Why ERP must change now
Enterprise resource planning (ERP) — the backbone of finance, supply chain, HR and operations — is entering a new phase as agentic artificial intelligence moves from research labs into production. In 2024, CIOs and vendors are confronted with a practical question: how to embed autonomous decision-making into systems that have historically emphasized transactions, auditability and human approval. The who: major ERP vendors including SAP, Oracle, Microsoft and cloud-native suites such as NetSuite and Workday. The what: adding orchestration of agentic workflows and continuous verification. The when and where: organizations are beginning pilots and production deployments across manufacturing, retail and financial services this year, primarily in the public cloud. The why: to accelerate cycle times, reduce manual toil and extract new value from enterprise data.
What ‘agentic AI’ brings to ERP
Agentic AI refers to systems that can autonomously plan and carry out multi-step tasks — for example, reconciling payments, negotiating supplier terms, or routing corrective actions in a factory — rather than only generating outputs for human review. For ERP, that means models and orchestrators must be tightly coupled with transactional data, business rules, and role-based controls.
This shift implies several engineering and product changes. First, ERP platforms will need native orchestration layers that can spawn, monitor and reconcile agent actions with ledgers and audit trails. Second, model lifecycle management — sometimes called MLOps or ModelOps — must be integrated so models that power agents are versioned, tested and governed alongside application code. Third, data lineage and observability become operational imperatives: every autonomous decision must be traceable to inputs, model version and the business rule set that authorized it.
Background: vendors and convergence
Over the past three years, cloud vendors and LLM providers have pushed enterprises toward more capable AI primitives. Microsoft’s investments in AI across Azure and Dynamics 365, Oracle’s cloud push, SAP’s platform roadmap and the ubiquity of models from providers such as OpenAI, Anthropic and Google Cloud have accelerated interest. At the same time, robotic process automation (RPA) vendors and low-code platforms have begun to blur into intelligent automation stacks, creating a path for legacy ERP systems to adopt agentic capabilities without a full rewrite.
Use cases that matter
Practical early adopters are focusing on constrained, high-value processes: month-end financial close, procure-to-pay exception handling, warranty claims resolution, and field-service dispatching. In manufacturing, agentic orchestration can re-sequence production and expedite parts orders when supply disruptions occur. In finance, autonomous agents can pre-clear routine reconciliations and surface only anomalous items for human approval, shortening close cycles.
Expert perspectives and industry implications
Analysts and practitioners stress that the change is less about replacing ERP and more about re-architecting ERP boundaries. “Agentic systems will force ERP to be restructured around intent, verification and continuous auditability,” said an industry analyst who follows enterprise applications. “Vendors that treat agents as modular services with strong governance will gain adoption faster than those that bolt on isolated AI features.”
ERP customers echoed that viewpoint. A former CIO at a large retailer, now advising deployments, noted that pilot projects must focus on measurable ROI and compliance: “If autonomous agents touch payments or inventory, you need immutability of records and clear human-in-the-loop gates.”
Risks, governance and change management
The introduction of agentic behavior raises legal, regulatory and operational risks. Auditors will demand proof that autonomous decisions meet internal controls and external regulations. Security teams must harden model inputs and outputs to prevent data leakage and adversarial manipulation. Equally important is workforce change management: many roles will shift from execution to oversight, requiring retraining and new governance roles such as model stewards and AI auditors.
To mitigate these risks, companies should adopt a phased approach: start with narrow agents on low‑risk processes, build comprehensive observability and rollback mechanisms, and codify escalation paths for exceptions.
Conclusion: a pragmatic roadmap
The agentic AI era does not mean ripping out ERP systems; it demands reimagining their architecture. Vendors and customers must collaborate on platform-level orchestration, integrated ModelOps, robust audit trails and user experiences that preserve control while enabling autonomy. For enterprises, the immediate priorities are proof-of-concept work that demonstrates ROI, investment in governance and observability, and organizational design to manage agentic workflows. Over the next 24 months, expect ERP roadmaps to emphasize composability — modular agent services tied to trusted data fabrics — and for early movers to capture efficiency gains while setting the standards for safe, auditable autonomous operations.
About the Author
