Automation 2025: What really happens when AI reaches the control level
Why automation is not disappearing – but redefining itself.
Artificial intelligence has dominated attention in 2025 – in strategy papers, in board decisions and in technical discussions. However, behind the visible developments, another topic has become the decisive factor for the productive use of AI: the ability to reliably translate decisions and analyses into operational processes. It is not the AI model, not the amount of data, but the execution layer that determines whether AI generates added value. This execution layer is automation – and this is precisely where understanding has changed fundamentally in the course of 2025.
The widespread assumption that AI will replace automation has finally been proven wrong in 2025. Practice has shown: Automation is not becoming less important, but more central – because it no longer just means increasing efficiency, but is a prerequisite for AI becoming usable and scalable in the first place. According to McKinsey, 78% of companies are now using at least one AI application productively, but more than half of them cite a lack of automation as a key barrier to scaling (McKinsey, State of AI 2024). The bottleneck is therefore not the ability to develop AI, but the ability to execute it in a stable, repeatable and comprehensible manner.
Why AI does not replace automation, but requires it
The shift can be seen above all in the question. For a long time, the question was: “Which processes can we automate?” In 2025, it became: “Which processes are integrated in such a way that they can be triggered and monitored by people, systems and AI alike?” While automation used to be associated with IT batch processes, scripts or RPA bots, in 2025 it increasingly became an orchestration layer between applications, data, events and decision-making systems. Automation has not disappeared, it has become more visible and strategic.
From tools to architecture: what has changed in 2025
In terms of technology, automation in 2025 has clearly shifted from time- or script-driven processes to event-, API- and data-driven workflows. Decisions are no longer made periodically, but in real time. Data is no longer stored in individual systems, but flows through hybrid landscapes. Processes no longer run linearly, but react to events, APIs, sensors or user signals. Under these conditions, in 2025 it was no longer enough to have “an automation tool” – the decisive factor was whether the underlying architecture was open, scalable and compatible with modern operating models.
Another driver was the increasing fragmentation of IT landscapes. According to Gartner, companies used an average of 47 SaaS applications in 2024, in addition to cloud, on-prem and legacy systems (Gartner, Application Architecture Report 2024). In 2025, it became clear that automation cannot function within a single system in such environments, but must create interoperability. AI can make recommendations, but without a connecting execution logic, it remains an isolated analysis tool. Only when processes are automated, monitored and controlled do operational benefits arise.
The risk of scaling without control
The use of AI in 2025 not only increased the speed of execution, but also the risk. Processes that used to be corrected manually could suddenly go wrong thousands of times automatically. According to a survey by the Federal Reserve Bank of Richmond, 60% of companies introduced new automation approaches last year, but only 16% of them invested in monitoring or observability mechanisms at the same time (Richmond Fed, CFO Survey 2024). This means that the execution layer is scaling faster than the control layer – and this is precisely what became a critical factor in IT and operational architectures in 2025.
The new role of automation in AI operations
2025 has also shown that automation is no longer just a means of reducing costs, but of reducing risk, ensuring compliance and accelerating innovation. The global market for intelligent process automation is therefore expected to grow from USD 14.5 billion in 2024 to USD 44.7 billion in 2030 (Grand View Research, 2024). Growth here is not generated by the replacement of human labor, but by the transition from static to dynamic process control.
What companies really need to decide in 2026
After a year in which the introduction of AI and the reality of automation have increasingly clashed, the central question for 2026 is shifting: Not “Do we need automation?” but “What form of automation will enable AI maturity, scale and governance?” A scheduler or bot framework can automate isolated processes, but cannot control a coherent process landscape. An AI application can make decisions, but without automated execution, there is no business impact. Only the interplay of data, AI logic and automation creates operational value.
Conclusion: Automation is not disappearing – it is becoming more visible
2025 has shown: Automation has changed its role – from a background process to the visible foundation of digital operating models. Anyone talking about AI maturity must talk about automation maturity. If you want to scale, you need automatable, observable and reliable processes. If you want speed, you need control logic, not just models. AI does not make automation superfluous – it makes it indispensable.
In 2026, the question will not be whether to automate, but how to create an architecture in which automation, data flows and AI systems are no longer separate worlds.
Sources
- McKinsey & Company – The State of AI in 2024
- Federal Reserve Bank of Richmond – CFO Survey, June 2024
- Grand View Research – Intelligent Process Automation Market Size Report, 2024-2030
- Grand View Research – Artificial Intelligence Market Size Report, 2024-2033
- Gartner – Application Architecture, SaaS & Integration Landscape Report 2024
Continuation of the series
This post is part 1 of our four-part blog series on the transformation of automation in the age of AI. In the coming weeks, we will be publishing further articles addressing the operational risks, maturity requirements and architectural decisions that companies will need to make in 2026 to become truly AI-ready.