Robotic process automation automates repetitive digital tasks. Understand how RPA works, how it differs from AI, and where it fits in commerce ops.
In retail, robotic process automation (RPA) uses software bots to perform repetitive, rules-based work across commerce systems. For example, a bot can follow predefined instructions to transfer order data between an ecommerce storefront, enterprise resource planning (ERP), and fulfillment tools. Many associate RPA with AI, but while they can be used together, RPA processes are not inherently AI-driven.
A 2025 survey from Camunda found that 87% of automation decision-makers and architects reported business growth from process automation during the previous year. Yet 72% said their initiatives were not keeping pace with the overall rate of organizational change. Understanding RPA can help you identify the processes most suited to RPA optimization before you invest in bots, which can increase the chances of your initiative succeeding.
This guide explains how RPA works, where it applies in commerce, and how AI is changing automation platforms.
What is robotic process automation?
RPA uses software bots to complete repetitive digital tasks according to predefined rules. Despite the name, RPA doesn’t involve physical robots. Bots are purely digital entities that work inside applications and move information between them.
RPA is not the same as artificial intelligence (AI). It carries out steps a person has already defined and does not interpret information or make decisions on its own like AI can.
How RPA works: A step-by-step walkthrough
An RPA bot doesn’t decide what to do as it goes. Someone maps out the steps in advance, including what prompts the bot to begin performing its job (the trigger), how it responds to conditions and variables in the systems it interacts with (rules), all the way through to completion of its function (output).
For example, a retailer receives a wholesale order as standardized spreadsheets attached to emails, then enters each order into an older ERP system by hand. An RPA bot can be programmed to manage that transfer through the following steps:
- Trigger:A new message in the retailer’s order inbox starts the bot. It downloads the attached order form and opens the file.
- Rules:The bot checks that the order details are present, then signs into the ERP and enters each value in its assigned field. An unrecognized SKU or incomplete address sends the order to a review queue.
- Output (or action):The bot submits the order in the ERP and records the new order number. The completed entry is the output of that run.
All bots fall into one of two categories: unattended and attended.
An attended bot performs its function when a user launches it. In commerce, a customer service representative might use an attended bot to transfer order details into a refund form.
An unattended bot starts in response to an event or schedule and runs in the background, as in the email order example above.
Shopify Flow uses a similar trigger-condition-action model for commerce operations. An event starts the workflow, a condition checks the store data, and an action updates Shopify or an installed app.
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Key RPA concepts: The four Ds of robotics and the four stages of process automation
Two frameworks help commerce teams think about RPA from different angles. The four Ds describe types of work commonly assigned to automation, and the four stages describe how an RPA program develops.
The four Ds of robotics
There are multiple formulations of the four Ds with different definitions and terms, but they are generally used to categorize the kinds of functions robots can perform. TheInternational Federation of Robotics has described the four Ds as dull, dirty, dangerous, and difficult:
- Dull: Repetitive work with little variation is well suited to RPA. Examples include copying tracking numbers, updating order statuses, or downloading the same marketplace report each morning.
- Dirty: In digital operations, dirty work involves data that requires predictable cleanup. A bot might remove unwanted characters from supplier SKU fields before importing a catalog file. More complex inputs that require interpretation can be routed to an employee or an AI-enabled tool.
- Dangerous: These are tasks that can produce costly operational or financial errors when completed incorrectly. An RPA bot might check fixed requirements before a bulk refund or inventory adjustment proceeds. An authorized employee reviews higher-risk exceptions.
- Difficult: Difficult work requires employees to repeat a precise sequence across several systems, such as reconciling marketplace and ERP order totals. Despite the difficulty of the tasks when they’re handled by a human, as long as the work consists of repeated steps that can be outlined precisely, a bot programmed to do the job can save your employees lots of valuable time.
The four stages of process automation
UiPath’s automation operating model uses four stages to represent how an RPA program matures. Applied to commerce operations, the stages look like this:
- Prove: The company tests RPA on one contained process with a clear result to measure. For example, a commerce team might try automating the daily transfer of marketplace orders into an ERP. In this pilot phase, the retailer can keep the manual process available in case there are any early issues with the RPA.
