Coherent’s AI Strategy: 15% Waste Cut by 2028

In 2023, Dr. Alistair Finch, who runs manufacturing ops at Coherent’s Santa Clara facility, had a huge problem on his hands. Demand was spiking for their high-power industrial lasers, the kind used in everything from EV battery plants to medical devices. But a stubborn 7% defect rate on the optical fabrication line was burning through expensive materials and killing production time. The problem went beyond simple costs. It was chipping away at Coherent’s reputation as a top-tier supplier of precision optical solutions and putting their entire Coherent digital strategy for AI market leadership at risk.

Dr. Finch knew that traditional quality control, with human operators squinting at parts, had hit a wall. The defects were often microscopic and the production volume was just too high, making manual checks slow and full of errors. He needed a completely different approach, something that could find tiny imperfections with machine-level accuracy and speed before they turned into scrapped parts. His vision put artificial intelligence at the very core of their next growth phase, not just as some add-on tool.

The first real roadblock was data. Coherent was sitting on terabytes of historical production info, optical scans, thermal profiles, material reports, but it was a complete mess, scattered across different systems. “We had a goldmine of information, but no map to navigate it,” Finch commented at a 2024 industry panel. So, phase one of their AI push was all about building a solid data infrastructure. This meant deploying a centralized data lake architecture, using Google Cloud’s BigQuery, to pull in and standardize all the data from their manufacturing stages. A 2025 investor brief noted this foundational project took a cross-functional team of data engineers and optical scientists nearly six months. This kind of prep work gets skipped all the time in the rush to get to the “cool” AI stuff, but it was the essential groundwork for the machine learning models that would later change their whole operation.

With the data finally in one place, Coherent brought in a specialized AI firm to build custom computer vision models. The objective was to train an AI to spot micro-fractures, impurities, and coating flaws on optical surfaces that a person could easily miss. The first models, which they piloted on one line in early 2024, looked promising but generated a ton of false positives. “The AI was like a very enthusiastic but sometimes overzealous intern,” Dr. Finch joked. “It flagged everything from dust motes to genuine defects.” This just shows how critical continuous refinement is. Coherent’s engineers had to work hand-in-glove with the AI developers, feeding them thousands of labeled images and constant feedback to get the algorithms right. This back-and-forth cycle of deploying, evaluating, and retraining is how these projects succeed in the real world.

By the end of 2024, the AI-powered optical inspection system was running on two of their main production lines. High-resolution cameras with custom lighting snapped pictures of every single component on the conveyor belt. Those images were processed instantly by AI models running on edge devices right there on the line. If the AI spotted an anomaly, the system automatically flagged the part and shunted it aside for a human to double-check. It augmented the human operators, it didn’t replace them. It let them stop doing the repetitive, eye-straining checks and focus on more complex problem-solving instead.

The results came fast. Within three months of going live, the defect rate on the AI-integrated lines dropped by 3%. By the middle of 2025, that number had hit a 15% reduction across all lines using the system. That translated into real money saved on wasted materials and rework. It also boosted their throughput and made output quality far more consistent which really strengthened Coherent’s standing in a tough market. “The AI wasn’t just finding defects. It was helping us understand the root causes,” Finch explained. The detailed data from the AI gave their engineers insights into upstream process variations, letting them make adjustments *before* problems occurred. This is the real payoff of AI: moving from reactive fixes to predictive control.

With defect detection working, Coherent’s digital strategy next tackled predictive maintenance for its complex manufacturing gear. Any unplanned downtime on a high-volume line costs a fortune. They started feeding sensor data from their laser cutters, polishing systems, and cleanrooms, things like vibrations, temperature swings, and power consumption, into AI models designed to predict equipment failures before they happen. For example, a tiny change in vibration frequency on a polishing machine could signal bearing wear long before an operator would ever notice a performance dip.

The predictive maintenance system was up and running by early 2026 and proved its worth almost immediately. Internal reports from Coherent showed a 20% drop in unscheduled downtime for their most important equipment in just the first quarter. Maintenance teams could now schedule repairs during planned shutdowns, causing minimal disruption. This proactive work saves money, extends the life of expensive machinery, and tightens their grip on their AI market leadership.

A softer, but still significant, benefit of Coherent’s AI adoption was the effect on employee satisfaction. The AI systems took over the most boring, tiring inspection tasks. This freed up skilled technicians to do more analytical work and actual problem-solving. Supported by internal training on the new AI tools and data analysis, the workforce became more engaged. The common fear is that AI just replaces jobs, but in Coherent’s case, it evolved them, creating new career paths inside the company.

Coherent’s digital strategy is still pushing forward. The company is now exploring AI for supply chain optimization, using predictive analytics to better forecast demand swings and potential disruptions for a more resilient procurement process. They’re also looking at AI-driven design, where algorithms can simulate and refine optical component designs for specific performance goals which could dramatically speed up the R&D cycle. Weaving AI into every part of the business, from the factory floor to the design lab, is how they’re building an enterprise that can actually keep up with the pace of change.

Of course, the whole project had its challenges. Data privacy and security were a constant worry, especially with all the proprietary manufacturing processes and client specs involved. To get ahead of this, they created a dedicated internal AI ethics committee in 2025 to oversee responsible AI deployment and ensure fairness and transparency. Getting this stuff right demands a thoughtful, principled approach, especially with new regulations like the EU’s AI Act starting to come online globally.

Dr. Finch often thinks back to the initial skepticism. “Some thought it was too complex, too expensive, or just another passing fad,” he shared recently. “But the numbers speak for themselves.” This represents a fundamental shift in how they create value. Coherent’s story shows how an AI-centered digital strategy, when executed well, can produce major operational gains and lock in a decisive competitive advantage in the global market for optical solutions.

The lesson from Coherent’s experience is pretty direct: a successful AI adoption needs a strong data foundation, iterative development with continuous expert oversight, and a clear vision for what business problem it’s supposed to solve. It’s not about doing AI for the sake of AI. It’s about using its power to get tangible, measurable results and to maintain a strong position in the market.