How Industrial AI is Different and Why the Distinction Matters
Artificial intelligence (AI) has exploded into mainstream discourse as technologists, professionals and general consumers gain familiarity with generative AI assistants and chatbots. Yet as AI adoption accelerates, industrial users must understand the critical distinction between consumer and industrial-focused AI.
In a recent episode of the Future Ready Podcast, Samir Desai Senior Director for AI and Data Strategy at Siemens Digital Industries Software, helped us dive into the difference between consumer and industrial-grade AI. Desai also explored how industrial AI solutions are developed, and the different roles occupied by key players in the industrial AI landscape.
Listen to the full episode below or continue reading for a summary of our discussion.
Building AI Infrastructure vs. Developing Industrial Intelligence
The conversation opens with some context setting, specifically how various industry players fit into the overall industrial AI landscape. Hyperscalers, companies like NVIDIA, Amazon Web Services and Microsoft, offer foundational AI infrastructure in the form of large language models, retrieval augmented generation guardrails, computing power and more. These services are industry agnostic, offering the platform or foundation on which other organizations can build solutions to purpose.
Desai clarified that Siemens operates differently in the space. Siemens is a leading industrial software provider, building tools that support engineering, design, multi-physics simulation, manufacturing design and operations, product lifecycle management and more. These tools are designed for companies that build complex physical systems: cars, airplanes, satellites, industrial machinery and consumer electronics, among others.
The task for industrial software companies is to use the AI infrastructure built by hyperscalers to infuse AI into industrial software systems to improve efficiency and speed. Critically, these industrial AI applications must understand the complexities and nuances of physical systems, design constraints, manufacturing constraints and other factors of designing complex physical systems.
Moreover, when Siemens infuses AI into this software, it is not simply layering a general-purpose model on top of existing tools. It is embedding intelligence that understands the context of an engineer designing a battery cell, a simulation specialist validating structural integrity, or a process planner optimizing a factory floor.
Deploying Industrial AI Across Domains
Desai offered a concrete example to help illustrate the full scope of industrial software and the integration of industrial AI across such a broad portfolio. The example centers around the development of an electric vehicle, touching on many domains within Siemens’ software portfolio.
The process begins with design: engineers use 3D modeling, electrical and electronic systems development, and other tools to define the vehicle’s geometry, route electrical cabling and set electronic component layout. From there, simulation specialists test and validate performance characteristics, including thermal performance, aerodynamics, structural rigidity and electrical loads, under real-world conditions.
Once the design is validated, manufacturing engineers take over, planning the production processes, tooling, and factory workflows required to bring the vehicle to physical reality. After the vehicle reaches market, service technicians rely on structured documentation and diagnostic tools to maintain and repair it throughout its operational life.
Each of these phases involves distinct user personas, distinct workflows, and distinct data requirements. Desai explained how a cross-portfolio AI approach can address the unique needs of these personas, workflows and data structures. Rather than developing AI capabilities in isolation for individual products, the program takes a holistic view, considering where AI can deliver meaningful efficiency gains across the entire lifecycle, such as by accelerating decision-making, surfacing better recommendations or automating time-consuming tasks.
Improving the range of an electric vehicle battery, for instance, is a multi-step problem. It requires design modifications, simulation validation, manufacturing feasibility analysis, and integration checks. Each stage is performed by different teams using different tools but working toward the same outcome. An AI-enabled workflow can surface design recommendations, accelerate simulation cycles, flag manufacturability concerns and connect insights across these phases in ways that were previously manual, slow and siloed.
Why Holistic Industrial AI Strategies will Win
Ultimately, such a strategy can set companies up for more flexibility and adaptability in a future of rapid evolution in foundational AI services and capabilities. Organizations that adopt a holistic, cross-domain strategy that connects technology, customer needs and strong guardrails at the program level can best deliver industrial AI capabilities that are robust, reliable and powerful.
Check out the Future Ready Podcast to learn more about Industrial AI, the Digital Twin, Connected Automation and more key technologies transforming the future of industrial technology.
Siemens Digital Industries Software helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform. Siemens’ software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today’s ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software – Accelerating transformation.
