“2025 was a year in which AI spread rapidly and became noticeably more mature,” says Konstantin Leitz, Vice President Business Innovation at INFORM. “In 2026, this pace will accelerate even further. Few other technology fields are currently experiencing such rapid innovation,” he adds. From autonomous AI agents and AI-native software architectures to customisable models and new governance standards, AI will become an integral part of operational value creation across many industrial organisations. As a result, the focus is shifting towards the systematic integration of AI into software, processes, and infrastructure.

Trend 1: AI agents increasingly take over operational tasks

In 2026, AI agents will play a far greater role in daily operations. They will monitor workflows in real time, assess deviations, and prepare decisions when conditions change unexpectedly. For example, when transport times fluctuate or material flows slow down. As individual agents become more widespread, new forms of interaction will emerge: many organisations will deploy multiple specialised agents working in parallel on different subtasks. A production planning agent, for instance, might detect early on that a required material will not arrive on time and automatically initiate coordination with logistics to explore alternative sourcing options and maintain smooth operations.

Trend 2: AI becomes the foundation of modern software architectures

As large language models continue to mature, software development is undergoing a fundamental shift. Applications will no longer merely integrate AI as an add-on; instead, they will be designed from the outset with learning, data processing, and decision logic as core architectural elements. At the same time, the development process itself is changing: AI-generated code segments, automated testing, and adaptive deployment pipelines will become standard practice in 2026. Skills such as crafting precise prompts, testing and validating AI-generated outputs, designing complex system architectures as well as maintaining established systems, where contextual knowledge may exceed the model’s training, will become increasingly important.

Trend 3: AI systems become more specialised and modular

As organisations expand their use of AI, demand for specialised intelligence grows. Rather than relying on universal models for all use cases, domain-specific systems trained with industry- and expert-level data are becoming more prevalent. They deliver higher accuracy and efficiency, particularly in environments where processes are tightly interconnected or where seamless transitions between planning, control, and execution are essential. In parallel, modular architectural concepts are gaining ground, enabling specialised models and components to be combined flexibly. For example, capacity forecasting models can be linked with models that prioritise time-critical tasks or assess current resource availability.

Trend 4: Transparency and compliance become essential for AI systems

With the increasing complexity of AI-driven applications, transparency is becoming a key quality criterion. “AI observability” refers to the ability to monitor the behaviour, performance, and decision logic of AI components in real time. In 2026, dedicated platforms and frameworks will emerge that make training, inference, and security fully traceable. At the same time, key European regulatory frameworks, including the AI Act, the NIS2 Directive, and the Cyber Resilience Act will reach new implementation stages. Organisations will be required to demonstrate that their AI systems are operated safely, transparently, and in full compliance with these regulations.

Trend 5: New roles and competencies will shape how organisations work with AI

In 2026, the human-AI interaction within organisations will come into sharper focus. New responsibilities will emerge at the interfaces between business domains, data, and AI applications, while existing job profiles evolve as dispatchers, planners, or shift supervisors increasingly work with AI-based recommendations and assistance tools. Skills development and structured change management will become more important, ensuring that AI systems can be used confidently, securely, and reliably in day-to-day operations.

“Above all, 2026 will be a year in which organisations significantly enhance their practical maturity in working with AI,” Leitz concludes. “Many developments are moving closer to the operational core, revealing where AI truly makes a difference. Organisations that begin gaining experience early and adapt their processes step by step will benefit the most.”