- Establish: At this stage, a retailer moves several bots into production within one business unit. The commerce operations team assigns owners, documents exception rules, and reviews run logs. Order entry and returns routing might both operate under the same automation standards.
- Expand: In this phase, the business expands RPA to additional teams and systems. Commerce, fulfillment, and finance use a shared process for selecting projects and measuring results. Here, a cross-functional workflow might carry order data from checkout through fulfillment and reconciliation.
- Scale: In the final and ongoing stage, automation is managed as a company-wide capability. Teams reuse tested components and monitor bots through central controls. A retailer entering a new market can assess whether tested components can be reused for another storefront, fulfillment partner, or ERP instance.
RPA vs. related automation technologies: What are the differences?
There are numerous technologies that are related and often associated with RPA, with meaningful distinctions in how they work and what they can do. The table below shows how these technologies differ and where each one fits, followed by more detailed definitions.
| Technology | What it does | Rule-based or adaptive? | Requires structured data? | Best fit for |
|---|---|---|---|---|
| RPA | Mimics actions across applications | Rule-based | Usually | Stable, repetitive tasks |
| AI agents | Plans and completes goal-based work | Adaptive | Not always | Context-dependent tasks |
| Business process automation | Automates complete business processes | Primarily rule-based | Varies | Cross-functional operations |
| Workflow automation | Routes work through defined steps | Rule-based | Usually | Approvals and handoffs |
| Intelligent automation | Combines automation with AI | Rule-based and adaptive | Not always | Complex, mixed-data processes |
- Robotic process automation (RPA): RPA follows the same instructions every time, such as copying order data from one system into another. If the screen or field layout changes, the bot may stop working until someone updates it.
- AI agents: AI agents select their next action based on a stated goal, available tools, and the context they receive. For example, Shopify Sidekick uses store data to answer questions and complete tasks in the Shopify admin. It presents proposed changes for review before applying them.
- Business process automation (BPA): BPA covers an entire business process from beginning to end. An order-to-cash process might begin with payment and finish with reconciliation, with RPA handling one task within that larger operation.
- Workflow automation: Workflow automation keeps work moving along a defined path. Shopify Flow, for example, can be used to watch for store events, check for the conditions set by the retailer and run a corresponding action.
- Intelligent automation (IA): IA uses RPA and AI in the same process. AI might extract information from a supplier email, then funnel that information to an RPA bot. The RPA bot then enters that information into the ERP according to a fixed set of rules.
These categories often overlap in enterprise environments. One process may use workflow automation to route work, RPA to enter data, and AI to interpret an exception.
RPA use cases in commerce operations
RPA delivers return on investment (ROI) in high-volume commerce work governed by rules. It can be especially helpful assisting workflows in which employees have to transfer information between systems that lack a direct integration.
Before implementing RPA, staff may have to copy an order from a marketplace into an ERP, compare inventory between a point-of-sale (POS) and a warehouse system, or reenter B2B account data in an older portal. An RPA bot can repeat those steps for each transaction and send only more complicated cases to employees to handle.
Below are some more specific common use cases for RPA in commerce operations.
Order processing and fulfillment
When an order arrives from a storefront or marketplace, a bot can check that the required payment and delivery data is present.
It can then enter the order into an ERP or warehouse management system and update the status. If there is missing information, the bot can send the order to an employee for review.
Mandaue Foam used Shopify Flow for the commerce-native part of this work. When an order arrives, a workflow notifies one of the company’s stores that has the product in stock.
The store can begin processing the order, and headquarters no longer has to track and assign each one manually. Shopify Flow handles the commerce workflow.
Inventory reconciliation and stock management
Say a store sale happens, but the online stock count does not update. Or a warehouse adjusts a quantity, but the marketplace still shows the old number.
RPA pulls inventory records from each system and matches products by SKU. It flags unexplained differences and posts adjustments that meet the retailer’s rules.
Offbeat Bikes encountered overselling when in-store sales did not update online inventory.
