Artificial Intelligence in ERP: Types, Use Cases & Implementation (2026) – Shopify

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.

What is AI in ERP?

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:

• AI-native ERP: Built from the ground up with AI as the foundation
• AI-enabled ERP systems: AI features are baked directly into the existing modules by the vendor
• ERP connected to AI tools (bolt-on): Using third-party AI platforms (like an external forecasting tool) that pull data via API, process it, and push results back into the ERP system

ERP AI vs. automation 

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.

Tip: Shopify’s Global ERP Program connects the unified commerce platform with leading ERP systems including NetSuite, Brightpearl, and Microsoft Dynamics 365.

Why AI in ERP matters in 2026

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:

• Proactive vs. reactive insights: Rather than just reviewing what happened last month, AI identifies emerging trends and anomalies in real time when data is clean and consistently captured, allowing you to pivot before a problem impacts the bottom line.
• Automated decision orchestration: AI moves beyond notifying a human to actually suggesting and executing complex tasks—such as rebalancing inventory across warehouses or flagging a specific supplier at a high risk of delay (with clear approval steps). 
• Reduced technical debt and faster ROI: Unlike rule-based systems that require constant human updates, intelligent ERPs learn from new data, significantly lowering long-term total cost of ownership (TCO) of the system and quickening time to value when implemented carefully and governed over time. 

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.

The core types of AI used in ERP today

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.

Predictive analytics

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:

• Demand planning: Traditional ERPs use simple moving averages, while an AI-powered ERP pulls in external signals like social media trends and weather forecasts. If the AI identifies a viral trend for linen shirts in California, for example, it might shift stock from a slow-moving warehouse to your West Coast distribution center.
• Intelligent reorder points: If AI sees that a supplier’s lead time is currently fluctuating between 14 and 22 days due to a seasonal holiday in the supplier’s country, the ERP could temporarily raise the reorder point for the month to prevent stockouts. 
• Returns forecasting: AI can spot trends in returns data, like customers who buy a dress in the size medium return it 70% of the time with the comment “it runs small.” The AI agent could then build a size guide to integrate into product pages to prevent future returns. 

In ERP contexts, forecasts should help inform decisions. They shouldn’t automatically trigger action—unless clear thresholds and approvals are in place.

Natural language processing (NLP)

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:

• Supplier and logistics emails: Instead of a human manually reading an email, NLP extracts the supplier’s name, the event (e.g., delay), and the reason (port strike) and updates the ERP record automatically.
• Invoice notes and descriptions: AI can distinguish between “shipping fee” and “shipping address,” or identify that a line item described as “high-performance thermal base layer” matches the ERP’s SKU for “winter undershirt.”
• Customer support tickets: NLP can perform sentiment analysis on tickets. If 200 people mention “zipper” and “broken” in the same afternoon, for example, the ERP can automatically flag a potential batch defect to the quality control team.

When NLP updates supplier or financial records, a quick human review can prevent small mistakes from spreading through the system.

Machine learning

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)

Robotic process automation (RPA) handles repetitive steps by following preset rules. It can automate routine tasks like:

• Clicking buttons
• Copy and pasting text
• Moving and reconciling data between systems
• Generating reports
• Creating purchase orders and invoices 

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. 

Chatbots and AI assistants

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:

• Why are shipping costs up?
• What alternative suppliers could we use for Black Friday, and how fast could they get 5,000 units to our West Coast warehouse? 
• Which product pages likely have a low conversion rate because of low-res product imagery?

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. 

Generative AI in ERP 

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:

• Summaries
• Reports
• Drafts 

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. 

Comparison: ERP AI examples

High-impact AI in ERP use cases (what to prioritize first)

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. 

Finance and accounting

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: 

• Anomaly detection: Prevent fraud and accidental overpayments by having the AI flag an invoice if bank account details changed.
• Close acceleration: AI can reconcile transactions between supplier portals, customer orders, and accounting platforms within your ERP system. 
• Cash forecasting: AI can incorporate historical payment behavior and external signals to improve cash forecasts—supporting planning without replacing human judgment. For example, if the AI knows a B2B buyer always pays 15 days late, it can adjust cash flow plans to account for that gap between fulfillment and revenue.

