Artificial intelligence is now part of day-to-day ecommerce operations. Per McKinsey, 88% of organizations report regular AI use in at least one business function—up from 78% the prior year—with roughly one-third beginning to scale their own AI programs.
But in the context of enterprise resource planning (ERP), AI is often a marketing angle. Vendors integrate rule-based workflows into their software and label it an “AI-powered ERP system.” Because ERP governs financial records, supplier payments, and inventory, adding superficial automation and bolted-on external tools—without rethinking workflows or governance—can introduce errors that compound across the business. Mistakes can get expensive quickly.
This guide shares how advanced AI works in ERP, where it delivers real value rather than. hype. We’ll also go over what can go wrong, and how to implement AI capabilities safely and measurably with the oversight and approval controls ERP demands.
AI in ERP describes artificial intelligence features embedded into enterprise resource planning (ERP) workflows and data. It can make suggestions, predict future trends, process documents, and detect anomalies. Some platforms also have built-in copilots that can give you feedback on ideas and draft summaries and reports.
For example, instead of a human manually checking if an invoice matches a purchase order, AI can view the document, extract the data, and flag discrepancies between the two.
There are three types of AI ERP solutions:
Some technology providers are layering in traditional rule-based automation and labeling it as “AI.” That simpler type of automation follows fixed rules. AI analyzes data to identify patterns and make recommendations. In ERP workflows, fixed rules can repeat mistakes at scale if guardrails aren’t in place.
| Use case | Normal rule-based automation | Artificial intelligence |
|---|---|---|
| Invoice processing | Matches an invoice submitted to your AP system with a supplier’s PO | Extracts data from a PDF invoice into the ERP, and flags discrepancies and inconsistencies—like multiple names for the same business |
| Inventory management | Raises a purchase order with a supplier according to a preset 14-day lead time | Balances factors like weather patterns, port congestion, and seasonality with anticipated demand to create supplier POs to avoid stockouts (requiring human approval before placing orders) |
| Handling refunds | Gathers data from a returns portal and inputs into your ERP system for fulfillment teams to analyze | AI agents recommend auto-approvals for low-risk refunds while summarizing the common reasons for a refund and suggesting ways to combat it (for example, creating a size guide) |
Tip: Shopify’s Global ERP Program connects the unified commerce platform with leading ERP systems including NetSuite, Brightpearl, and Microsoft Dynamics 365.
An ERP is the control center of business operations, especially for omnichannel brands. Data from every sales channel comes together into the system of record, which increases both the impact of decisions and the risk of errors. But with so much information to sift through, it’s difficult to distill the ERP data you need to make data-driven decisions quickly.
Plus, rule-based automation offers limited return on investment (ROI). It’s tethered to human-defined logic that cannot adapt to shifting trends or learn from complex data patterns.
Enterprise brands are moving from basic ERP reporting and surface-level automation to ERP intelligence because it offers:
IBM reports “enterprise-AI bullish” organizations experience 27% higher ROI and 9% stronger operating margins compared to those who are “bearish.” It also found transformational AI leaders executed 80% more enterprise AI and GenAI projects, and reported 4.4x greater integration of AI processes with their ERP platform.
Not all AI features carry the same benefits—or risks. Understanding how each type works helps teams decide what kind of AI is best suited to optimize their ERP workflows.
While standard ERPs tell you what is in your warehouse right now, predictive analytics in AI-driven ERPs help estimate what you may need in three weeks. It feeds historical ERP data (orders, supplier performance, financial cycles) into machine-learning models that can spot patterns.
Practical use cases of predictive analytics in ERP include:
In ERP contexts, forecasts should help inform decisions. They shouldn’t automatically trigger action—unless clear thresholds and approvals are in place.
Businesses often struggle when business information is trapped in text formats that standard automation rules can’t read. Natural language processing (NLP) solves this by reading the content, extracting the data, and formatting it into your ERP system.
For example:
When NLP updates supplier or financial records, a quick human review can prevent small mistakes from spreading through the system.
Machine learning (ML) helps systems spot patterns in data and flag things that look off. Finding patterns helps show anomalies, classify data, and make recommendations.
For example, in your warehouse, a specific picker’s error rate might spike by 15% on Tuesday afternoon. The ML identifies the pattern and correlates this with a faulty handheld scanner or a specific bin location that is hard to reach. You can make changes based on this insight—perhaps replacing the scanner or adjusting your warehouse layout—to maintain accuracy.
With ERP workflows, ML works best for providing recommendations that teams can review before taking action.
Robotic process automation (RPA) handles repetitive steps by following preset rules. It can automate routine tasks like:
While RPA can improve accuracy and support faster data processing, it needs guardrails. It executes exactly what it’s programmed to do—so be specific and clear with your programming. Data errors can throw off accuracy if future plans and context aren’t considered.
Set clear and specific exception-handling rules—for example, “refund requests for orders over $100 require human approval”—and keep certain tasks human-owned to make RPA safer to deploy.
