Blog series 3 Automation & AI

Part 3 of the blog series “Automation in the age of AI”

2026 will be the year that determines whether automation becomes the execution and orchestration layer for AI – or a bottleneck itself. Most automation systems running in companies today were not developed for data-driven, event-based or AI-supported processes, but for repeatable workflows with clearly defined parameters. AI is fundamentally changing this logic: decisions are no longer made at fixed intervals or by manual approval, but within seconds – triggered by data, events or combined signals from applications, APIs or sensors. This shifts the question from “How do we automate existing processes?” to “How do we create an architecture that can execute AI decisions in a reliable, scalable and traceable way?”

2024 and 2025 have shown that the biggest bottleneck lies not in model quality, not in the cloud strategy and not in data availability – but in the technical execution level. Where automation is seen as a tool, isolated solutions are created. Where it is understood as an architecture, scalability emerges.

Why classic workflow automation is reaching its limits

Most automation landscapes in companies have grown historically: scripts, RPA bots, batch schedules, integration bridges between systems – each introduced to solve a local bottleneck. This works as long as processes are sequential, predictable and manually correctable. But as soon as AI comes into play, the relationship between process speed, data complexity and control requirements changes.

AI-based decisions generate follow-up steps, validations, API calls, rule tests, approvals or escalations in milliseconds, not in time windows. What used to be daily processing is now a permanent stream of events. However, many automation systems do not react to events – they wait for time triggers. They do not orchestrate services – they start scripts. They do not monitor business processes – they only check status codes.

This shows that it is not a lack of AI functionality that limits the effect, but an execution layer that was built for a different operating model.

What AI-supported processes really require technically

An AI-supported process is not an automated process with “more intelligence”, but a structurally different process. It needs an architecture that reacts to events, not just schedules. An execution that can control APIs, systems and data flows – not just tasks in a system. Governance that makes decisions traceable – not just logs error messages. And monitoring that observes business processes – not server statuses.

Studies such as “Future of Digital Infrastructure 2025” by IDC and “AI at Scale” by Deloitte show that this is precisely where companies fail: Not because AI is unreliable, but because the technological basis for dynamic process control is missing. AI generates decision-making logic – but only automation generates impact. And this is precisely where the gap that needs to be closed in 2026 has emerged.

The four architectural mistakes that 2025 has made visible

2025 has clearly shown that many automation initiatives fail not because of technology, but because of architecture. Firstly: tool silos instead of orchestration. A bot for finance, a scheduler for IT, a script for data transfer – but no connecting control logic. More tools do not lead to more automation, but to more complexity.

Secondly: logic in people, not in the system. Decisions are made automatically, but their execution remains manual or incompletely linked. AI can check, prioritize and segment – but the next step is often still an email or a ticket.

Thirdly: No technical feedback. AI delivers a result, but the result leads neither to corrective process logic, nor to an improved database, nor to automated escalation. Processes run – but they don’t learn.

Fourthly, there is a lack of control and monitoring. While systems are getting faster, transparency is decreasing. According to ISACA Europe, only 31% of companies had end-to-end monitoring of their AI-supported processes in 2025. Speed increases – but controllability decreases.

From tool stack to execution layer: automation as architecture

The key shift for 2026 is therefore not “More automation”, but “Other automation”. Automation is no longer the final phase of a project, but is becoming the operational platform layer. It links events, services, decisions, data models and control mechanisms. It does not replace individual activities – it controls the flow between systems.

This also changes the role model in companies. Instead of “Who maintains the script?”, the question is: “Who owns the end-to-end process?” Roles such as Automation Architect, Execution Layer Owner or Process Reliability Engineer are emerging instead of IT Batch Owner. And instead of local optimization, a platform is created that supports business-critical processes – regardless of whether they are triggered by people, systems or AI.

What companies will have to decide in 2026

2026 will not be characterized by the question of which new AI use cases are possible, but by the question of which execution architecture can support them. Companies must decide whether automation will continue to be seen as an operational efficiency measure – or as a strategic control layer for value creation, scaling, security and compliance.

This involves setting the course in five ways: Automation must become event-driven, not time-driven. It must become API-oriented, not system-bound. It must become observable, not just executable. It must be architecturally anchored, not tool-centered. And it must be operated as a platform, not as a project.

Conclusion: architecture is the bottleneck, not the technology

AI has accelerated decision-making logic in 2025 – but automation will determine whether these decisions have an impact. Companies that talk about AI maturity in 2026 need to talk about automation maturity. Not how many processes are automated, but how automatable, controllable and extensible the execution layer is.

If AI makes decisions in milliseconds, but processes run at the old pace – how long can this contradiction be glossed over with tools before architecture becomes a business issue?

Sources

• Deloitte – AI at Scale: Enterprise Transformation Findings (2025)
• IDC – Future of Digital Infrastructure 2025
• ISACA Europe – AI Use is Outpacing Policy and Governance (2025)
• Gartner – Hype Cycle for I&O Automation 2025
• Bitkom – AI use in the German economy in 2025

Continuation of the series

This post is part 3 of our four-part blog series on the transformation of automation in the age of AI. While Part 1 described the strategic shift and Part 2 highlighted the operational scaling issues, this post shows why the next phase is only possible through changeable architecture. In Part 4, we present a maturity and decision-making model that helps companies to assess where they will really be in 2026 – and which transformation paths will be necessary.