The Future Runs Predictively

Using AI to Transform Physical Industries

AI is often sold as a software story of digital apps, chatbots, and virtual assistants. But for Naresh Shanker, former CTO of Xerox and CEO of its legendary research center PARC, the real opportunity lies where atoms meet algorithms. From predictive maintenance and smart infrastructure to remote healthcare and open innovation, Shanker sees AI not only as a disruptor of business models but as a catalyst for global societal change.

"AI is going to continuously evolve," Shanker said, reflecting on the technology's widening impact across domains. "The acceleration of GenAI will start marrying this bridge between the whole human condition as well as context.”

Where Machines Predict Themselves

In asset-heavy sectors like manufacturing, energy, and logistics, AI is already paying off. Shanker pointed to predictive maintenance as a prime example: "It ensures optimal performance and extends the lifespan of a lot of this equipment, and that translates to millions and millions of dollars of capital being saved."

The model is simple but powerful: sensors embedded in physical infrastructure stream data that machine learning algorithms process in real-time. Anomalies are flagged, repairs are scheduled before breakdowns occur, and operational downtime plummets. “It’s going to be very, very powerful when you think about its application across manufacturing, transportation, logistics,” he added.

By pairing AI with IoT and edge technologies, companies can finally act on the information flowing through their systems, turning maintenance from a reactive cost center into a predictive advantage.

Smarter Cities, Smarter Systems

Shanker’s holistic AI vision also includes the urban landscape. Energy systems, water management, and transportation are often governed by static infrastructure and legacy rules. But when equipped with sensing capabilities, real-time analytics, and AI-led orchestration, these systems can become far more intelligent.

"You're going to start looking at things. For example, its applicability in the field of energy efficiency," he explained. "Smart cities, smart water, smart transportation are going to start accelerating at a global level." This transformation will be made possible by the convergence of multiple technologies. "When you start putting this together, you create more intelligent systems."

The benefit is both systemic and environmental: reduced energy consumption, lower operational costs, and more responsive infrastructure.

From Urban Centers to Remote Clinics

While much of AI’s promise lies in optimization, Shanker also emphasized its role in expanding access, especially in healthcare. In rural and underserved communities, access to quality medical services remains a critical challenge. AI, he argued, can close that gap.

"If you look at a combination of hardware and software technologies… soon there has to be the ability to provide not just remote diagnostics, but also be able to perform remote surgeries to such remote parts of the world."

He sees this not as science fiction, but as an emerging reality enabled by integrated platforms that combine AI, imaging, robotics, and telecommunications. The same technological convergence transforming factories and cities could also enable healthcare without borders.

The key is integrated intelligence. Not just smart software, but coordinated ecosystems that work across distance, domain, and device.

Innovation Without Borders

To fuel such breakthroughs, Shanker champions a model of open innovation where knowledge, tools, and talent are distributed rather than centralized. He believes AI can democratize invention by making cutting-edge capabilities accessible to more people in more places.

"Accessibility through technology is now ubiquitous," he said. "Both data, the ability to learn, to have access to talent, and to have access to ideas really goes back to the heart of what I call the Open Innovation Model."

This approach has deep roots at PARC, where Shanker led efforts to reimagine how research, industry, and entrepreneurs could collaborate. In this model, AI serves as a multiplier of human ingenuity. It enables faster iteration, more inclusive problem-solving, and a larger funnel of solutions for global-scale challenges.

Protecting What Makes You Different

With AI becoming widely available, Shanker pointed out that data strategy is now the key differentiator. "There is going to be data that's going to be what I call foundational to these models," he said. This includes open or widely available data used to train large models.

But the real edge comes from what lies above that foundation. "Then there is going to be a whole layer of specialization," he explained, "how you actually put that IP together, the implications, the security, the regulatory, the ethics around it."

Smart companies, he noted, will carefully separate and govern these layers. "All of that has got to get layered… classified, segregated, compartmentalized," he said, warning of the risks of careless implementation.

"There are several complexities that come with this. The folks that get ahead of this will understand the issues, implications, ethics, and regulatory concerns that have to be addressed upfront… before you unleash the monster."

Building for the Long View

While many AI conversations focus on immediate ROI or competitive advantage, Shanker called for a broader view. The industries poised for the most radical transformation (manufacturing, energy, healthcare, infrastructure, etc.) also carry the heaviest responsibilities.

AI, in his view, must be embedded in a layered architecture of sensing, computing, security, and human oversight. This is not plug-and-play technology; it is deeply contextual and must be designed with purpose. That’s why Shanker insists that AI must be industry-specific, responsibly governed, and deployed with both ambition and restraint.

Lessons in Holistic Intelligence

Shanker’s journey, from Xerox to PARC and beyond, has placed him at the crossroads of research and application, theory and deployment. He sees AI not as a narrow tool for productivity, but as a broad platform for transformation.

From predictive maintenance that saves millions to connected healthcare that saves lives; from open innovation that levels the playing field to layered data strategies that preserve an advantage, Shanker’s vision is clear: AI must be contextual, composable, and collaborative.

In his hands, AI becomes less about automation and more about orchestration, intelligently connecting people, machines, and systems to create a more efficient, equitable, and sustainable world.