AI Trends 2026: From Hype to Productive AI in the Workplace
Many organisations are already using AI in individual departments – for example, in the form of copilots, chatbots, research tools or automation functions. However, scaling up remains a challenge: whilst many companies use AI, significantly fewer are already achieving measurable results at a corporate level. The trend for 2026 is therefore: moving away from isolated individual initiatives towards industrial-scale operational capability.
Industrialisation means developing, integrating and operating AI in such a way that it is reliable, secure, scalable and economically viable. Just as DevOps transformed traditional software development, new operational disciplines are emerging for this purpose: MLOps for machine learning systems and LLMOps for applications based on large language models. They lay the foundations not only for deploying AI, but also for managing and further developing it in the long term.
AI agents: How intelligent assistants automate business processes
In 2026, generative AI will increasingly evolve from assistive tools into systems capable of taking action. AI agents can retrieve information, prepare tasks, support decision-making and coordinate multiple work steps across system boundaries. This shifts the central question: it is no longer simply whether AI can generate text, code or analyses, but how it can be integrated into real-world processes in a controlled manner.
- AI agents require clearly defined scopes of work, roles and decision-making limits.
- They must be able to access reliable data sources, rather than working in isolation with incomplete context.
- Business processes, interfaces and authorisations determine whether agent-based AI will be productive or risky.
This is particularly relevant in areas such as IT service management, customer service, knowledge management, procurement, financial processes and public administration. In these areas, AI can support repetitive tasks – provided it does not make uncontrolled decisions and processes sensitive data only within clear boundaries. It is therefore crucial for organisations not to view AI agents as a technical gimmick, but as a targeted means of streamlining processes: where do they reduce turnaround times? Where do they improve service quality? And at which points must specialist departments remain involved?
Integrating generative AI: Why AI must become part of the enterprise architecture
A common mistake is to view AI as an isolated solution. In practice, however, benefits only arise when AI is embedded within existing processes, data environments and IT landscapes. Whether it’s ERP, CRM, MES, DMS or ITSM: AI must operate where operational decisions are prepared, information is processed and work steps are carried out.
This brings API strategies, data platforms, integration architectures, and identity and access management more firmly into focus. Organisations with established system landscapes, in particular, must clarify which data AI is permitted to use, which systems are to be connected, and how results are fed back into workflows in a traceable manner. An AI strategy is therefore always also an integration strategy: it determines whether applications are interoperable in day-to-day operations or remain isolated, siloed solutions.
AI governance and the EU AI Act: compliance as the foundation for scalable AI
The EU AI Act makes transparency, risk management, traceability and AI expertise more binding. Initial requirements are already in force, with further transparency obligations coming into effect from August 2026. For many organisations, this is a wake-up call: AI governance must not be left until a system goes live.
- Which AI systems are being used, either officially or unofficially?
- What risks arise for data, processes, customers, citizens or staff?
- Who is responsible for operations, quality, security and technical approval?
- When is human oversight required?
Good governance does not slow down AI. It creates the framework within which organisations can scale up more boldly. Clarifying responsibilities, documentation, monitoring and approval processes at an early stage reduces future risks and brings successful use cases into routine operation more quickly. For businesses and public authorities, this means that governance is not merely a compliance issue, but a catalyst for productive AI.
Data quality, AI security and digital sovereignty as success factors
AI systems are only as robust as the data, contexts and platforms on which they operate. For businesses and public authorities, data quality, access policies, protection requirements and operational models therefore become strategic issues. Particularly when it comes to sensitive information, a high-performance model alone is not enough. What matters is where data is processed, who has access to it and how results are verified.
- Clean data pipelines and well-maintained knowledge sources form the basis for reliable AI results.
- Authorisations, role models and logging must be taken into account from the outset.
- Cloud, hybrid and on-premises approaches should be selected to suit compliance, latency, cost and sovereignty requirements.
The industrialisation of AI is therefore closely linked to digital sovereignty. Organisations must retain their ability to act in technological, organisational and regulatory terms. Data protection, security and sovereignty should therefore not only be assessed at the end of an AI project, but should already shape the architecture, platform selection and operating model.
Reducing AI costs: Why cost control determines scalability
As the use of generative AI becomes more widespread, operating costs also rise. Model invocations, data storage, monitoring and security measures can quickly become significant factors. Industrial AI therefore requires active cost management – not just after roll-out, but right from the architecture and use-case selection stages.
- Not every use case requires the largest or most expensive model.
- Retrieval-augmented generation can help to make existing knowledge usable in a targeted manner.
- Domain-specific models can reduce costs and improve quality when used appropriately.
- Monitoring should provide transparency not only on quality and security, but also on usage, runtime and costs.
The focus is thus shifting from a mere comparison of models to a more strategic question: which combination of model, data, process integration and operation delivers the best sustainable benefit? For organisations, this means evaluating AI use cases not only in terms of their degree of innovation, but also in terms of expected usage, operating costs, model strategy and measurable added value. Often, the best choice is not the most powerful model, but the solution that strikes a sensible balance between quality, security, performance and costs.
Developing an AI roadmap: from individual use cases to a robust strategy
Anyone wishing to industrialise AI needs a roadmap rather than a collection of individual experiments. Use cases should be systematically assessed – in terms of business value, technical feasibility, data availability, regulatory risk, integration effort and operational model. The initial step does not need to be a major undertaking, but it should remain strategically scalable. A sensible approach combines pilot projects that can be utilised quickly with a long-term target architecture. This creates a portfolio that enables rapid success whilst simultaneously building sustainable AI capabilities.
Successfully implementing AI: What businesses and public authorities should be doing now
A clear course of action is emerging for 2026: organisations should no longer merely experiment with AI, but integrate it into their operations in a structured manner. This includes an up-to-date AI inventory, clear governance rules, prioritised use cases, robust data and integration architectures, and an operational model for MLOps and LLMOps. For potential Materna clients, the key question is which next step will deliver the greatest benefit: a use-case assessment, a governance structure, a data and platform analysis, or the transition of an existing pilot project into live operation. External IT service providers can help to bring together business requirements, technological implementation, security and compliance.
The key trend is this: AI is coming of age. Anyone wishing to use it successfully in future must treat it as a business-critical capability – integrated, controlled, measurable and operated responsibly. This is where the opportunity lies: organisations can deploy AI specifically where it improves processes, makes knowledge actionable, accelerates services and reduces the workload on staff.
