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.
What is agentic AI and why does it matter for ERP?
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.
Charlie’s evolution: From knowledge assistant to agentic engine
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.
Agentic AI in action: Three strategic ERP impact zones
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? |
For Dynamics 365: Copilot + Agent
Microsoft Dynamics is embedding agentic-style Copilot agents directly into modules like Finance, Supply Chain, and HR, enabling autonomous actions within its cloud ERP portfolio.
Fortude’s agentic layer for Dynamics complements MCP-like controls, domain logic, and stronger orchestration so that the built-in agents and our higher-level agents can coexist and cooperate safely.
How Fortude embeds Agentic AI safely & effectively
Governance & safety first
Agentic systems bring risk: misaligned actions, unpredictable behavior, and “objective drift.” To mitigate these we recommend:
- Role-based permissions and least privilege enforced via MCP.
- Audit trails for every agent transaction.
- Confidence thresholds and fallbacks (agent halts if confidence is too low).
- Human-in-the-loop escalation for ambiguous or high-impact decisions.
Incremental rollouts & fail fast
We start with narrow, high-value use cases (like release analysis), then scale horizontally. This approach mitigates risk, allows learning, and builds trust with end users.
Continuous learning & feedback
Agents monitor outcomes, compare with ground truth, and adjust models. This ensures the system becomes more precise over time, not static.
Domain integration
Because Fortude already has deep experience in Infor, M3, and Dynamics, we embed domain logic, configuration context, and ERP best practices, not just generic AI models.
What this means for ERP users & project stakeholders
| Benefit | Description |
|---|---|
| Speed & agility | Faster adoption of updates, faster decision cycles |
| Reduced manual effort | Agents replace repetitive tasks so teams can focus on higher-value work |
| Proactive alerting | Agents catch issues before they escalate |
| Higher consistency & auditability | Less risk of human oversight in configurations |
| Scalable autonomy | Agents can coordinate and scale with ERP growth |
Step into the era of autonomous ERP
Agentic AI is no longer a future ambition, it’s here, embedded in the tools you already use. Whether you’re navigating complex ERP updates, forecasting demand across regions, or trying to optimize inventory without constant firefighting, agentic AI can be your catalyst for real, scalable transformation.
At Fortude, we are building trustworthy partners for your ERP ecosystem. Ready to explore how Charlie, CharlieX, and our AI-driven automation services can elevate your Infor or Dynamics operations?
Let’s talk. Contact our team to learn how to embed agentic AI into your ERP strategy.
FAQs
Yes,Gartner estimates that over 40% of agentic projects may be scrapped by 2027 due to unclear value propositions, poor governance, or unrealistic expectations. These risks are real, but they’re manageable. We mitigate them by focusing only on high-ROI, applying tight governance frameworks, ethical and operational guardrails, and an incremental deployment model that ensures each phase delivers validated outcomes before scaling further.
The timeline may vary depending on the organization and readiness of it. The first phase typically involves setting up foundational infrastructure, aligning teams, and building confidence through pilot use cases. Once adoption and training stabilize, productivity gains, process optimization, and decision-support improvements become visible.
It’s possible, but primarily when systems are poorly designed or lack clear constraints. Agentic systems must be governed by robust safety and compliance frameworks to ensure reliability, include predefined fallback thresholds, and human oversight for all critical actions. These controls ensure that agents operate within intended parameters, learn safely, and defer to humans when uncertainty or ethical ambiguity arises.
Not at all, agentic systems are designed to complement human expertise, not eliminateit. They excel at automating repetitive, data-heavy, or time-consuming tasks, freeing people to focus on higher-value judgment, creativity, and strategic oversight. Humans remain responsible for defining goals, managing exceptions, and ensuring ethical alignment.
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