Discussion around artificial intelligence tends to focus solely on efficiency, speed, and automation. In an industrial setting, however, the conversation is inevitably different, as software is an integral part of a physical product.
When software controls machines, production lines and automated systems, the impact of potential errors extends directly to product safety, cybersecurity, product quality and sustainability. In industrial environments, safety-related risks are business‑critical, as they can lead to personal injury, production downtime, environmental hazards, as well as significant financial losses and reputational damage. Because industrial products are often long‑lived and subject to strict regulation, safety is not a one‑off requirement but a responsibility that spans the entire product lifecycle.
Artificial intelligence is no longer merely a tool for developers, but an integral part of the industrial operating environment. In the age of AI, industrial software development requires more than isolated solutions. It calls for controlled, holistic thinking.
Artificial intelligence is not a standalone component
The adoption of artificial intelligence is changing how production is controlled, risks are managed, and quality is assured. Responsible and effective use requires AI to be embedded within the overall architecture, safety practices, and software governance throughout the entire lifecycle.
Successful adoption of artificial intelligence in industrial software development is not a single technical decision. It is a combination of people’s expertise and trust, shared operating models and ground rules, and clear business objectives. Only when these elements come together does AI become part of a controlled software development value chain, rather than an external accelerator operating outside it.
The hidden risks of acceleration
When artificial intelligence is integrated into production, its impact extends beyond technology to organisational operating models, responsibilities, and risk management, including cybersecurity. AI is reshaping the nature of cybersecurity by opening up new attack surfaces, automating phishing, and accelerating the evolution of threats. At the same time, it offers new opportunities for threat detection, anomaly analysis, and system monitoring at a scale that exceeds human capacity.
Cybersecurity is an essential part of the responsible adoption of artificial intelligence and the governance of the entire software development lifecycle. Secure use of AI requires proactive operating models, clear responsibilities, continuous auditing and monitoring, as well as people’s expertise and trust.
Competitive advantage is built on control, not speed
Development speed cannot increase at the expense of quality. Every change must remain traceable, testable, and justifiable even years down the line. Code, tests, and documentation generated by AI must adhere to the same principles and meet the same requirements as those produced by humans.
When AI becomes part of software development, there is a risk that adoption accelerates faster than the organisation’s ability to lead and govern it.
Code is generated, tests are automated and documentation is produced faster than ever before. In doing so, strategic focus is at risk of being lost.
Based on our experience, effective governance of AI development requires understanding progress from four perspectives: the individual, the organisation, the business and digital development.
- The individual: How does each person find their role and more impactful ways of working?
- The organisation: How can core processes and operations be enhanced through the use of AI?
- The business: How does AI help create new value for customers?
- Digital development: How can digital development harness future waves of AI in a self‑directed and sustainable way?
Fully realising the potential of artificial intelligence requires high‑quality data, as AI solutions are only as reliable and valuable as the data that underpins them. In long‑lived and safety‑critical systems, a strong data foundation ensures that AI‑driven solutions remain trustworthy throughout the entire product lifecycle.
The product lifecycle is where success or failure with AI is determined
One of the defining differences of industrial software development compared to other industries lies in the length and complexity of the product lifecycle. Systems are often built for years or even decades of use, as part of long‑lived physical equipment.
The digitalised product lifecycle refers, in practice, to the journey from the initial product idea all the way to end‑of‑life and decommissioning. At its core lies the product’s digital twin, which not only represents the real machine as a 3D visualisation, but also behaves like the physical device itself. It incorporates the machine’s physical models — such as masses, structural strength and flexibility — alongside the software and data required for its operation. The digitalised lifecycle alone has already been shown to enable development times that are up to 75% shorter (Aberdeen Strategy Research, 2022). Artificial intelligence will accelerate this even further. (You can explore the benefits of digital lifecycle management in more detail here.)
The digitalised product lifecycle alone already enables development times that are up to 75% shorter, and artificial intelligence will accelerate this even further.
A study commissioned by Gofore in 2025 on the use of artificial intelligence in the manufacturing industry shows that while the majority of players see AI as a source of competitive advantage, only a few have succeeded in turning ideas into practical solutions that genuinely create value. Concrete use cases that could be implemented in the near term remain unclear, which slows down investment decisions and makes it difficult to get the implementation journey properly underway.
Without a lifecycle‑driven approach, AI quickly turns into technical debt, where the cost of maintenance consumes the very benefits it was meant to deliver.
The real challenge, then, is not the first working version, but what happens afterwards. How are solutions maintained and updated? How does governance and decision‑making remain in human hands? How does the impact of AI on the product stay transparent, and how do teams continue to follow shared architectural, quality, and operating principles?
The solution: AI Beyond Tomorrow — AI as a competitive advantage in industrial software development
For industrial market leaders, the question is no longer whether to adopt artificial intelligence, but how to do it right. The AI Beyond Tomorrow transition model provides a structure for integrating AI into software development in a way that:
- supports safety and quality
- takes into account long lifecycles, documentation and regulatory requirements
- strengthens both technical and business competitiveness
When artificial intelligence is integrated in a controlled manner at the core of software development, it becomes a sustainable advantage rather than an unmanaged risk.
Here you can explore the AI Beyond Tomorrow model in more detail:
