IT Trends 2026: Artificial Intelligence for Efficiency and Security

1. AI-Native Development: AI becomes part of the architecture

Software development is undergoing a fundamental transformation. AI-native development means that AI is not merely used as a support tool, but is an integral part of architecture, design and operations right from the start.

Instead of isolated AI functions, systems are emerging that:

• Support AI-based architectural and design decisions
• Automate testing, reviews and documentation
• utilise continuous feedback loops for models and applications

In the public sector in particular, projects are demonstrating how AI-assisted development tools are reducing the workload for developers. Routine tasks are automated, whilst teams can focus more on business logic, quality and security.

2. Specialised models and multi-agent systems

Large, generic language models are reaching their limits in many use cases. Consequently, domain-specific (large) language models will gain in importance by 2026. These specialised, often finely tuned models deliver more precise results for industry-specific tasks.

At the same time, multi-agent systems are becoming established. Several AI agents take on clearly defined roles and work together in a coordinated manner – for example, for analysis, decision support or process automation. A key issue here is agent orchestration: rules, escalation mechanisms and responsibilities must be clearly defined.

In the energy sector and in administrative processes, significant efficiency gains are already being realised through this modular approach – for example, in AI projects such as those implemented by Materna in the field of artificial intelligence.

3. AI-supported service and administrative processes

AI is transforming the interaction between humans and systems. Assistive functions in specialist applications, intelligent search and classification mechanisms, and virtual assistants enhance service quality and processing speed.

At the same time, the importance of ‘human-in-the-loop’ concepts is growing. In regulated environments in particular, humans remain the final decision-makers.

4. Data strategy and governance as the foundation

Without reliable data and clear governance, AI remains ineffective. By 2026, governance will extend far beyond traditional data management. It will encompass:

• Data quality and classification
• Training and reference data
• Models, prompts and agent logic
• Versioning, documentation and auditability

The term ‘digital provenance’ describes the traceability and integrity of digital assets. A robust governance structure is essential for scalability, compliance and the sustainable use of AI.

5. Security, Trust and Regulation

As AI is deployed in production, security requirements are also increasing. Alongside traditional IT security issues, new risks are coming to the fore: model manipulation, data leakage, shadow AI and uncontrolled use of prompts.

Modern AI security approaches are therefore increasingly seen as part of a holistic AI trust and risk management framework. For many organisations – particularly in the public sector – the EU AI Act provides the binding framework for trustworthy and legally compliant AI applications.

6. Digitalisation and increased efficiency through AI infrastructure

High-performance infrastructure remains a key factor. AI supercomputing platforms, specialised hardware and confidential computing make it possible to operate even data- and computation-intensive AI applications securely.

Energy data rooms and interoperable management platforms demonstrate how AI-powered analytics can sustainably improve efficiency, stability and flexibility.

Conclusion: 2026 is the year of AI industrialisation

The IT trends for 2026 make it clear: artificial intelligence is evolving from a building block of innovation into a strategic foundation of modern IT landscapes. Successful organisations are those that do not view AI in isolation, but consistently integrate it into core processes whilst considering governance, security and compliance from the outset.

Those who move from the pilot phase to structured scaling now will lay the foundations for sustainable efficiency gains, better services and future-proof digital business models.