AI is changing how modern manufacturers navigate disruptions, innovate and make mission-critical decisions. Three-fourths of manufacturers are already investing in AI to improve performance and productivity and 70 percent of organizations across industries now use or plan to use AI to help manage risk – up from 62 percent the previous year.
Processes that used to be slow, manual, and siloed are becoming faster, intelligent, and integrated across the factory floor and global supply chains.
Manufacturers are operating in a highly volatile environment. Nearly three-quarters of risk leaders say political volatility is impacting their business, and almost half anticipate severe supply chain disruptions under prolonged trade tensions.
As the risks these companies face evolve and pressures mount, many are rethinking their approaches to risk management and are turning to AI to help them be more proactive and protect the business.
Pressures Are Rising, Budgets Aren't
Risk management and resilience teams in manufacturing are navigating trade uncertainty, supplier instability, tightening regulations, workforce safety demands, cybersecurity risks, and adverse weather and climate-related disruptions, all while facing expectations to guide strategy and maintain operational continuity. At the same time, risk management budgets are not keeping pace.
Only 28 percent of risk leaders report an increase in technology budgets – a number that has remained flat for three years. Nearly two-thirds say budgets haven’t changed at all.
With risk teams stretched thin, manufacturers are turning to AI as a force-multiplier. AI instantly delivers powerful insights that show what’s most important, what’s most likely to happen, and the best course of action. Risk teams get the lift they need to proactively prepare and respond to a broad risk footprint with speed and accuracy.
AI Use Cases in Manufacturing Risk Management
Here are four ways AI can help manufacturers keep up with growing demands when budgets and resources do not:
- Risk assessments. The number one way risk leaders are using AI is to assess risks, reported by 34 percent of risk leaders. More than one-quarter – 28 percent – use AI to surface risks they hadn’t previously considered. AI can automatically identify, catalog, and prioritize potential risks based on patterns in risk data from incident reports, audits and third-party insights. The data can be in any format, including unstructured sources like email threads, incident notes, and reports, which have historically been too difficult for humans to include in the analysis.
- Scenario planning. 61 percent of risk leaders have simulated their worst-case scenario, up from 44 percent the previous year. AI can rapidly simulate multiple plausible outcomes, test strategies against different conditions, and identify and resolve vulnerabilities before disaster strikes. AI makes the simulations faster, smarter and more realistic - using real-time signals from current events, supply chains, regulations, and economic data, ultimately boosting companies’ preparedness for risk events.
- Supply chain visibility. One of the biggest challenges manufacturers face is limited visibility beyond their immediate suppliers. 45 percent of risk leaders say they can only assess and monitor their tier-1 partners, leaving deeper supply network vulnerabilities unchecked. AI-driven analytics can aggregate data from ERP systems, supplier financial reports, logistics updates, and external risk signals to provide a deeper, more complete view of risks across the extended supply chain. With earlier warnings and clearer insight, manufacturers can act before disruptions escalate into quality issues, downtime, or missed deliveries.
- Risk forecasting. Twenty-eight percent of risk leaders say they’re using AI for risk forecasting and to help understand how risks are likely to change over time. AI analyzes historical data and real-time signals to identify patterns and anticipate emerging threats, estimating the likelihood and potential impact of future risk. This predictive view helps manufacturers act earlier, prioritize resources more effectively, and make decisions with greater confidence.
The manufacturers that benefit most from AI are those that apply it intentionally. Here are four practical steps to move from experimentation to real impact:
- Start with high-impact use cases. Focus on areas where AI can clearly improve outcomes. For example, use AI-driven predictive maintenance to reduce equipment downtime or increase visibility for automated quality control in production.
- Integrate AI into existing frameworks. AI should enhance established operational processes, not exist as a separate initiative. Embed AI tools into your current risk management and manufacturing workflows so that insights seamlessly inform decision-making.
- Invest in people and platforms. Teams need training, governance, and systems to validate and scale AI use effectively. This includes upskilling staff on AI tools and investing in platforms that can handle large data volumes from manufacturing operations.
- Show impact to leadership. Connect AI insights to continuity metrics and operational performance (e.g., reduced supply chain disruptions or improved on-time delivery rates), as well as strategic goals, to build support for ongoing investment. Demonstrating tangible benefits like fewer production stoppages or faster risk response will help secure executive buy-in for AI initiatives.
High-stakes environments call for faster, smarter, and more proactive risk management. The gap between what risk teams at manufacturers face and what they can manage with existing resources is widening. AI is helping to close that gap by delivering clearer insight, stronger foresight, and faster response.
Jim Wetekamp is the CEO of Riskonnect, a leading provider of integrated risk management software.
