After the test phase, companies now need to scale up AI and reliably transfer it to operations. This is where 2026 will be the turning point. The transition from experiments to productive environments brings with it new risks, from rising costs and growing dependencies, to complexity that undermines the economic benefits. This makes it all the more important to look at the trends that are now leading the way. Four developments stand out in particular:
1. Agents as the new standard
In 2026, AI agents will evolve from augmented assistants to autonomous digital employees. They will act in a goal-oriented manner, autonomously access APIs, data sources, or internal systems and increasingly coordinate tasks in even more powerful multi-agent environments. This also makes robust standards for interoperability indispensable, above all open communication protocols such as MCP.
Open source plays a key role in ensuring the necessary flexibility and independence from individual providers, particularly in the context of agent systems. In practice, agents can be developed and operated efficiently in a modular way and based on open standards - for example with the Llama stack.
2. Operational phase
The operational phase is becoming the decisive factor: with the transition to productive use, one of the central problems regarding the cost-effectiveness of AI solutions is shifting. The challenge here is that large reasoning models deliver impressive results, but generate massive load peaks and drive up costs. While running small experiments with cloud providers was once sufficient, companies now often seek new approaches for deploying their models in production.
Hybrid cloud approaches are considered particularly promising in order to be able to implement use cases flexibly and efficiently anywhere. At the same time, an architecture of specialized models is gaining ground—smaller, compressed and domain-specific models solve specific tasks faster and much more cost-effectively. 2026 will therefore also mark a transition to systems consisting of several models that can be dynamically orchestrated according to a company's requirements, and offer maximum efficiency.
3. Real specialists
AI models are becoming real specialists: GenAI models are usually too large or too imprecise for corporate use. However, traditional fine-tuning is also increasingly proving to be too expensive and inefficient, which is why the adaptation of models will shift more to the data. Synthetic data, i.e. specifically generated training data for specific specialist domains and use cases, will become the central lever here. New processes for continuously incorporating knowledge allow additional expertise to be added without replacing existing capabilities.
This leads to smaller, more precisely tailored models.The trend is thus clearly moving away from complex model training and towards controlled, data-driven specialization and new approaches such as orthogonal subspace fine-tuning (OSF). By continuously integrating new knowledge into existing models without losing existing knowledge, this method addresses a major problem: standard fine-tuning.
4. More diverse hardware
The strong dependence on a few GPU providers is having an increasingly negative impact on many companies—for example due to high costs, long delivery times, or vendor lock-in. This means, in addition to classic GPUs, alternative platforms and accelerators are increasingly being used.
This is made possible above all by a software layer that standardizes this diversity. In the future, abstraction layers can ensure that companies can also use their models independently of hardware without the need for complex code adaptations. The result will be more flexible infrastructures that can be better adapted to costs, availability and energy consumption, as well as allow companies more technological freedom.
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