The evolving role of Agentic AI in ERP
Agentic AI is quickly emerging as the next major shift in enterprise technology. Unlike earlier generations of analytics or automation, Agentic AI introduces autonomous software agents that can set goals, make decisions and execute actions across systems in real time. With every organization under pressure to move faster, operate more efficiently and adapt continuously, the appeal is obvious.
At the center of this transformation sits the ERP system. For decades, ERP platforms have served as the backbone of enterprise operations, supporting finance, supply chain, manufacturing, HR and procurement. They house the transactional data and process logic that organizations rely on to run their businesses.
Yet ERP systems were never designed to operate autonomously. They excel as systems of record, preserving data integrity, enforcing controls and ensuring compliance. What they don’t do or do well is orchestrate actions across a growing landscape of applications, data sources and business events in real time.
This is where Agentic AI changes the equation. Agentic AI introduces a system of action — one that can sit over existing ERP environments to coordinate processes, connect data and drive decisions across the organization.
Taking a composable, smarter path enables faster and more cost-effective results with less risk. Strengthening existing ERP systems and layering Agentic AI on top is quicker, less disruptive and more economical than replatforming or migrating to vendor-controlled SaaS solutions. Most importantly, it keeps the organization flexible in its future roadmap.
However, to unlock meaningful Agentic AI outcomes, organizations must first ensure their ERP foundation is AI-ready. By ensuring the ERP beneath it is stable, trustworthy and well governed foundation.
What does a solid ERP foundation look like?
A solid ERP foundation isn’t defined by whether a system is on premises or in the cloud, or by how recently it was upgraded. It’s defined by how reliably it supports the core ERP Processes the organization depends on.
Across industries, organizations execute the same fundamental ERP Processes, such as order-to-cash, procure-to-pay, record-to-report, etc. These processes are remarkably consistent, even as the technology used to support them has evolved. Agentic AI doesn’t replace these processes. It amplifies them.
To do that effectively, several foundational elements must be in place:
1. Clean, centralized data
While Agentic AI systems are designed to learn and adapt, they cannot fully compensate for poor or fragmented data models. Learning algorithms can infer patterns, but they amplify whatever foundation they are given, both good and bad. Fragmented master data, inconsistent definitions and duplicated records introduce ambiguity that can undermine autonomous decision-making. To ensure reliability, organizations must know the limits of what AI can and cannot do with imperfect data. Agentic AI thrives on contextual truth, not guesswork. A solid ERP foundation where core data is governed, centralized and aligned provides the bedrock for trustworthy automation. Without this, even the most advanced AI agents risk making flawed decisions at scale. Clean, centralized data is a prerequisite for an effective, Agentic-ready ERP.
2. Documented and standardized processes
Agentic AI can infer patterns and even learn user intent over time, but it cannot fully replace the need for clear, standardized processes. When workflows are undocumented, overly customized or reliant on tribal knowledge, automation amplifies chaos instead of creating efficiency. AI agents execute processes as they are defined, and if those definitions are ambiguous or inconsistent, outcomes will be unpredictable and risky. Perfect standardization is not the goal, but a baseline of documented, repeatable processes is essential. These guardrails provide the structure for AI to act consistently and safely, while enabling faster onboarding and reducing dependency on individual expertise.
3. Reliable performance and availability
Agentic AI operates in real time. ERP environments plagued by latency, instability or batch delays disrupt decision loops and automation chains. Predictable system performance is essential for autonomous execution.
4. Strong security, compliance and auditability
As AI agents assume responsibility for executing business processes, security and governance become foundational requirements. IT and security teams must enforce role-based access, continuous security monitoring and end-to-end auditability to ensure autonomous actions remain compliant, explainable and resilient. Organizations must partner with security experts who will ensure their defense posture is maintained without forcing disruptive ERP upgrades or migrations that can introduce new vulnerabilities and operational risk into stable, mission-critical systems.