An AI chatbot uses large language models (LLMs) to understand a user’s input and retrieve answers based on business data housed inside your ERP system.
For example, you could ask the AI assistant questions like:
AI copilots should provide analysis—not make final ERP decisions. The suggestions it provides may not always make the most sense, so keep humans in the loop for approvals. For example, if it recommends improving the product imagery for a particular category but you plan to revamp that entire assortment in the coming months, it might not be the best use of your time.
AI can retrieve rows upon rows of data, but it’s easy for important insights to get lost when you’re staring down a huge report. Generative AI acts as the final layer. It takes raw numbers from your ERP and translates them into digestible formats like:
In ERP, generative AI outputs should be treated as drafts—not final records—because models can produce incorrect details or miss important context humans know.
This use case is particularly popular: per one report, overall GenAI adoption rose 44.6% year over year, and self-reported time savings from GenAI made up 1.6% of all work hours.
| AI type | ERP example | Risk level | Human input |
|---|---|---|---|
| Predictive analytics | Forecasting a 20% stock shortage in Q4 due to port strikes | Medium | Approval before ordering |
| Natural language processing | Reading a supplier email and updating a “Shipment status” field in the ERP | Low | Occasional audit |
| Agentic AI | Identifies a picker has 20% lower accuracy and schedules a review for their assigned barcode scanner | High | Data input with continual model training and auditing |
| Robotic process automation | Raising a PO with the relevant supplier when safety stock levels dip below your threshold | Medium | Occasional audit with preset guardrails |
| Chatbots | Answering a question like “Why did shipping costs increase during Q4?” | Low | Occasional audit |
| Generative AI | Generating inventory turnover reports | Low | Fact-checking |
AI implementation in ERP is often limited less by what’s possible and more by what’s safe to scale. Because ERP touches core financial and operational workflows, the strongest starting point is use cases that are both measurable and relatively low risk.
Focus on workflows that actually move metrics like cash conversion cycle, inventory turns, forecast accuracy, procurement efficiency, and close speed—while keeping approvals in place. This balance helps teams prove AI’s value and secure buy-in.
Financial data is high-volume and repetitive, making it an ideal starting point for agentic AI use cases—especially when outputs are reviewed before they touch the ledger:
The supply chain industry is increasingly affected by disruptions like port congestion, tariffs, and labor shortages. AI can help teams plan around variability and support procurement decisions.
High-impact use cases of ERP AI in this context include:
Inventory management is a thorn in many retailers’ sides. There are endless situations that can throw allocations off course.
AI inside your ERP can mitigate this with:
Dive deeper: ERP for Inventory Management: Features, Benefits & Implementation
Warehousing was one of the first business processes to be transformed by technology. Robotic picking machines are commonplace in most large-scale smart warehouses.
Apply this optimization to your ERP through AI tasks like:
As with other ERP use cases, teams get better results when recommendations are measured and reviewed before changes roll out at scale.
Customer support is a common starting point for brands who want to implement AI. It’s relatively easy to configure an automated chatbot to handle customer queries based on published policies (returns, shipping, etc.)
When AI is integrated with ERP systems, it can unlock other high-impact use cases:
As the system of record for your entire business, traditional ERP systems contain sensitive information. Because so many core processes run through ERP, small AI mistakes can have oversized consequences. That’s why the way AI is implemented often matters more than the technology itself.
It’s no surprise teams are concerned about:
Bear in mind that a successful pilot AI ERP implementation program doesn’t guarantee enterprise-wide scale. Treat early rollout as an experiment: workflows may need to be redesigned and data cleaned before expanding to higher-stakes automation.
Artificial intelligence capabilities are evolving quickly, often faster than existing laws.
Some regulations exist, like the EU AI Act for brands operating in/into the EU. It uses a risk-based approach to categorize use cases. Apply these to your own AI projects: inventory forecasting might be categorized as minimal risk, but some functions, like hiring, are almost always high-risk and require legal signoff.
Elsewhere, other policies and frameworks help reduce the risk of using AI in ERP and future-proof your infrastructure.
For US businesses, the NIST AI Risk Management Framework (RMF) breaks this into four categories:
AI uses historical data from your enterprise resource planning (ERP) system to predict what’s next, automate certain tasks, and interact with users in plain English (for example, summarizing inventory performance or flagging unusual transactions).
While some ERP platforms are iterating on AI-native features, many vendors are simply reskinning old workflow automation and calling it AI. Check the difference by asking whether the AI improves recommendations from historical data over time, rather than just following preprogrammed rules.
AI will become more common within ERP systems, especially for repetitive tasks and low-risk decision-making. Humans will still need to be in the loop to oversee legal compliance, train the AI systems, and verify that what it says is accurate (for example, reviewing any AI-suggested changes that affect purchase orders or accounting).
The safest first AI use cases in ERP include:
