AI Is Making Software Development Faster. Industrial Software Still Requires Engineering Expertise.

AI Is Transforming Software Development

A few weeks ago, I asked a generative AI tool to build an online version of a Japanese card game my father used to own. About ten minutes later, a working web application was ready.

The result was both impressive and eye-opening. A few prompts generated functional code, built the user interface, and produced an application that worked. Only a short time ago, creating the same application would have required significantly more engineering effort.

Experiences like this demonstrate how profoundly AI is changing software development. Developers can now generate code, build applications, automate workflows, and accelerate development in ways that were unimaginable only a few years ago. Increasingly, AI is moving beyond simple code generation into agentic workflows that can propose changes, run tests, and automate larger portions of the engineering process.

As Product Management Director for GENESIS at Mitsubishi Electric Iconics Digital Solutions, my thoughts immediately shifted from a simple web application to industrial automation software. If AI could build a functioning application in ten minutes, what happens when the same capabilities are applied to human-machine interface (HMI), supervisory control and data acquisition (SCADA), visualization, reporting, and other industrial applications?

Answering that question revealed an important distinction. AI is making software development dramatically faster. Industrial software introduces engineering challenges that extend far beyond generating an application.

Yes, AI is transforming how industrial software is built, but engineering expertise remains essential to ensuring industrial software performs reliably throughout its operational life.

Industrial Software Requires More Than Working Code

Anyone with access to today’s AI tools can generate a working application in minutes. Building a website, dashboard, or simple visualization no longer requires extensive programming experience.

Industrial software requires much more than functional code.

Think about a manufacturing facility already using an industrial connectivity platform such as Kepware to collect operational data from the plant floor. AI could quickly generate a web application, dashboard, report, or configuration workflow to display that data. The dashboard works, the information is visible, and the application demonstrates the concept.

Yet operational success depends on much more than a successful demonstration. Industrial software must integrate with existing automation systems, perform reliably under real-world conditions, and continue supporting mission-critical operations long after deployment.

Generating the first version of an application is becoming increasingly straightforward. Delivering industrial software that organizations can deploy with confidence remains a fundamentally different engineering challenge, one that requires expertise in industrial architectures, cybersecurity, reliability, validation, observability, and long-term operational performance. 

Subject Matter Expertise Makes the Difference

Experienced engineers understand the operational requirements behind industrial applications, the questions that must be asked, and the risks that must be addressed before software is ready for deployment.

Cybersecurity vulnerabilities, software components, industrial architectures, regulatory considerations, and long-term maintainability all become part of the engineering process. AI-generated applications may also introduce dependencies, APIs, credentials, data flows, or configuration assumptions that must be reviewed before deployment. Working code demonstrates what an application can do. Engineering expertise determines whether organizations can deploy that application with confidence. 

Build Industrial Applications on Proven Platforms

Engineering expertise is essential, but experienced engineers rarely build industrial software from scratch. Modern industrial applications are built on proven software platforms that have been developed, tested, and refined through years of real-world deployment.

AI can quickly generate a web application on top of an industrial connectivity platform, dramatically reducing development time. That application, however, represents only one layer of the solution.

This principle remains true even as AI moves deeper into industrial automation. Purpose-built engineering agents can now generate programmable logic controller (PLC) logic, HMI screens, and other industrial application components, while major software vendors are embedding generative AI directly into industrial platforms. 

These capabilities are impressive, but they reinforce an important question: who validates the output, who owns the lifecycle, and who is accountable when software controls a real industrial process? Faster generation is not the same as software that organizations can deploy with confidence and support for years to come.

Proven industrial software platforms already provide capabilities such as validated architectures, redundancy, cybersecurity protections, and lifecycle services, allowing engineers to focus on solving industrial problems rather than rebuilding proven capabilities. 

Proven software platforms also provide established security models, update paths, governance, and long-term support that ad hoc generated applications rarely have from the outset. 

Lifecycle Management Extends Beyond Deployment

Deploying industrial software marks the beginning of the engineering process, not the end. Consider a plant manager requesting an operational report. AI generates the report in minutes, the application works as expected, and everyone is pleased with the result. Then the plant manager says, “I want that report every day.”

A one-time report has now become operational software that people depend on to do their jobs. Software platforms evolve, operational requirements change, and new cybersecurity vulnerabilities emerge. Ongoing engineering becomes essential to validate updates, maintain compatibility, apply security patches, and ensure reliable operation over time.

Building the first version of an application may now take only minutes. Maintaining that application throughout its operational life remains an ongoing engineering responsibility. Long-term reliability depends on governance, lifecycle management, and continuous support, not simply generating code.

Why Choosing the Right Industrial Software Matters

Experienced industrial software companies provide far more than application functionality. Engineering expertise, product validation, cybersecurity knowledge, ongoing software updates, and long-term lifecycle support all contribute to software that organizations can depend on in mission-critical environments.

Organizations evaluating industrial software should look beyond demos and today’s functionality. Long-term operational success depends on asking the right questions before software is deployed. Who will maintain, secure, validate, and continue developing the software throughout its operational life? The decision is not only about feature velocity but whether the software can be secured, governed, updated, audited, and supported over time. 

Final Thoughts

Building an online version of my father’s Japanese card game in ten minutes changed my expectations about software development. AI is dramatically reducing the time required to create software, and those capabilities will continue reshaping industrial automation.

Speed alone does not determine whether industrial software is ready for mission-critical operations. Organizations should look beyond how quickly software can be created and consider who is building it, how it is engineered, and who will support it throughout its operational life.

In industrial automation, those are the questions that determine whether software becomes a useful demonstration or a dependable operational system.