5. Reliable, comprehensive support
By leveraging the expertise of a trusted, third-party ERP support and services partner such as Rimini Street, organizations can keep their systems running optimally and make continuous improvements, further optimizing the foundation for the introduction, configuration and maintenance of Agentic AI systems.
The risks of having a weak ERP foundation
Deploying Agentic AI on top of a weak ERP foundation can introduce risk rather than value.
- Poor data quality leads to unreliable outputs and erodes trust in AI-driven decisions.
- In regulated environments, inconsistent data and opaque automation increase compliance exposure.
- Decision loops break when agents cannot reliably access or act on ERP information in time.
There is also a clear economic impact. Organizations already spend most of their IT budgets maintaining existing systems, leaving little room for innovation. When Agentic AI initiatives stall due to foundational issues, skepticism grows, rework increases and expected returns fail to materialize. Instead of accelerating operations, AI becomes another layer of complexity.
Without a solid ERP foundation, Agentic AI may amplify existing problems rather than solve them. Upgrading systems to the vendor’s cloud won’t likely fix these issues; it just moves these issues to a new version to be addressed after the upgrade.
Innovative use cases for Agentic AI ERP
When built on a strong, composable foundation, Agentic AI can unlock meaningful improvements across core ERP-driven processes. A composable foundation enables enterprises to use interchangeable applications and services, giving them the autonomy to select, integrate and evolve technology based on business needs rather than vendor constraints. In this environment, AI agents can orchestrate processes, connect systems and drive outcomes at scale.
Vendor onboarding
In vendor onboarding, AI agents can orchestrate data validation, compliance checks and approvals across ERP, finance and third-party systems without manual handoffs.
Quality inspection
In quality inspection, agents can analyze operational data in real time, detect anomalies and trigger corrective actions automatically.
Order fulfillment
Order fulfillment becomes more resilient as agents continuously align demand, inventory and logistics constraints across systems.
Inventory transfer
Inventory transfers can be optimized dynamically based on real-time signals rather than static planning assumptions.
Predictive maintenance
Predictive maintenance is another high-impact area, where agents monitor asset and operational data, anticipate failures and initiate maintenance workflows before disruptions occur.
In each case, the value comes not from automating a single task, but from orchestrating outcomes across processes and systems.
How to strengthen existing ERP without replatforming using Agentic AI
Organizations don’t need to replace their ERP systems to prepare for Agentic AI.
In fact, existing ERP software can continue to provide value for many years as a stable transactional backbone. The key is to shift innovation away from the ERP core and into an agentic layer that operates over the top.
First, it’s important to recognize that ERP vendors aren’t always positioned to innovate at the pace AI demands. Many organizations achieve better results by deploying AI outside the ERP core and using the ERP as a trusted system of record and execution engine.
Second, Agentic AI should be applied to end-to-end processes, such as order-to-cash, procure-to-pay, hire-to-retire and record-to-report. These processes already exist and offer clear opportunities for incremental automation and measurable outcomes.
Third, organizations should move use case by use case. Starting small, proving value and expanding incrementally reduces risk and accelerates time to value. With modern low-code and no-code technologies, many Agentic AI deployments can deliver results in weeks rather than years.
Finally, Agentic AI enables a shift from historical reporting to predictive and real-time decision support. Instead of explaining what happened last quarter, use AI agents to help determine what action should be taken next.
Key takeaways
Agentic AI represents a fundamental shift in how ERP-driven processes are executed — transforming them from static systems of record to dynamic systems of action. However, that shift only succeeds when it is built on a solid, composable ERP foundation.
With the right foundation in place, enterprises can extend the life of their ERP systems, unlock autonomous execution and achieve faster, lower-risk returns on AI investment. The future of ERP isn’t defined by wholesale replacement but by building the foundation that allows Agentic AI to deliver real, measurable outcomes.
Learn how Rimini Street helps transform how leaders Support and Optimize their software portfolios so that they can Innovate with Agentic AI ERP — all via a proven methodology called the Rimini Smart Path™.
