06-08-2025
Building Resilience: Leveraging AI For Smarter Manufacturing
Russ Bukowski, President of Mastercam.
Manufacturers are facing increasing pressure from geopolitical disruptions, trade conflicts and economic uncertainty.
According to a 2025 report by Fictiv, a digital manufacturing and supply chain company, 96% of surveyed manufacturing and supply chain leaders indicated concerns about U.S. trade policies. Moreover, 91% of leaders are taking global friction into account when forming their long-term supply chain planning strategies, a five-point increase from 2024.
Amid these challenges, many forward-looking companies are embracing digital transformation and using artificial intelligence (AI), automation and data-driven decision making to build more nimble, resilient operations.
I've seen the potential impact of applying AI to manufacturing. My company recently introduced AI-powered Help and Command features to improve accessibility for newer computer-aided manufacturing (CAM) users in an effort to free programmers to focus on the most complex challenges. These streamline toolpath creation, reduce clicks and shorten learning curves, especially for newer users navigating powerful but complex toolsets.
On a broader level, AI can deliver real-time insights for faster decision making, support a wide range of skill levels and automate knowledge distribution—ultimately helping organizations stay adaptive.
From Legacy Systems To Labor Gaps
As digital tools, data analytics and automation redefine production environments, many companies find themselves constrained by outdated systems. Manual processes and legacy training models are no longer sufficient.
Many manufacturers are juggling multiple software platforms that generate tons of data. But if those systems don't talk to each other, that's a big problem. A fragmented setup requires users to manually transfer data, which wastes time, increases costs and leads to errors.
From design to quality inspection and every step in between, data should flow seamlessly, allowing users to see the full picture without having to search for information across various platforms.
Compounding the challenges facing manufacturing is the combination of a widening skills gap and an aging workforce. Manufacturers are still dealing with a major talent shortage. In fact, a study by The Manufacturing Institute and Deloitte found that the industry's talent gap "could result in 2.1 million unfilled jobs by 2030." As experienced professionals in the industry retire, they take decades of expertise with them.
To address these challenges, manufacturers must prioritize knowledge transfer, invest in upskilling programs and embrace tech-forward systems. Additionally, manufacturers need to shift toward a fully integrated digital setup to optimize data workflows.
Adapting To Customization And Supply Chain Disruptions
AI enhances intelligent manufacturing by maximizing toolpaths for faster cycle times and reduced waste. This helps address one emerging trend that I've observed: Production volume sizes are decreasing as demand for customized parts increases. Unlike traditional mass production, where it's relatively straightforward to produce millions of identical units, producing thousands of slightly different parts is a challenging task.
Machine learning algorithms can optimize production schedules and quickly adapt to design changes with minimal downtime. AI-driven quality control systems can also learn to detect subtle variations across different custom parts.
Manufacturers must also be able to respond quickly to disruptions amid rising geopolitical risks and increasing supply chain volatility. AI models can forecast demand patterns, detect supply chain risks and help adjust inventory levels.
The Case For Industry-Specific AI Tools
When searching for AI solutions, manufacturers should seek those tailored to their specific industries. There are two key reasons why.
For one, from my observations, general-purpose tools can deliver inaccurate results, which can have serious consequences, especially in industries such as aerospace and automotive, where inaccuracy can compromise safety.
Additionally, industry-specific AI tools arguably have a faster learning curve for workers. When an AI solution is able to understand intent and provide suggestions in the context of a certain industry, workers can adopt and integrate the technology more swiftly into their existing workflows.
Laying The Foundation With Clean Data
AI is a critical enabler of operational flexibility, but it must be paired with strategic planning and a company culture that is ready to support change. Data quality is paramount. Clean, well-structured data is essential for reliable AI outcomes.
The key to success is not trying to solve everything at once. Instead, manufacturing leaders should identify priority problems, refine their data strategies and build use cases gradually, ensuring each is rooted in a clear objective. Preparing models takes time, so patience is essential.
Starting small allows teams to test, learn and measure what matters most to an organization. Using AI wisely also means investing in people and processes rather than just technology. Empowering workers with training and new skills to collaborate with AI strengthens the organization. Leaders should find a 'champion' to help drive adoption.
Breaking down data silos and enabling platform integration enables AI tools to work optimally across the entire value chain.
AI Is An Enabler, Not A Catch-All Solution
As manufacturers face mounting pressure from trade tensions and geopolitical disruptions, they are rethinking traditional operational models. AI and automation are vital tools in this digital transformation, enabling greater efficiency, deeper insights and faster responsiveness to change.
While AI offers powerful capabilities, it is not a one-size-fits-all solution. Its impact depends on thoughtful, strategic implementation aligned with specific business needs. AI is not a replacement for sound decision making, but rather a powerful enabler that can help manufacturers build more resilient, adaptive operations.
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