Practical AI to Industrialize Additive Manufacturing

AI is revolutionizing industries, and additive manufacturing is no exception. With its complexity in design, materials, and processes, AM is ripe for transformation. Yet, while startups like Backflip—recently raising $30 million—focus on AI-driven geometry and generative design, the real short-term opportunities lie elsewhere: in eliminating the “busy work” that drains engineers’ time.

Take Boeing as an example. Through our ThreadsDoc solution, we automated the creation of Technical Data Packages (TDPs) for spare parts. Each TDP previously took 120–150 hours of skilled labor. Automating this task saves Boeing thousands of hours, cleared a backlog of over 100 parts, and allowed engineers to focus on higher-value work. This is where AI delivers immediate ROI—by enhancing efficiency and gaining trust through practical, low-risk applications.

Other impactful use cases include:

• Spare Part Identification: Give frontline maintenance workers the knowledge and tools to, at an instance, identify potential additive solutions to their problems.
• Parameter Transitions: Moving certified applications from device to device often involves trial-and-error. AI narrows parameter ranges, speeding requalification.
• Automated Reporting: AI streamlines compliance documentation, saving time and improving accuracy in regulated industries.

These pragmatic applications are already reshaping workflows. Engineers and operators readily adopt them because they enhance productivity without threatening expertise.

AI’s revolutionary possibilities—like identifying defects mid-build, generating intricate designs, or even optimizing material properties—are undeniably exciting. However, these advancements hinge on AI’s ability to grasp complex material behaviors and production nuances, which vary widely across processes and conditions. They also rely on precision, which the current crop of AI is not known for and which would require a significant amount of data—the kind that remains out of reach for most organizations today—to succeed. As a result, industries such as aerospace and healthcare, where precision is paramount, remain cautious. Trust in these systems will take years, if not decades, to solidify, especially with the stringent demands of certification bodies. Many of these explicitly prohibit the use of systems that cannot be properly explained, which means a “blackbox” approach to additive is not just undesirable but untenable.

This slow pace of adoption isn’t a failure; it’s the nature of industrialization. Building trust in new processes takes time. That’s why we work hand in glove with engineers to understand their specific frustrations and eliminate them. Over time, this partnership allows us to introduce more ambitious AI capabilities in ways that align with their expectations and industry standards. By removing inefficiencies, Authentise and others pave the way for AI’s long-term potential in design, real-time optimization, and beyond.

I look forward to discussing this balance of short-term wins and future vision with Alex and Karsten at Additive Manufacturing Strategies in February. For now, let’s focus on what AI can do today: remove busy work and let engineers innovate.

Andre Wegner is founder and CEO of Authentise (www.authentise.com), a leader in flexible, AI-powered workflows in the most agile manufacturing and engineering settings.  Andre will participate in person at Additive Manufacturing Strategies, Feb 4-6 in New York City.