AI Illuminates the Future of Photonics

Amit Shah doesn’t speak about AI as a buzzword, but as a strategic shift that’s fundamentally changing how companies operate, innovate, and deliver customer value. For the Executive Vice President and Chief Information Officer at Excelitas Technologies, artificial intelligence isn’t just a tool for incremental improvement; it’s reshaping product design, operational productivity, and cybersecurity while redefining what’s possible in a historically hardware-centric industry.

Shah shared how Excelitas, a global leader in photonics and optoelectronic technologies, has applied AI in both analytical and generative forms to create real outcomes. He also offered clear perspectives on where enterprise AI stands today, how companies should prepare for the next wave of productivity gains, and what opportunities lie ahead in AI-enabled cybersecurity and software development.

Photonics and AI

Excelitas effectively blends physical products with intelligent systems. Shah explained that the company operates in the photonics space, designing and manufacturing technologies that generate, detect, and manipulate light across the spectrum, from UV and IR sources to optics, imaging, and sensors. These technologies power critical systems across aerospace, defense, medical, industrial, and consumer markets.

Shah observed that although hype around generative AI is high, deep value comes from integrating it with foundational AI techniques already in use: “A lot of the hype recently is around Gen AI … but I take a broader view when I use the word AI … even tools like RPA are AI, and we’ve been using that for four years.”

In other words, AI at Excelitas is not confined to experimental chatbots or flashy demos. It includes long-standing analytical AI applications like machine learning for optical inspection systems and automation through robotic process automation (RPA).

AI Across Products and Operations

Excelitas applies AI in two primary classes: analytical AI and generative AI. Each of these delivers distinct forms of impact.

Analytical AI focuses on well-defined problems with structured data. Shah described how Excelitas uses machine learning in its hardware applications, including machine learning in night vision goggles (NVGs). Future models will include algorithms that help soldiers more accurately distinguish objects in low-light conditions, turning raw sensor data into higher-confidence insights in mission-critical environments. Machine vision helps quality inspectors identify defects more reliably and efficiently than manual inspection alone.

Shah explained that for analytical AI, clearly defined problems and strong data inputs translate directly to measurable outcomes: “The majority of our focus and realized value has happened in what I call analytical AI.” These implementations reduce manual effort, improve consistency, and enable capabilities that were previously impractical at scale.

Generative AI Productivity Transformation

Excelitas is also embracing generative AI, particularly to enhance enterprise productivity across internal teams and develop new capabilities for developers and business users. Evaluating enterprise copilot tools from major platforms to accelerate productivity among business analysts, HR, finance, marketing, and IT professionals. Exploring tools to improve developer productivity, enabling code augmentation and faster iteration.

Shah emphasized that getting data foundations right remains essential before deriving high-quality AI outputs, especially with generative AI: “There’s a golden principle in the IT world: garbage in, garbage out … if you feed garbage to that technology, it’s going to spit out garbage.”

He argued that thoughtful data cleanup and process streamlining must precede wide-scale generative AI adoption if organizations want reliable results rather than noise or hallucinations.

The Next Decade of AI

Shah sees three major ways AI will reshape enterprise technology over the next five to ten years. He framed AI’s long-term impact as analogous to the spread of the internet in the mid-1990s, a shift that ultimately became so ubiquitous that people use it without thinking about it: “You will be using AI without you knowing you are actually using AI … embedded into … data structure … helping you execute business processes faster.”

In this future, generative AI will be embedded into core workflows: cleaning data, improving document distribution, and optimizing business processes from back-office accounting to supply chain planning.

Shah was clear that AI is now a requirement in cybersecurity, not an optional enhancement:

“The bad guys are going to and already are using AI … I want my cybersecurity vendors to use AI to reduce the complexity in the fragmented nature of how cybersecurity is done today.” He emphasized that traditional tools like SIEMs can’t keep pace with modern threats, and that AI must be used to detect anomalies and prevent intrusions in real time rather than reacting after the fact.

Shah predicts AI will transform software development itself: “Programming is going to be totally different … AI will revolutionize how code is created, tested, and deployed.” This shift will shorten development cycles and improve innovation velocity across all software components, including those embedded in physical products.

Organizational Imperatives

Shah highlighted several strategic imperatives for leaders preparing for AI’s broader adoption. Excelitas is working to reduce its data fragmentation, consolidating CRM, ERP, and cybersecurity platforms, because unified data is the foundation for strong AI performance.

Leaders must enable teams to experiment with AI while ensuring compliance with privacy and security regulations across geographies, not least because Excelitas operates in highly regulated defense and industrial sectors.

When asked how companies should measure CIO success, Shah offered three metrics: top line and bottom line value creation, cybersecurity outcomes, and employee enablement and productivity gains. This framework links technology leadership directly to business impact: a measure that resonates far beyond IT departments.

Lessons Learned

Across the conversation, Amit Shah shared several strategic lessons for enterprise leaders embracing AI. AI adoption must start with data excellence. Without clean, harmonized data, AI outputs are unreliable. Analytical AI is already creating measurable value. Practical machine learning and RPA deployments are the backbone of current success.

Generative AI will reshape productivity but requires groundwork. Leaders should invest in data and process readiness now for exponential gains later. Cybersecurity must leverage AI to stay ahead of threats. Reactive defenses are no longer sufficient in a world where attackers use AI too. AI will transform how software is built and consumed. Innovation cycles will accelerate as AI becomes part of every development toolchain.

AI as the Fabric of Modern Technology

Shah’s vision reveals that AI is not merely a set of isolated projects but the fabric of how modern technology teams will create outcomes in the coming decade. From enhancing photonic product capabilities and automating quality assurance to driving cybersecurity outcomes and improving organizational productivity, AI is at the core of Excelitas’s strategy.

As Shah put it, AI will become so foundational and embedded that “you will be using AI without you knowing you are actually using AI.” For enterprise leaders, that realization, and preparation for it, is the competitive imperative of our time.