Agentic AI in ERP: Unlocking autonomous workflows in Infor & Microsoft Dynamics

Today, the rise of agentic AI in ERP systems has rewritten the rules. Agentic intelligence is elevating ERP beyond automation, enabling self-driving, context-aware workflows. This blog will discuss how Fortude is embedding these capabilities into Infor CloudSuite, M3, and Microsoft Dynamics through Charlie, CharlieX, and smart protocols. We’ll walk through three core use cases, explain how the Model Context Protocol (MCP) enables safe AI-ERP integration, and illustrate how customers can move faster, reduce manual effort, and set up autonomous ERP ecosystems.

At its simplest, agentic AI refers to AI systems that can perceive, decide, and act autonomously toward defined goals, rather than just react to prompts. Unlike traditional “assistants” which require human direction, agentic systems can plan, monitor, adapt, and intervene.

In the context of ERP, this means agents can:

• Monitor system state, detect anomalies or exceptions.
• Automatically execute tasks or workflows (within constraints).
• Adapt based on feedback, learning over time.
• Coordinate with other agents or humans in a broader process.

As multiple industry sources emphasize, ERP systems are shifting toward event-driven, autonomous platforms powered by agentic AI, breaking away from static, reactive models.

A recent Bain survey states that 78% of IT leaders expect agentic AI to augment or replace parts of ERP within 3 years, with 44% predicting it will impact over 10% of ERP functionality.

For ERP users and implementers, agentic AI promises far more than incremental automation. It unlocks predictive, self-healing, and autonomous workflows that can reduce delays, errors, and human effort in core modules like supply chain, finance, inventory, compliance, and release management.

At Fortude Charlie, our AI companion has evolved from a smart knowledge assistant to a proactive, domain-aware agent. Previous blogs described Charlie’s foundation in Retrieval-Augmented Generation (RAG) and its ability to deliver accurate, context-sensitive responses.

Now, we are layering agentic capabilities on top of that foundation. This transition is powered by CharlieX, our insight agent, which connects AI capabilities directly to ERP and CRM systems, enabling domain-aware decision making.

Charlie already executes tasks like ticket creation, data lookups, and automation workflows through a skill manager. The future lies in agents that go beyond executing tasks to perceive system state, reason for outcomes, and initiate actions without explicit user prompts.

This evolution is already aligned with industry trends: Infor, for example, is building “domain-specific AI agents” into its industry cloud platforms and publishing about AI agent trends in their forecasts.

Rather than listing use cases, we have grouped our most successful deployments under three core business value themes. These reflect the real impact agentic AI delivers to ERP users — especially those navigating complex, multi-system environments like Infor CloudSuite and Microsoft Dynamics.

1. Operational foresight: Predict, don’t react

The problem:
ERP-led demand planning often relies on internal data alone, resulting in inaccurate forecasts and costly stockouts or overstocking.

Our agentic approach:

• Agents combine internal ERP data (sales, POs, inventory) with external signals like weather, holidays, and campaign schedules.
• AI/ML models forecast SKU-level demand and recommend POs and in-house dates.
• The result: smarter replenishment and fewer missed opportunities.

Key question for ERP leaders:

Can your current forecasting model adjust to external demand signals in real time?

2. Update agility: From release lag to release leadership

The problem:
ERP updates from Infor or Microsoft require exhaustive manual review of release notes and client-specific impact assessments delaying adoption and introducing risk.

Our agentic approach:

• CharlieX’s agents parse release documents, analyze custom configurations, and generate Jira tasks automatically.
• Multiple agents work as a “release swarm” ensuring no change goes unnoticed.

Key question for ERP teams:

How much value could you unlock if release management became 90% autonomous?

3. Seamless connection and safety at scale

The problem:
Unstructured AI use in ERP can lead to inconsistent actions, compliance issues, and security gaps.

Our agentic approach:

• Fortude’s Model Context Protocol (MCP) acts as a connector and a governance layer, defining agent permissions, data access scopes, and fallback thresholds.
• This ensures agents operate within boundaries and escalate complex scenarios.

Key question for CIOs and architects:

Is your AI framework governed by a protocol that enforces trust and accountability?