What Risk Management Measures Should Companies Take Before Adopting AI?

Artificial intelligence is gradually becoming part of the core operations of manufacturing companies. From AI-powered visual inspection, predictive maintenance, and production scheduling to demand forecasting, supply chain management, and document analysis, businesses are adopting AI to improve efficiency, reduce costs, and strengthen decision-making.

However, as AI moves beyond proof-of-concept projects and begins connecting with production, quality, equipment, and customer data, the risks associated with implementation also increase. If training data is incomplete or unreliable, models may produce inconsistent results. If access controls are inadequate, confidential information may be exposed. If companies cannot explain how AI generates its outputs, managers may struggle to determine when model recommendations can be trusted and when human intervention is required.

Before asking what AI can do, companies should therefore answer another important question: when AI makes an incorrect decision, data is leaked, or a model fails, who is responsible for identifying the issue, responding to it, and stopping the system?

The AI Risk Management Framework (AI RMF 1.0), developed by the U.S. National Institute of Standards and Technology (NIST), is a voluntary framework designed to help organizations integrate trustworthiness into the design, development, deployment, and use of AI systems. Its purpose is not to restrict innovation, but to help organizations identify, measure, and manage AI risks in a more systematic way.

For manufacturers, AI competitiveness depends on more than model accuracy. It also depends on whether the organization has effective data governance, clearly assigned responsibilities, risk monitoring, and incident response capabilities. AI should only be introduced into critical business processes when it can be understood, controlled, and continuously validated.

What Are the Most Common Risks When Companies Adopt AI?

The risks associated with AI implementation are rarely limited to a single technical issue. They usually result from the combined effects of data, models, business processes, cybersecurity, and regulatory requirements.

The first major concern is data quality. AI models depend heavily on their input data. If the data contains missing values, inconsistent formats, incorrect labels, or unrepresentative samples, a model may perform well during testing but produce biased or unreliable results in real operating environments.

For example, if a predictive maintenance model is trained primarily on data collected during normal equipment operation but lacks sufficient failure and abnormality data, it may be unable to recognize genuine warning signs. Similarly, the performance of an AI vision inspection system may decline when lighting conditions, product colors, raw material batches, or camera angles change.

The second concern is model reliability. Many companies focus mainly on accuracy during project acceptance but overlook the real business consequences of incorrect predictions. If a quality inspection model classifies a defective product as acceptable, the result may be customer complaints and returns. If a scheduling model recommends an impractical production plan, it may cause production congestion, excessive changeovers, or delivery delays.

Companies should not only ask whether a model is accurate. They also need to understand when errors are most likely to occur, how much damage different types of errors could cause, and whether the model has clearly defined operating boundaries.

The third concern involves cybersecurity and privacy. Employees may expose confidential or sensitive information by entering product drawings, customer data, process parameters, contract terms, or equipment records into external generative AI tools. AI systems may also face emerging threats such as unauthorized access, data poisoning, prompt injection, and manipulated model outputs.

The fourth concern is transparency and accountability. When AI is used for supplier evaluation, quality decisions, equipment maintenance, or workforce management, companies must define who approves its use, who monitors performance, who reviews the results, and which department responds when errors occur.

The OECD AI Principles emphasize that AI systems should remain robust, secure, and reliable throughout their lifecycle. Related risks should be continuously assessed and managed, while developers and users should assume responsibility according to their roles, context, and level of control.

The first step in AI adoption is therefore not selecting a model. It is defining the use case, data sources, potential consequences of failure, and boundaries of responsibility.

Why Is AI Governance More Important Than the Capability of a Single Model?

Many AI projects begin within a technical department or individual business unit. The team may select a model, prepare data, and build a proof of concept before discussing formal deployment. However, once AI enters a real operating environment, the long-term success of the project depends less on the model itself and more on whether the company has established effective governance.

AI governance ensures that every project has a clearly defined purpose, ownership structure, decision authority, and monitoring process. Companies need to understand why AI is being used, which decisions it may influence, what data it may access, and when humans must take control.

For example, an AI vision system may assist with product inspection, but whether it should be allowed to make final scrap decisions depends on product risk, model maturity, and quality requirements. For general cosmetic defects, manufacturers may allow a higher degree of automated decision-making. For products involving safety, legal compliance, or critical functions, human review and sampling should remain in place.

AI governance must also cover third-party providers. Many companies rely on external cloud platforms, model services, or system integrators. If contracts do not clearly define data usage, retention periods, model updates, cybersecurity responsibilities, and data handling after service termination, the company may unknowingly assume additional risks.

Before formal deployment, organizations should establish cross-functional AI governance roles. IT and cybersecurity teams should manage systems and access controls. Data owners should confirm data quality and permitted use. Business and manufacturing teams should define operational requirements. Legal and compliance teams should review obligations and responsibilities, while senior management determines the level of risk the organization is willing to accept.

AI governance is not intended to create unnecessary approval procedures. Its purpose is to align technology, operations, and accountability. Without governance, even an impressive short-term AI demonstration may be difficult to scale because its results cannot be verified, responsibilities remain unclear, or the organization is unable to respond to abnormalities.

What Management Principles Can Companies Learn from the NIST AI RMF?

