GenAI as an operating system: The AI layer over existing enterprise IT

GenAI has long since moved beyond the proof-of-concept stage in many companies. Yet there is often a huge gap between a functioning pilot and a scalable part of the corporate IT infrastructure. What is needed to bridge this gap is not another use case – but a fundamentally different architectural perspective.

From pilot to platform: Why the second step is so difficult

The pattern is familiar: a team develops a compelling GenAI use case. The technology works, the results are promising, the stakeholders are enthusiastic. And then? Then the truly difficult phase begins.

Because what was still manageable in the controlled pilot environment suddenly becomes complex when scaled up. Access to corporate data must be regulated. Existing systems – ERP, CRM, data platforms – need to be integrated. Compliance requirements relating to the GDPR and the EU AI Act must be met. And all of this simultaneously for a use case that was originally built for a demo.

In practice, this often means that use cases work technically but never make the leap into production. Not because of the AI – but because of a lack of the underlying foundations. What companies need in this situation is not yet another standalone solution. What they need is a platform.

The concept: GenAI as a horizontal infrastructure layer

A GenAI operating layer is not software you can buy. It is an architectural principle – the idea of not building GenAI capabilities in isolation and on a project-by-project basis, but rather providing them as a shared, reusable infrastructure layer.

The image that best describes this concept is that of an operating system. Just as an operating system makes memory, the processor and the network accessible to all applications without each app having to manage these resources itself, a GenAI operating layer provides the basic AI functions – for all teams, all use cases, and all systems within the organisation.

Technically, such a layer typically consists of five core components:

• Orchestration layer: Control of LLM calls, prompt management and workflow coordination
• Data hub: Controlled integration of existing sources such as SAP, data platforms or document systems – for example via RAG architectures
• Governance component: Centralised management of access rights, audit trails and compliance requirements in accordance with the EU AI Act and GDPR
• Agentic Runtime: Execution environment for autonomous AI agents capable of processing tasks across multiple systems
• Integration API: Standardised interfaces enabling business units to develop their own applications – without having to rebuild the infrastructure

The principle behind this is: Centralised governance, decentralised innovation. Business units retain their autonomy. The platform ensures that no one experiments at the expense of security or compatibility.

What this means for CIOs and IT decision-makers

A new phenomenon is currently emerging in many companies: Shadow AI. GenAI solutions are developing outside central IT structures – teams are building their own applications, using different models and entering into individual cloud contracts. What provides speed in the short term leads to structural problems in the medium term: a lack of transparency, unresolved compliance issues and virtually no reusability.

A GenAI operating layer is the structural solution to this problem. It does not create centralised control that stifles innovation – but rather a common foundation on which innovation becomes faster and more secure. Those who invest in this foundation today will be able to deploy new use cases tomorrow in a fraction of the time previously required.

For CIOs, this is a strategic decision: whoever builds the operating layer sets the rules of the game for GenAI within their own organisation. Those who wait until the pressure becomes great enough are building on a disordered foundation.

Agentic AI: The next evolutionary step on the operating layer

Whilst many companies are still working on building their first GenAI layer, the next stage is already a reality: Agentic AI. AI agents no longer merely respond to prompts, but plan independently, execute subtasks and orchestrate multiple systems in the process.

This fundamentally changes the requirements for the underlying infrastructure. An agent that accesses SAP data, coordinates approvals and writes results to a dashboard no longer operates in isolation, but is deeply integrated into existing system landscapes. This requires a controlled environment with clear permissions, full traceability and defined escalation paths. This is precisely where the GenAI operating layer becomes a crucial prerequisite.

Agentic AI is already beginning to shape production systems. Companies are already deploying agents today – for instance, for reporting automation, incident management or complex approval processes. What these companies have in common is that they established the necessary infrastructural foundations at an early stage.

adesso supports companies in building precisely this foundation – from architectural design through to the implementation of agentic AI systems.

Three steps to the GenAI operating layer

Building a GenAI operating layer is not a project that you simply plan once and then implement. It is developed iteratively. This is also necessary because it must fit the specific IT landscape. At the same time, many companies face a fundamental strategic decision. They will not build their GenAI operating layer entirely in-house, but will procure parts of it. The real question is therefore not just about architecture, but about dependency.

Architecture before use cases

The most important decisions are not made during the next use case, but before it. Which data sources will be connected? Which cloud infrastructure will be used? Which LLM orchestration framework fits the existing technology landscape? And above all: which parts of the value chain remain within the company’s own sphere of influence and which are handed over to platform providers? These questions determine whether GenAI can be scaled later or whether new dependencies arise.

Governance from the outset

Security and compliance requirements must be integrated into the architecture from the very beginning. Not retrospectively. This is particularly true in light of the EU AI Act, which sets clear requirements for transparency, documentation and human oversight. At the same time, a key question of ownership arises: Who is responsible for the operating layer? A purely centralised IT control structure can slow down innovation and isolate business units. An overly decentralised structure quickly leads back to shadow AI. Those who do not actively shape this balance do not solve the problem, but merely postpone it.

Think of the platform as a product

A GenAI operating layer is not a traditional IT infrastructure. It is an internal product that is continuously being developed. This requires clear ownership, defined interfaces and a roadmap. Companies must make a conscious decision about who is responsible for this product and how closely IT and specialist departments collaborate. Only in this way can a platform be created that is both controllable and open to innovation. Companies that consistently take this step create a foundation on which new use cases emerge in weeks rather than months.

The GenAI portfolio from adesso supports companies on this journey. From the strategic clarification of key architecture and ownership issues to the scaled operation of agent-based systems.

Conclusion

In most companies, GenAI is no longer a question of ‘if’. The crucial question is: how can this be turned into a scalable, secure and competitive capability? A GenAI operating layer provides the answer. It combines governance and innovation, bridges the gap between pilot and platform, and lays the groundwork for the next evolutionary step: autonomous AI agents that drive real business processes. Those who invest in this foundation today secure a lead that will be difficult to catch up with tomorrow.