Technological developments in 2026 clearly show that companies are aligning their data, quality, and documentation processes more strategically than ever before. Artificial intelligence and sovereign cloud infrastructures form the core of modern digital infrastructures. This opens up new opportunities for organizations in industrial and plant engineering: they use specialized models, automated platforms, and resilient security architectures to sustainably strengthen efficiency, compliance, and transparency.
Multi-agent systems
Multi-agent systems (MAS) connect AI agents that jointly achieve complex goals. They are developed and deployed in a single environment or independently of each other. The benefit: Multi-agent systems offer companies a practical way to automate extensive business processes, train teams, and create new ways of collaboration between humans and AI agents. Modular, specialized agents increase efficiency, accelerate deployment, and reduce risk by reusing proven solutions in different workflows. This approach also facilitates the scaling of operations and rapid adjustment to changing requirements.
Digital sovereignty
Cloud sovereignty is now a concern for a large number of organizations in light of increasing global instability. For industrial and manufacturing companies in particular, the decision to opt for sovereign IT infrastructures is becoming a strategic competitive advantage. European cloud providers are committed to the strictest data protection and IT security standards, comprehensive certifications, and consistent data storage in Europe. Transparent access control, AI-supported documentation processes without the disclosure of sensitive content, and flexible adaptability via no-code/low-code not only ensure the confidentiality of company knowledge, but also the ability to innovate. Digital sovereignty is an essential component of data security, compliance, and sustainable business success in the European industrial environment.
Domain-specific language models (DSLMs)
Generic large language models (LLMs) provide broad knowledge, but often reach their limits when it comes to complicated technical tasks. Domain-specific language models (DSLMs) close this gap with higher accuracy, lower costs, and better compliance. Unlike ChatGPT or Claude, for example, DSLMs are trained or fine-tuned using context-specific data for a particular industry, function, or process. Unlike general-purpose models, DSLMs offer higher accuracy, reliability, and compliance for defined business requirements.
For organizations, this means: A document management system provides the ideal foundation for training domain-specific models that are precisely tailored to company-specific requirements. AI-supported pattern recognition and automated analyses accelerate the creation of technical documentation, increase the reliability of quality controls, and ensure a more intelligent response from plant processes.
AI-native platforms
Companies are increasingly integrating AI applications into dedicated system landscapes. These enable specialist departments to automate processes independently or develop new applications while taking central security and compliance guidelines into account. AI-native platforms increase flexibility, efficiency, and transparency in processes such as transmittal management, review and approval workflows, and audit management.
Physical AI
AI is leaving the screen and now controls robots, drones, and intelligent equipment – machines that perceive, decide, and act.1 This results in measurable improvements in industries where automation, adaptability, and safety and security are priorities.
For quality management and technical documentation, this means: Companies are integrating operational, testing, and inspection data directly into their documentation processes. Teams are optimizing maintenance, quality assurance, and compliance more quickly and proactively as a result.
Preventive cybersecurity and digital provenance
According to Gartner, proactive security will take center stage in 2026. Organizations will increasingly rely on preventive measures to detect cyber risks at an early stage. This implies using AI-supported security operations, programmatic defense, and deception to act before attackers strike.
At the same time, the origin of data and documents (“digital provenance”) is becoming increasingly important as companies rely more and more on third-party software, open source code, and AI-generated content. Digital provenance refers to the ability to verify the origin, ownership, and integrity of software, data, media, and processes. New tools such as software bills of materials (SBoMs), attestation databases, and digital watermarks offer companies precisely this capability: they track and validate digital assets throughout the entire supply chain.
This is particularly relevant for organizations with complex supply chains or high regulatory requirements. They document every change to data and documents in an audit-proof and traceable manner.
Conclusion
The top technology trends for 2026 show: In order to remain successful in the long term, it is important for companies to realign their digital foundations. Multi-agent systems, domain-specific AI, and sovereign cloud infrastructures create a digital environment in which quality, security, and efficiency converge. Those who integrate these developments early will strengthen their data sovereignty, accelerate decision-making processes, and increase operational resilience – especially in complex industrial environments.
