Top 10 Agentic AI ERP Systems & 6 Solutions

Gartner predicts that by 2028, one-third of enterprise software will include agentic AI, making up to 15% of daily decisions autonomous. ¹

Agentic AI ERP refers to AI agents integrated into Enterprise Resource Planning systems, enabling data analysis, prediction, and autonomous actions. For example, an ERP agent might detect a shipping delay and autonomously reroute deliveries, notify customers, and update inventory.

Explore agentic AI ERP systems for enterprise-level and smaller and mid-sized businesses (SMBs):

Note that these tools are listed alphabetically.

Enterprise ERP platforms with agentic AI

Large ERPs are leveraging agentic AI to automate complex, multi-step workflows with AI agents, such as:

IFS Cloud

IFS targets asset-intensive industries with AI agent orchestration. Through TheLoops’ Agent Development Lifecycle (ADLC), IFS allows companies to design, deploy, and monitor multiple agents. The AI agents can

• Schedule technicians, optimizes routes, and communicates with customers.
• Replenish inventory, adjust production.
• Predict failures, sources spare parts, and triggers repairs.

Infor CloudSuite

Infor’s Coleman AI acts as a voice/chat-enabled assistant and predictive automation engine to:

• Execute ERP actions via natural commands.
• Predict supply needs, schedules maintenance, and flags anomalies.
• offer industry-specific skills, accelerating deployment for manufacturing and healthcare.

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Microsoft Dynamics 365

Microsoft integrates Copilot across Dynamics ERP and CRM. Copilot agents operate with human-in-the-loop or autonomous modes, powered by Azure OpenAI. This allows businesses to offload repetitive processes like vendor follow-ups.

• Supplier Communication Agent autonomously emails vendors, parses replies, and updates ERP orders.
• AI highlights anomalies in demand planning and rescheduling.
• Copilot provides instant answers on ERP data (e.g., overdue invoices).
• Drafts responses and summarizes support cases.

Oracle Fusion Cloud ERP

Oracle embedded 50+ Oracle AI agents into Fusion Cloud ERP, SCM, HCM, and CX. Powered by Oracle Cloud Infrastructure (OCI) GenAI, these agents combine LLMs with retrieval-augmented generation (RAG) to ensure responses are accurate and secure. Oracle AI agents can:

• generate anomaly explanations, variance narratives, and predictive forecast drivers.
• Draft project reports and plans by mining historical data.
• Auto-generate product descriptions and negotiation summaries.
• Provide personalized job fit explanations and Q&A.
• Summarize chat sessions for faster support.

SAP Joule AI

SAP introduced SAP AI agents across finance, supply chain, HR, and procurement. Joule operates as both a copilot(conversational interface) and a network of autonomous agents. These agents leverage SAP’s Business Data Cloud and Knowledge Graph, ensuring they act across SAP and non-SAP data.

• Accounts Receivable Agent automatically analyzes overdue invoices and initiates follow-ups.
• Sourcing Agent creates sourcing events by analyzing supplier history.
• Maintenance Planner Agent adjusts schedules based on predictive signals.
• Performance Management Agent provides coaching insights for reviews.

Workday

Workday defines agentic AI as systems that initiate, plan, and act autonomously.

• Recruiting: AI shortlists candidates, schedules interviews, and chats with applicants.
• Finance: Reconciles accounts, flags anomalies, and adjusts forecasts.
• HR: Agents provide personalized employee development paths.

Workday emphasizes augmentation which AI initiates tasks while humans retain oversight.

SMB ERP Platforms with Agentic AI

For smaller and mid-sized businesses (SMBs), agentic AI is becoming an accessible and cost-effective tool, as their ERP platforms are leveraging natural language interfaces and pre-built automations to streamline operations without the need for large IT teams:

Microsoft Business Central

Business Central now includes AI Copilot that can deliver:

• Conversational Q&A on sales, inventory, and finance data.
• Suggested reordering points, flags anomalies, and auto-drafts product descriptions.
• Integrated with Power Automate + AI Builder for custom bots.

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Odoo 19

Odoo integrates an AI assistant directly into its ERP apps (CRM, Accounting, Inventory). The assistant is LLM-agnostic, supporting OpenAI, Gemini, or open-source models. Odoo Agentic ERP:

• Automates lead assignment, overdue invoice reminders, and weekly summaries.
• Generates marketing copy, product descriptions, and task assignments.
• Takes smart actions to auto-detect patterns and automate workflows.

QuickBooks

QuickBooks uses Intuit GenOS AI interface to:

• Auto-categorize transactions.
• Predict cash flows and flags anomalies.
• Answer financial queries with chat assistant.

Sage Intacct

Focused on finance automation by:

• Auto-categorizing bills, matches POs, and posts AP entries.
• Reconciling subledgers and flags variances.
• Learning from corrections, acting as a “junior accountant.”

Zoho Zia Agents

Zoho transformed its assistant into a full agentic AI platform with capabilities like:

• Pre-built agents, such as customer Support, Inventory Manager, HR Interview Scheduler.
• Custom agents to built via low-code Agent Studio.
• Agent marketplace to deploy agents with one click.
• Agents orchestrator across 50+ apps, handling multi-step workflows (e.g., onboarding + invoicing).