The NIST AI RMF is built around four core functions: Govern, Map, Measure, and Manage. Together, they help organizations incorporate AI risk management throughout the system lifecycle, from governance and contextual analysis to risk assessment and mitigation.

These functions are not a one-time linear sequence. They form an ongoing management cycle that must be repeated and adjusted as systems, data, and operating conditions change.

Govern focuses on establishing organizational policies, roles, and responsibilities. Companies should clearly define which AI applications are acceptable, which data cannot be used, which decisions require human review, and how problems should be reported and systems suspended. Governance provides the foundation for the other three functions. Without clear accountability, identified risks may remain unresolved.

Map requires companies to understand the AI use case and its potential impact. The same model may create very different risks when applied to different business processes. An AI system used to classify internal documents should not be subject to the same verification, monitoring, and human oversight requirements as one used to determine product safety.

At this stage, companies can identify users, data sources, stakeholders, dependent systems, and possible failure scenarios. Projects can then be classified according to their potential impact. Applications involving safety, quality, compliance, or customer rights generally require stricter controls.

Measure focuses on using tests and performance indicators to understand risk. In addition to accuracy, companies should evaluate false-positive and false-negative rates, data bias, model stability, cybersecurity vulnerabilities, and performance under different operating conditions. Test data should closely reflect the real production environment and include abnormal, boundary, and low-frequency but high-impact scenarios.

For generative AI, NIST has also published the Generative AI Profile as a cross-sector companion resource to AI RMF 1.0. It helps organizations address risks such as inaccurate generated content, information security, data privacy, and other challenges specific to generative AI.

Manage involves taking action according to risk priority. Companies may choose to mitigate, transfer, accept, or avoid specific risks while establishing monitoring, incident response, and system shutdown procedures. If model performance falls below an acceptable threshold, data distributions change significantly, or an abnormality threatens safety or customer interests, automated decisions should be suspended and transferred to human reviewers.

The most important principle of the NIST AI RMF is that AI should be treated as a system requiring continuous management—not as a software feature that can be deployed once and used indefinitely. Models, data, use cases, and external conditions all change over time, and risk management must evolve accordingly.

How Can Companies Build a Trustworthy AI Management System?

Companies do not need to create a large and complex AI governance organization from the beginning. However, they should at least begin with four areas: project inventory, risk classification, validation mechanisms, and post-deployment monitoring.

First, companies should establish an inventory of AI projects and identify which departments are developing or using AI. This should include formal systems, third-party tools, and generative AI applications used independently by employees. If an organization does not know which AI systems are in use, it cannot effectively manage data, permissions, or accountability.

Next, AI applications should be classified according to their purpose and potential impact. A tool used to assist with internal copywriting should not be subject to the same review standards as a model that directly affects product release, equipment safety, or customer credit. Higher-risk applications require more complete testing, documentation, human review, and management approval.

In data management, companies should confirm whether data was lawfully obtained, whether it is representative, whether it contains sensitive information, and who is responsible for data quality. Data cleaning and labeling rules should also be documented so that model results can be traced back to how the underlying data was prepared.

Before a model is formally deployed, companies should define clear acceptance criteria. In addition to average accuracy, they should establish acceptable error ranges, conditions for human intervention, and procedures for handling system failure. For important applications, AI can initially operate in an advisory mode, allowing employees to compare model recommendations with actual decisions before gradually increasing automation.

Model performance must continue to be monitored after deployment. Changes in products, raw materials, equipment, customers, or market conditions may gradually reduce the accuracy of a previously effective model. Companies should regularly review performance, data drift, abnormal events, and user feedback while maintaining procedures for retraining, revalidation, or retirement.

The OECD's 2026 Responsible AI Due Diligence Guidance further extends AI risk management to corporate due diligence and the wider AI value chain. It emphasizes that organizations should continuously identify and address potential adverse impacts related to AI rather than performing a one-time review during initial development.

Companies should also maintain channels for human intervention and appeal. AI can support decision-making, but human judgment should not be completely removed from situations involving significant quality, safety, employee, or customer interests.

Trustworthy AI does not mean that a model will never make mistakes. It means that the organization understands when mistakes may occur and has the ability to detect, correct, and stop the system in time.

From Adopting AI to Managing AI

The purpose of adopting AI is to improve efficiency and decision quality—not to introduce new operational risks.

Without data governance, model validation, cybersecurity controls, and clear accountability, AI may amplify existing process weaknesses. Incomplete data may produce unstable models, unclear decision ownership may prevent errors from being addressed promptly, and unmonitored systems may continue generating unreliable results after operating conditions change.

The NIST AI RMF provides companies with a practical management approach: establish governance, understand the use context, measure risk, and then manage and monitor risks according to priority. Organizations should not wait until AI technology is fully mature before establishing governance. Trustworthiness, cybersecurity, legal requirements, and human responsibility should be incorporated into the project from the planning stage.

For manufacturers, valuable AI is not simply a model that performs well in a controlled testing environment. It is a system that operates reliably on the production floor and within real business processes, produces verifiable results, keeps abnormalities under control, and continues to support sound decision-making.

In the future, competition in AI will not be determined only by which companies deploy the most models first. It will also depend on which organizations build the governance capabilities required to create sustainable business value while keeping AI risks under control.