At the 2025 MIT MIMO Symposium, Arch Systems’ Co-Founder and CTO, Tim Burke, led an informative panel that shed light on the real-world impact of generative AI in manufacturing.
The discussion, “From Pilot to Production: Generative AI in the Modern Enterprise,” brought together senior leaders from Boeing, BAE Systems, Flex, and Provisur Technologies to explore how this emerging class of tools is transforming operations across the factory floor.
What stood out wasn’t just the technology but the strategic, targeted ways these organizations are scaling generative AI to solve high-value problems in production, quality, and workforce continuity.
Generative AI in Manufacturing: Real-World Applications with Measurable Outcomes
Each panelist shared a distinct use case demonstrating how generative AI in manufacturing is moving from concept to capability:
- AI-Powered Root Cause Analysis at BAE Systems
Ranah Dalbah, Senior Director of AI & Data Governance at BAE Systems, described using generative AI to mine years of unstructured quality data. By building a chatbot interface on top of a vectorized knowledge base, engineers can now uncover root causes in minutes instead of days.
The result: a soft ROI of $3–4 million from just a $4,000 pilot—an impressive demonstration of what focused AI deployment can achieve in a manufacturing environment.
2. Preserving Tribal Knowledge at Provisur Technologies
Provisur’s Sid Srivastava highlighted a common challenge across manufacturing: retiring experts and undocumented best practices. His team uses generative AI to identify and reconcile discrepancies between written procedures and real-world machine builds, preserving critical institutional knowledge before it disappears.
“We’re using AI to figure out what we should be measuring, why it matters, and whether our data is even trustworthy.
3. Smoother Shift Handoffs at Flex
John Lane, VP of Application Solutions & Implementations at Flex, shared how AI is helping reduce productivity loss during shift changes. By summarizing operational data and surfacing key events, generative AI enables smoother handovers and faster ramp-up time on the line.
“It’s not about mistakes—it’s about removing friction. Generative AI helps us deliver the right information at the right moment.”
4. Engineering Efficiency at Boeing
Dan Braley, formerly a Technical Fellow at Boeing, described using generative AI to streamline the creation of technical data packages for spare parts, especially in high-pressure scenarios where aircrafts are grounded. Automating the first 70–80% of engineering work has resulted in major time savings and reduced manual effort.
“You’re not replacing engineers—you’re freeing them to work on higher-value challenges. That’s the opportunity with generative AI in manufacturing.”
Scaling AI in Manufacturing: 5 Critical Takeaways
1. Solve Specific Problems First
Across all organizations, generative AI was introduced to tackle defined pain points such as inefficiencies, knowledge gaps, or process delays. Successful teams didn’t begin with an AI project; they began with a business need.
2. Build Governance from Day One
BAE Systems created clear usage policies and AI training programs before deployment. Flex set up AI councils to evaluate use cases and manage risk. Guardrails like these help organizations scale responsibly while empowering teams to experiment.
3. Keep Experts in the Loop
Every panelist underscored the importance of human oversight. While generative AI speeds up analysis and drafting, experts remain essential for validation, particularly in high-compliance sectors like aerospace and defense.
4. Design for Reuse
Solutions that work in one department or factory can often be adapted elsewhere with minimal changes. Both BAE Systems and Flex are using this “template model” to accelerate time-to-value across their organizations.
5. Generative AI Isn’t Just for Big Companies
Even small teams are seeing an impact. Braley noted that his current work with manufacturing startups is benefitting from the same technologies. “One person with a focused mandate and the right AI tools can deliver enterprise-grade results,” he explained.
Why Generative AI in Manufacturing Matters Now
The shift toward generative AI in manufacturing isn’t about novelty; it’s about necessity. With experienced workers retiring, data volumes expanding, and production environments becoming more complex, manufacturers are turning to AI not just to automate tasks, but to elevate how decisions are made across the enterprise.
What’s clear from industry leaders is that success doesn’t come from applying the latest technology. It comes from aligning that technology with real problems, validating it with human expertise, and designing with scale in mind.
As manufacturers continue to navigate this transition, the most effective approaches will be those that combine technical innovation with operational clarity. Generative AI has the potential to reshape how factories run, but only when deployed with purpose, structure, and a clear understanding of what’s at stake.