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Technical integration of agentic AI in ERP

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Large language models and natural language interfaces

Most agentic AI capabilities are powered by large language models (LLMs), which enable conversational AI experiences in ERP systems. These models interpret natural-language queries and generate human-like outputs, allowing users to interact with the ERP by simply asking questions or issuing instructions.

Unlike older tools, modern LLMs support complex reasoning, enabling them to help with sophisticated business processes such as multi-step financial forecasting or personalized HR development plans.

Retrieval-augmented generation and knowledge graphs

Since LLMs lack direct access to enterprise data, ERPs use retrieval-augmented generation (RAG) to ground AI responses in organizational records. This involves combining artificial intelligence with real-time data retrieval from ERP databases, document repositories, or knowledge graphs. 

For example, SAP’s Knowledge Graph links customers, invoices, and supply chain data so that AI agents can reason across relationships. Oracle applies a similar method through secure RAG pipelines on OCI, ensuring accuracy without exposing sensitive data.

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APIs, tool use, and Robotic Process Automation (RPA)

For AI to go beyond advice and take action, it must interact with ERP functions through APIs and robotic process automation. APIs allow AI to create transactions such as purchase orders, update supplier records, or schedule jobs.

Where legacy modules lack API access, RPA bridges the gap by mimicking user interactions in the interface. This combination empowers AI agents to act autonomously, handling both modern integrations and legacy systems seamlessly.

Multi-agent orchestration

Many Agentic AI systems rely on multiple specialized agents collaborating to complete workflows. Agentic orchestration frameworks manage delegation, communication, and escalation among agents.

For example, in supply chain management, one agent detects a predicted shortage, another creates a procurement order, and a third updates production schedules. These platforms provide frameworks for coordinating these agents so business processes remain cohesive and transparent, even when distributed across departments.

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AI studios and lifecycle management

ERP vendors provide low-code development studios to help customers design, test, and monitor their own AI agents. These tools allow business users to define goals in natural language and deploy agents into production.

These platforms include compliance checks, testing sandboxes, and usage monitoring, ensuring agents behave predictably. They also support cost control by tracking how resources like LLM tokens are consumed, which is essential for scaling agentic AI.

Cloud infrastructure

Behind these tools lies the backbone of cloud computing, which provides the scale and reliability needed for AI to run within ERP systems. Vendors ensure that sensitive ERP data is never shared outside the organization, maintaining trust while still enabling advanced analytics. 

Role-based access extends into the AI layer, guaranteeing that AI actions remain aligned with corporate security policies. This ensures that agentic AI systems do not only work effectively but also meet compliance and security requirements for mission-critical operations.

Use cases for Agentic AI in ERP

Supply chain and operations

Agentic AI revolutionizes supply chain management by bringing adaptive intelligence to workflows. AI agents constantly monitor stock, supplier reliability, and logistics. They then act autonomously to prevent disruptions by rerouting shipments, updating customers, and adjusting production schedules in real time.

AI agents can also predict demand to optimize replenishment and rescheduling with minimal human oversight, resulting in a more resilient and proactive supply chain.

Finance and accounting

In finance, AI agents automate reconciliation, reporting, and compliance. They can automatically extract and match invoice data, post entries, and flag anomalies. For month-end processes, agents reconcile ledgers and generate explanations, providing clear context.

They also accelerate cash flow by following up with customers on overdue payments. This streamlines processes, leading to faster and more accurate reporting.

Human resources

Agentic AI automates both administrative and strategic HR tasks. Recruiting agents can shortlist candidates and schedule interviews. For current employees, AI agents monitor engagement and performance to suggest tailored development opportunities, which is valuable for retention.

Routine tasks like onboarding paperwork, leave requests, and benefits inquiries are also automated, allowing HR professionals to focus on strategic initiatives.

Procurement

Procurement is ideal for Agentic AI because of its repetitive nature. AI agents automate vendor communication by sending reminders and updating records.

They also manage sourcing by analyzing supplier data and drafting RFPs. This ensures compliance and prevents delays. By integrating with various systems, AI can execute complex tasks, making procurement faster and more consistent.

Customer service and sales

Agentic AI enhances customer-facing operations. AI-driven service agents can answer questions 24/7 using real-time data. Sales assistants can draft proposals, generate personalized offers, and nurture leads.

These systems use conversational AI to engage directly with customers and prospects, improving satisfaction and sales productivity by offloading repetitive communication from human staff.

Data and analytics

Agentic AI adds significant value to data management. AI agents ensure data quality by identifying and automatically cleaning duplicates, inconsistencies, and missing information.

For analytics, managers can query data in natural language and receive narrative explanations and recommended actions. By using predictive modeling, agents can highlight trends and anomalies, turning the ERP from a passive tool into an intelligent advisor.

Further reading

Explore more on how AI agents used in other systems, solutions and industries:

• AI Agents vs Agentic AI Systems
• 40+ Agentic AI Use Cases with Real-life Examples

Reference Links

1.

How Intelligent Agents in AI Can Work Alone | Gartner

Gartner

2.

Infor and AWS enterprise generative AI | Blog

3.

Erste Schritte mit einer Business Central-Vorschauversion für Copilot - Business Central | Microsoft Learn

4.

Zia Agents: Limitless. Autonomous. Supersonic.

Industry Analyst

Hazal Şimşek

Industry Analyst

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Hazal is an industry analyst at AIMultiple, focusing on process mining and IT automation.

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