purchasing history,
  • Collecting customer feedback,
  • Analyzing customer sentiment with NLP models.

These can allow retailers to:

  • Understand their customers better, identify pain points, and develop strategies to improve customer experience,
  • Improve employee productivity by automating manual customer service tasks and assisting them with more complex tasks,
  • Provide 7/24 customer service.

Inventory management

By leveraging ML models and historical sales data, intelligent bots can predict the optimum amount of inventory for different goods and create allocation plans for different locations and different times of the year. They can alert suppliers when stores or warehouses are running low on stock. This can help retailers:

  • Prevent stockouts,
  • Reduce waste,
  • Automate restocking orders.

Invoice automation

Manually processing invoices is time-consuming and costly: it costs around 10$ and takes 25 days to manually process an invoice. The process is also error-prone with repetitive tasks including:

  • Matching up the billed amount and the amount on purchase orders,
  • Resolving any discrepancy in the amounts charged,
  • Entering data to relevant systems,
  • Sending the invoice to relevant employees

Intelligent bots with OCR and NLP capabilities can:

  • Monitor for incoming invoices,
  • Extract relevant data from invoices,
  • Cross-check invoices against purchase orders,
  • Enter the extracted invoice data to the system,
  • Make payments and settle the invoice.

Feel free to check our article on invoice automation for a more comprehensive account.

Returns processing

“Bracketing”, or intentionally purchasing more than intended to keep, increased from 40% to around 60% after the Covid-19 pandemic. Returns are an inevitable part of online shopping, and an efficient returns management is vital for retailers as it impacts profitability and customer retention: 96% of shoppers who rated their return experience positively stated that they would shop from the retailer again.

Intelligent bots integrated with chatbots can:

  • Guide customers through the return process,
  • Collect necessary customer information,
  • Update the inventory database,
  • Send notifications to customers and employees in the finance department.

Case studies

Feel free to read our article on intelligent automation case studies. Some example case studies in retail include:


Problem: Accelirate is an RPA consultant that helps companies automate their business processes. Struggling with manually processing up to 700 invoices from gasoline and freight vendors, a major retailer consulted Accelirate to help drive invoice automation. Prior to automation, retailer’s staff had to open individual emails containing invoices, find the supplier ID, manually extract invoice data, and enter it into the internal accounting system.

Solution: Working with an intelligent automation solution provider, Accelirate developed an automation solution that combines RPA and OCR. The solution reads emails containing invoices, extracts relevant data from the invoices, sends them to payment processing, and generates a report.

Results: The retailer reduced the time to process an invoice from 3-5 minutes to 30 seconds. 93% of the invoices could be reconciled without manual review. In this way, the company saved 160 hours per month.1

Recode Solutions

Problem: A large consumer goods retailer in the U.S. had many paper-based back-office processes in its accounting, loan, and credit departments. The company also had an inefficient customer service operation that required call center staff to log into multiple systems to retrieve customer information in order to answer customers’ questions.

Solution: The retailer partnered with Recode Solutions which worked with an intelligent automation solutions provider to automate processes such as AP invoice processing, customer service tasks, and loan servicing requests. 

Results: The solution reduced the time to process an invoice from 10 minutes to 30 seconds. It processes 65,000 invoices annually. In addition, the average call handling time was reduced to 85 seconds. This helped the company save $2 million annually.2

For more on intelligent automation

If you want to explore intelligent automation use cases in your business, feel free to check our article on intelligent automation use cases & examples in different business functions and industries.

You can also check our data-driven list of intelligent automation solutions. If you need help, feel free to reach out:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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