“If a bike or a certain part had just sold out at the shop, our online shoppers weren’t necessarily aware of that,” says owner Mandalyn Renicker. “That led to us overselling items and having to manually cancel orders and issue refunds.”
The bot means no one has to guess which number is correct. The retailer sets theside those limits goes to the inventory team
Fraud detection and risk-flagging
RPA can be used to apply rules to a risk score or flag produced by a fraud system. A high-risk order might be placed on hold and sent to a review queue, for example.
With Shopify’s Order risk analyzed trigger, the tool completes its risk analysis first. Shopify Flow then takes an action based on the result. Retailer W. Titley & Co. used Flow to flag high-risk orders for their team.
An RPA bot applies the same operating policy across an external order management system (OMS), payment portal, or review tool. The risk engine produces the assessment, and the bot carries out the action specified by the retailer’s rules.
B2B invoice routing and account management
Without RPA, wholesale account setup may require staff to enter the same approved buyer information in several systems. Once a buyer is approved, staff may have to create the company in an ERP, assign payment terms, add contacts, and route invoices to several people.
RPA can enter the approved account details into systems that lack a direct integration. It can also update records when an account’s terms or purchasing permissions change.
Shopify’s B2B automation templates have workflows for approving company account requests, tagging B2B orders, and sending an invoice to multiple email addresses.
These workflows handle the Shopify portion of the process. An RPA bot can pick up the next manual step, such as entering the approved company in a legacy ERP or uploading its invoice to an external accounts-payable portal.
Returns and refund processing
Return processing combines routine administrative steps with exceptions that require employee review. Once a return is approved, an RPA bot can create the return record in the order system and update its status.
It can then submit the refund or store-credit request according to the retailer’s rules. Damaged products, missing items, and disputed amounts can be routed to an employee.
Cozykids used Shopify Flow to automate work tied to refunds and cancellations across thousands of products. Creative director Panos Voulgaris explains where the saved time went, “We reinvested time that we saved on Shopify Plus into ecommerce management, optimizing shipping, ensuring quality translations, and we focused more time to curate products and produce engaging content. It’s been a huge time-saver.”
Customer service ticket routing
Ticket routing works when each case contains structured information. The bot can take the order number and reason code from the ticket, open the order record, and add its current status. It can then follow rules to assign the case to the right service queue.
The bot can then assign delivery problems to logistics, send B2B issues to a dedicated team, and add priority tags to urgent cases according to predefined rules. Free-form messages that require interpretation go to an AI system or a person, and RPA handles the lookup and routing steps around that decision.
Product catalog updates
Suppliers may send product changes in a spreadsheet or upload them to a separate portal. RPA can match each row to a product record and enter approved changes into the retailer’s product information system. Missing SKUs and unusual price changes can be sent to a merchandiser for review.
Shopify’s workflow examples give commerce teams a starting point for automation. The prebuilt templates cover B2B, fulfillment, inventory and merchandising, orders, risk, and loyalty. Teams can use those templates for Shopify-native workflows, then apply RPA to any step that still relies on a separate interface.
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Benefits of RPA for enterprise commerce teams
Enterprise RPA programs have reported gains in processing volume, accuracy, labor time, and capacity.
Higher throughput on fixed-rule work
Korean retailer Lotte’s ecommerce team once reviewed about 4,400 product descriptions per month by hand. After combining RPA with optical character recognition, one robot processed between 9,000 and 10,000 cases per month. The automation handled the repeatable screening step, leaving product compliance exceptions with employees.
Less data reentry and fewer errors
Ahold Delhaize USA’s shared-services group reports fewer errors and less rework after applying RPA to inventory and other back-office processes. Their RPA program used 65 production bots across the business.
These gains depend on accurate rules because a faulty workflow repeats the same error at scale. RPA enters approved data consistently and applies the same validation rules to each transaction. That consistency depends on accurate rules because a faulty workflow will repeat the same mistake at scale.
Measurable time and cost savings
E. & J. Gallo reported $500,000 in RPA-driven cost avoidance. Their program covered master data, warehouse management, and channel marketing processes.
