What are experts saying on what the next wave of industrial software looks like in the next 2-5 years?

The next wave of industrial software will be defined by AI-driven penetration into legacy physical sectors, fundamentally altering both the companies building the software and the industries consuming it . Experts predict a significant migration of software engineering talent away from pure-play tech and toward traditional industrial giants like John Deere and Caterpillar, which are now leveraging AI to automate their core businesses . This shift necessitates a new organizational model that fuses the agile culture of Silicon Valley with the deep domain expertise of industrial veterans, a combination seen as a critical success factor for companies tackling complex physical-world problems [5, 19]. This trend is gaining traction as software becomes essential for the competitiveness of Western manufacturers, with firms successfully selling complex systems into historically impenetrable sectors like automotive . This new paradigm positions software not merely as a tool but as the core operating system for revitalizing capital-intensive industries, filling a void left by a decades-long "brain drain" from industrial control software to internet technologies [16, 28].

Functionally, this new industrial software will move beyond simple augmentation to become the primary operator of complex processes. The next revolution in operational software is the development of **"prediction machines"** capable of forward-simulating the state of assets and supply chains over days and weeks . This predictive capability enables a more profound transformation: the complete replacement of outsourced vertical services with AI . Over the next decade, a significant shift is expected from traditional business process outsourcing (BPO) to outsourcing processes directly to AI systems [6, 10]. This represents a step-change from software that helps humans do their jobs to software that autonomously performs the job itself, creating opportunities for massive productivity gains by re-engineering workflows from the ground up .

The creation and interaction model for this software is also poised for a radical overhaul, driven by AI agents and natural language interfaces. The traditional development model of product teams anticipating user needs is expected to be fundamentally changed by AI agents within the next 10 years [7, 12]. Experts predict that AI will generate **95% of all new code within five years**, shifting the role of human engineers from writing code to reviewing it or operating AI-powered, code-generating systems [23, 25, 30]. This will democratize software creation, allowing users to build applications through natural language prompts and even modify them directly on the product interface without seeing the underlying code [4, 26]. As the cost of creating software approaches zero, the principles of software engineering are expected to permeate all other business domains, enabling companies to generate niche applications on-demand internally rather than purchasing standardized products [13, 24].

The structure of the software market is expected to evolve in a pattern reminiscent of previous shifts like cloud and SaaS, where incumbents get bigger while numerous new billion-dollar companies emerge in previously unpredictable categories . However, there is a tension in predictions about the form these new software products will take. One view is that the future will be dominated by a multitude of specialized, domain-expert agents . In contrast, another perspective suggests the industry will shift back toward more general-purpose, malleable tools akin to 1990s applications like FileMaker Pro, but delivered as a service [9, 21, 27]. This suggests a potential bifurcation between highly verticalized AI solutions and flexible, user-configured platforms. Concurrently, the lines between infrastructure and applications are blurring, with foundational model providers also offering direct-to-user applications, creating powerful flywheels that challenge traditional business strategies .