Procurement and supply chain logistics

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:

• Price predictions: AI lets you go beyond tracking last month’s spend and predicts upcoming price hikes. For example, if you know the price of recycled polyester is predicted to rise 8% next month, you might create a PO in advance to mitigate the new rising cost.
• Lead-time predictions: Instead of relying on a vendor’s standard 14 days, machine learning can analyze actual delivery history, port congestion data, and weather patterns. This helps you plan alternative sourcing methods or stock up in advance. 
• PO automation support: Have AI draft POs with the relevant supplier when safety stock levels dip below your threshold based on the vendor’s average lead times and any anticipated market changes. 

Inventory and demand planning

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:

• Forecasting: AI can digest large datasets to predict future demand more accurately than looking at historical data alone. For example, a clothing brand might expect a 30% spike in rain gear for the Pacific Northwest because the AI scanned a 14-day weather forecast and saw a recurring pattern in which rain during “back to school” week increases coat sales. 
• Smart replenishment suggestions: The AI might see that you need 400 units of a particular SKU. But it notices that if you order 500 units, you hit a 15% bulk discount and perfectly fill a shipping pallet. It also notes that you have excess cash this month, so raises a suggestion: “Order 500 units now. This saves $1,200 in unit costs and $400 in shipping.”

Dive deeper: ERP for Inventory Management: Features, Benefits & Implementation

Warehouse and fulfillment

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:

• Labor planning: AI can predict how many work hours are needed for certain tasks and schedule staff rotations accordingly. It can balance this schedule against the terms of each worker’s contract and salary. 
• Slotting suggestions: ERP AI can identify which items are bought together and how fast they move. It might use this to suggest a change to your warehouse layout: fast-moving products go closer to packing stations with commonly paired items situated nearby. 
• Return routing: Retailers process an estimated $849.9 billion of returned merchandise every year. ERP AI can ease this burden by evaluating the item’s condition, demand at various locations, and return shipping cost. For example, if a customer in New Jersey returns a “like new” product, the AI might generate a shipping label for your Manhattan store where that product is popular and close to selling out. 

As with other ERP use cases, teams get better results when recommendations are measured and reviewed before changes roll out at scale.

Customer operations

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:

• Agentic customer support: Moving beyond static “Where is my order?” bots, ERP AI can interpret the intent of complex queries—like “I need to change my address but my order just shipped”—and route the request to the right action or team to contact the carrier or reroute the package.
• Returns and refunds workflows: Instead of a simple refund, the ERP AI can analyze real-time inventory to offer personalized instant exchanges or bonus store credit at the customer’s nearest retail location. 
• Service insights: AI can digest customer complaints buried in return forms and identify at-risk VIP customers. It might trigger a proactive outreach task for a human agent with a personalized discount code to use on the customer’s favorite categories. 

Risks and limitations of AI-powered ERP

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:

• Implementation challenges: Training, process design, and data migration are lengthy and expensive, often requiring the technical expertise most teams lack in-house.
• Wariness of AI hype: “AI-powered ERP” can be a misleading phrase as many vendors layer superficial AI features on top of existing systems to tap into the trend. 
• Compliance and governance: Per Google Cloud, 37% of organizations reported data security and privacy as among their top three considerations when searching for new LLM providers. 
• Accuracy: Half of respondents from organizations using AI report at least one negative consequence; nearly one-third report consequences stemming from AI inaccuracy. Data cleanliness and regular auditing are critical.
• Workforce buy-in: Employees are often worried about AI impact: 52% of workers say they are worried about the future impact of AI use in the workplace. Almost one-third think it will lead to fewer job opportunities for them in the long run. This affects adoption and change management. 

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. 

Compliance and responsible AI in ERP (what to know in 2026)

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:

1. Govern: Who owns the AI? Is there a clear AI policy? Do role-based permissions ensure only the right systems and teams can access sensitive ERP data?
2. Map: What is the AI doing? Is it predicting sales (low risk) or payment fraud (high risk)? If your ERP’s NLP engine is viewing payment details, for example, you might anonymize that data before processing it.
3. Measure: Is the model accurate? Does it show bias against certain demographics? Regular audits help confirm accuracy before AI is more deeply integrated into ERP. 
4. Manage: If the AI is failing, do you have a clear stop process or a human fallback? For example, if AI predicts a significant stockout and suggests a big rush order, require approval for any purchase order over $100k before it’s submitted.