Other teams can evaluate ROI by comparing current labor and error costs with the cost of building, monitoring, and maintaining the automation.
Automation across existing systems
Ahold Delhaize USAalso used RPA to work with vendor systems that weren’t built for their automation requirements.
RPA works through the same interface an employee uses. That gives enterprise teams a way to automate a task in an older ERP, warehouse system, or vendor portal without waiting for a new integration.
More capacity during order spikes
One Fortune 500 technology company used RPA and document automation for an order-to-cash process with large quarter-end spikes.
According to theAutomation Anywhere case study, the company automated 20% of the process and achieved 75% straight-through processing. They scaled the number of bots as order volume changed and reported $350,000 in savings over three months.
The bot can handle orders that meet the defined rules, and employees work the exceptions. As volume rises, qualifying orders enter the automated queue, while employees continue to handle exceptions.
When RPA is not the right fit
RPA handles high-volume tasks that follow stable rules. Another technology may make more sense when:
- The input isn’t structured. RPA needs information in consistent fields. Free-form emails, product images, and invoices with changing layouts require AI-based data extraction or human review before a bot can process them.
- The process changes regularly. Interface updates can break the selectors a bot uses to find fields and buttons. Workflow automation or an API integration is easier to maintain when the process keeps changing.
- The task is low-volume. For a low-volume or infrequent task, the time saved may not cover the cost of building and maintaining the bot. A manual process or simple workflow may cost less for smaller businesses.
- The process itself doesn’t work well. Automating a process with duplicate approvals, unclear ownership, or incomplete exception rules preserves those problems. Map and redesign the process before automating individual tasks.
- The systems already integrate. A direct API or native integration avoids screen-level automation. Shopify Flow handles trigger-condition-action workflows inside Shopify.
RPA and AI: Where the category is heading
RPA plus AI combines fixed automation with systems that interpret less predictable inputs. RPA follows a defined sequence. AI classifies text, images, or other information that doesn’t arrive in consistent fields. The bot then can then use that result to complete a rules-based task.
Agentic AI extends that model. An AI agent works toward a goal, chooses from available tools, and adjusts its steps as new information appears. Retailers are currently beginning to use automation platforms to coordinate AI agents, RPA bots, and employees in the same process.
UiPath’s guidance suggests keeping high-accuracy financial and regulatory tasks with deterministic automation. An AI agent may interpret a customer request or investigate an exception. RPA handles the fixed action that follows.
Shopify Sidekick shows how an AI assistant operates in a commerce setting. Teams can use everyday language to ask questions, analyze store data, manage orders, edit products, generate content, or build apps.
Sidekick isn’t an RPA tool. It handles requests that depend on language and store context. RPA repeats defined steps across systems. They complement each other when a context-based task leads to a repeatable back-office action.
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Understanding robotic process automation FAQ
Is AI replacing RPA?
No. AI and RPA handle different parts of automation. AI interprets language, images, and changing inputs. RPA follows fixed rules and completes repeatable actions. Enterprise platforms increasingly combine AI agents and RPA bots in the same process.
Is robotic process automation the same as workflow automation?
No. RPA copies the actions a person takes across software interfaces. Workflow automation uses triggers, conditions, and actions to move work through a defined process. A workflow may call an RPA bot when one step requires software with no API or native integration.
What types of processes are best suited to robotic process automation?
RPA is best suited for commerce processes like entering orders into an ERP, reconciling stock records, updating product data, and routing invoices. Tasks with frequent changes or judgment-heavy exceptions require another technology or employee review.
How long does it take to implement robotic process automation?
Implementation time depends on process complexity, system access, exception rates, and testing requirements. A narrow automation may take several weeks. A rollout across several systems usually takes longer because it adds security reviews, governance, monitoring, and employee training.
Can robotic process automation work with legacy systems?
Yes. RPA can work through the same screens and fields an employee uses, so it can automate tasks in older software without a modern API. Interface changes can break the bot, so legacy system automations require testing and maintenance.
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