A modern ERP manufacturing system is likely to include an artificial intelligence (AI) component. You’ve probably read about how AI within an ERP manufacturing system enables it to perform tasks, send reminders, route invoices, and run reports automatically.

However, what you aren’t reading about is that the current “AI sprawl,” or proliferation of AI within platforms, can lead to

some actions that increase a company’s risk. Governance, or the establishment of guardrails and rules for AI use and tasks, can help reduce that risk.

What Is Governance in AI?

When people talk about governance in the context of AI, they’re really talking about the structures and practices that keep AI systems responsible, safe, and aligned with human goals. It’s the combination of policies, oversight, and decision‑making frameworks that determine how AI is developed, deployed, monitored, and corrected when needed. In other words, governance is the guardrail system that keeps AI from drifting into harmful or unintended territory.

Good AI governance usually covers things like transparency, accountability, fairness, data handling, and risk management. It defines who is responsible for what, how decisions are made, and how issues get escalated or resolved. It also sets expectations for testing, auditing, and monitoring AI systems over time, because governance isn’t a one‑and‑done activity; it’s ongoing stewardship. Think of it like a blend of policy, ethics, and practical controls.

AI Sprawl Is Real

AI sprawl is another new term. As more and more software includes artificial intelligence built into the platform, companies find themselves with multiple AI systems that don't talk to each other or interact. Accounting may embed AI into its software to help route approvals or generate reports, while the warehouse uses an AI-powered alert system to notify managers of low stock levels. Over time, this leads to “AI sprawl.”

Coordinate AI Use Through Smart Governance Frameworks

Governance is a fancy way of saying rules or guidelines. No matter how heavy or light your company’s AI use is, you should have some guidelines in place for how AI is used in your company.

The more AI that is added to your business, either intentionally through stand-alone platforms or by adding new software that contains an AI element, the greater the risk. Data collected by such systems multiplies; without strong governance, it’s unclear who owns or controls this data. AI models can change rapidly, and each change can open new risks. Remaining on top of the situation can be a full-time job.

The key to reducing risk from AI use is a solid governance framework that guides the business in using AI effectively without losing sight of the potential risks.

Define the Rules for AI in ERP Manufacturing Systems

The first step in governance is defining exactly where AI should operate and where it must not. This means documenting approved use cases, such as forecasting demand, suggesting production sequences, or analyzing maintenance logs, and drawing firm lines around prohibited activities, like changing safety parameters. Establish the guidelines and rules, and make sure your team understands them as well. Document them in writing and in training sessions, and don’t forget that new employees will also need training, so they understand this basic first step in AI governance.

Determine the Governance Team

This is what we call “cross-functional ownership.” Someone must be designated as the clear system owner, but a team from different departments should collaborate with the system owner to build the rules. Someone from operations, IT, compliance, legal, safety, procurement, financing, and perhaps the warehouse or shop floor should work together to decide which AI tools your company uses. This team should also review the AI model’s behavior and look at how the systems interact with other data sources: production, quality tracking, and supplier data.

AI is increasingly used in scheduling, procurement, and quality control. These are critical aspects of any manufacturing business and warrant a closer look. Shared ownership among team members, each with his or her own unique perspective and area of expertise, helps ensure accountability across the company and multiple viewpoints, which can be helpful when building AI governance models.

Set Low, Medium, and High-Tier Risk Levels

AI actions within an ERP manufacturing system differ. Some have low risk, such as simply routing an expense report for approval. Others pose a greater risk, such as allowing AI to reorder low stock without approval. Low-risk activities can generally run with AI automation without a hitch but consider the potential risk of allowing medium and high-risk activities to run without human oversight. These are the tasks to focus on.

High-risk actions, such as payment approvals, should always require an employee to review and approve them. By reviewing the possible AI-based actions in your ERP manufacturing system and labeling them high, medium, and low risk, you can set governance guidelines around the high and medium-risk activities. Permission-based boundaries, audit logs, and manual approvals can keep AI in check without hurting productivity.

AI in ERP Manufacturing Systems Offers Powerful Productivity with Risk

AI used in ERP manufacturing systems will continue to grow. New models, new software, and advancements in technology will put unprecedented power at our fingertips. How we use this power and the extent to which we manage it with AI governance and frameworks will determine the risks and the associated rewards around this powerful technology. With planning and teamwork, you can manage the risks and still reap the benefits of AI-enabled manufacturing software.

PositiveVision

Positive Vision works with manufacturers to help them choose the right ERP system. We can help you build AI governance models and learn more about this new technology, too. Contact us to speak to one of our product experts about a customized solution today.

Linda Baran

Linda Baran is in charge of the people side of PositiveVision. Linda’s background includes working in a variety of industries including investment, manufacturing, and information technology.