When I discuss physical AI with clients, one of the clear messages I hear is that they understand its benefits and potential. Another is that they want to embrace it at speed, with a sense of urgency that is well justified.
NTT DATA’s 2026 Global AI Report: A Playbook for AI Leaders defines AI leaders as organizations with more robust AI strategies, greater AI maturity and higher AI-driven profits than their peers. The report finds that AI leaders are far more likely than laggards to say they want to move fast and lead the market: 46.1% versus 25.4%.
But speed alone isn’t enough. Our report also shows that AI leaders are more than twice as likely as laggards to rebuild core applications with embedded AI: 34.5% versus 14.5%.
Simplifying and modernizing your organization’s operational core must be one of the first steps in a physical AI strategy. Even when you move with urgency, it shouldn’t be at the expense of the core.
Lay the foundation for AI speed and modernization
Start by simplifying and modernizing the processes, applications, data and technology that keep your organization running. Strengthening the foundation makes it possible to deploy physical AI through humanoid robots, autonomous vehicles, drones, cameras and intelligent machines.
What that looks like in action depends on your industry:
On a manufacturing line, it could be an exception reaching an operator before a defect moves downstream.
In the automotive industry, it might mean AI coordinating cameras, tools, machines and inspection stations so production teams can respond to changing production conditions on the factory floor.
For retail and consumer packaged goods (CPG) organizations, AI may guide decisions about shelf execution, product availability and promotion performance in ways that are traceable, secure and governed.
Whatever industry you’re in, modernization now has a higher bar: Making your organization’s operational core ready for intelligence that can that can turn observation into operational response.
ALSO READ: Physical AI: Moving from cloud-centric AI to intelligence where work happens
Manufacturing: Quality moves upstream
Even with highly sophisticated automation in place, manufacturers may discover quality issues on the factory floor only after it’s too late. Once a part leaves the station, your organization has lost the opportunity for an easy correction.
NTT DATA co-developed a physical AI solution with Hyster-Yale Materials Handling that shows one of the key ways physical AI can help mitigate quality issues. The solution uses vision sensors to see the assembly work as it happens and edge AI to analyze the activity on-site. It compares physical actions such as installing parts, completing steps and following sequences against the expected production process, while the process is in motion.
As a result, if a part is missing or a step is incomplete, the system can flag the issue while it can still be corrected at the station, where the cost of correction is lower. In fact, physical AI technologies such as computer vision and edge AI become more valuable as quality moves upstream, where teams can catch issues before they become downstream defects or reliability problems.
ALSO READ: Beyond the pilot trap: How to build a manufacturing AI strategy that scales
Automotive: AI needs orchestration on the factory floor
Physical AI works best when models are connected to real workflows, real equipment and real decisions on the factory floor. Signals from cameras, sensors, tools and inspection stations need to be orchestrated to detect quality issues, route exceptions and guide the next action automatically, without disrupting production.
So, when you have dozens of these models operating close to the activities on your factory floor, it presents an orchestration challenge as much as a deployment challenge.
NTT DATA partnered with an automaker facing just that challenge. Our client had 80 AI models in use across their factory operations, with many of the models running in the cloud. Bringing all that intelligence out of the cloud and onto the factory floor would enable their teams to make decisions closer to production. But first, they needed trustworthy data about which of the models were working, whether they were producing reliable results and how they should be managed as part of day-to-day operations.
As automotive operations grow more connected, software-defined and data-intensive, AI has to be orchestrated, monitored and governed throughout the production environment. Otherwise, intelligence will remain fragmented. Individual models may still generate valuable insights in pockets, but those insights become harder to trust and act on at scale.
CPG and retail: The shelf becomes a signal
For retail and CPG companies, physical AI delivers value by connecting intelligence to the real-world conditions of stores, shelves and field execution.
Commercial plans often fail in the gap between what your team planned and what actually happened on the shelf. You might fund a promotion only to learn that the product you’re promoting has become unavailable. Your team may plan a display but not execute it. Or you might find that an assortment decision looks right in a system but breaks down in the store.
Intelligent retail execution can help your field teams keep plans on track. By combining product availability signals, route planning and performance data with cameras, AI-powered image recognition and shelf-scanning robots for inventory management, they can see clearly which stores need attention and what actions matter most.
Make modernization a reality in your organization
Ultimately, physical AI gives modernization a tangible outcome. It helps you connect AI intelligence to the physical work of making products, moving goods and serving customers. In markets where speed, trust and resilience increasingly determine performance, that kind of operational core becomes a competitive advantage.
NTT DATA has been named a Market Shaper in the Gartner® Emerging Market Quadrant for Physical AI Services – Established Vendors, which assesses 27 providers within the emerging physical AI services market.*
We have the platforms, the partnerships and the expertise to put AI to work on the factory floor. Get in touch and let us show you how.
This article was co-authored by Alonso Fernandez, Global Co-Lead: Product Industries at NTT DATA.
WHAT TO DO NEXT
Read more about NTT DATA’s approach to modernizing the core to see how we can help you build an AI-ready foundation for an intelligent, connected and resilient enterprise.
Gartner. Emerging Market Quadrant for Physical AI Services — Established Vendors, Katie Gove et al. 8 June 2026. Gartner® does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose. GARTNER is a trademark of Gartner, Inc. and/or its affiliates. Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.