The answers become inconsistent, processes remain fragile, and many individual experiments do not yet result in a measurable business impact. The reflex is obvious: Then we just need better models. But this is precisely where the copilot illusion begins. In many cases, the bottleneck is not the model, but the environment in which it is supposed to operate.

The Uncomfortable Truth

The problem rarely lies in the technology. Large Language Models (LLMs) are already very powerful when it comes to understanding language, generating content, recognizing patterns, and making knowledge accessible. Often, they even deliver more than we’d like—for example, when they generate plausible but incorrect answers. After all, they only reliably deliver value when context, data, processes, and operational boundaries are clearly defined.

The real problem is our expectations. We often treat AI like a conventional tool that can be easily integrated into an existing environment and used immediately. We assume that you simply install it, plug it in, and get started right away.

But just as a new employee needs onboarding to understand their role and place within processes, networks, and the organization as a whole, AI needs exactly that. Companies are a tightly interwoven structure with complex processes, often unspoken rules, and established hierarchies. AI must be embedded within this and carefully structured. Systems only function reliably when their operating conditions are clearly defined. However, this requires that the underlying environment itself be carefully structured and consistent. In practice, this is rarely the case. People learn such rules through experience, conversation, and observation. AI, on the other hand, requires explicit structures: clear data, defined processes, roles, rules, and boundaries.

Yet a company’s knowledge is scattered across many locations and data points, across documents with unstructured content, and across people. Terms are used differently depending on context, and important information is often only implicit, hidden in texts or in the minds of experienced employees. Processes are thus rarely fully documented, and decisions within workflows depend heavily on experience and situational context.

An experienced employee can navigate this complexity intuitively. An AI system cannot. Without a clear understanding of how things are connected, AI systems quickly reach their limits, and the initial illusion crumbles. Without structure, a system cannot reliably link information; it does not understand end-to-end processes and has no clear boundaries for action.

The consequences are predictable: inconsistent results, fragile workflows, and isolated standalone solutions. With every new use case, another siloed solution emerges, without a coherent big picture emerging. Technical intelligence grows, but actual business value does not. And there is yet another problem…

The platform that doesn’t (yet) exist

Many assume that the right platform for deploying new models has long since existed. They turn to Microsoft Teams, M365 Copilot, Salesforce Agentforce, or ChatGPT. These solutions address important sub-problems: they provide access to models, integrate with existing applications, and significantly lower the barrier to entry.

What they do not automatically provide, however, is a company-wide consistent operational logic in which data, processes, interfaces, responsibilities, and governance interact across system boundaries.

It is precisely this type of collaboration that is becoming increasingly attractive to companies and is often discussed under the term AI agents. Yet the successful deployment of cooperative and autonomous AI agents requires an even more robust and standardized foundation, which, as described above, simply does not exist in most companies. To understand why agents need more of precisely what most companies still lack, we must first understand what AI agents are and how they work.

What is an AI agent?

There are now many different approaches to defining them. In some, an AI agent is a prompt that defines behavior, or a workflow that orchestrates individual steps. Others describe it as a service that performs actions. In reality, an AI agent is usually a combination of all of these. Yet even a conceptual definition of an agent does not answer the question of where such an agent actually operates within a technical and structural environment. Some locate it in the backend or equate it with the workflow system. Others say it belongs embedded in the user interface. Currently, there is no uniform answer to this, not even from solution providers.

At the same time, we are receiving more and more inquiries from customers who want to integrate agent-based systems into their workflows. A common misconception is that AI agents can simply be added as an extension of existing systems. This assumption is not entirely accurate.

Can’t agents simply be integrated into Teams?

The answer to this question always depends on exactly what is to be integrated. A chat interface or a simple frontend can be connected relatively easily. A system, on the other hand, that actually intervenes in business processes is a completely different challenge and shifts the focus from pure integration to the interaction of many components. Suddenly, structured data, clearly defined processes, and interconnected tools are needed, both for agents and for users. Furthermore, the starting point changes when not just one, but several agents are to be introduced into existing business processes. We refer to this scenario as an Agentic Mesh. It goes far beyond integration and essentially describes a new “operating system” for AI in enterprises.

An Agentic Mesh describes a networked system of specialized AI agents, data sources, tools, interfaces, and governance rules that does not automate isolated tasks but coordinates work across process and system boundaries.

However, the path to this goal has not yet been fully paved. A key building block for this is Retrieval Augmented Generation (RAG).

Why Vector RAG Is Not Enough

RAG systems improve access to internal corporate information. They help identify relevant content and enable more informed answers. Yet the now widely adopted vector-based RAG systems solve only part of the problem. They do not define how information is structured, how relationships are modeled, or how decisions and actions are operationalized. Vector RAG leads to better answers, but not to a functioning knowledge or process system. In other words: Vector RAG is an important step, but not the end goal. It helps systems find relevant information. However, it does not automatically answer the questions of which information is binding, what action may follow from it, which process step comes next, and who remains responsible for it. That is why a change in perspective is needed.

The Change in Perspective

AI cannot simply be superimposed onto existing structures. What is needed is a fundamental shift in thinking:

Only under these conditions can AI do more than provide isolated support for individual questions and become an active, reliable part of real work in a corporate context.

An example from customer service illustrates this: An AI co-pilot can quickly formulate a response to a customer inquiry. However, if product data, contract logic, escalation rules, and CRM status are not properly integrated, it remains unclear whether the response is correct, permissible, and compliant with processes.

It is precisely at this point that it is determined whether AI merely provides support or becomes a reliable part of a business process.

What this means in practice

Although AI often seems more complex than expected, that doesn’t mean we’ve failed. It simply means that the underlying challenges are now becoming visible. Many organizations are at exactly this point. They recognize the potential of AI but are now encountering structural limitations. The illusion is fading. Because if the problem is structural in nature, the solution must also be structural.

The next phase of enterprise AI will not be decided by who deploys the latest model. What will be decisive is which companies structure their data, processes, responsibilities, and systems in such a way that AI can operate reliably within them.

Copilots were the starting point. Agent-based systems are the next step. But the more powerful the technology becomes, the more important the foundation beneath it becomes. The real work doesn’t start with the prompt—it starts with the company’s architecture.

About the Author

Dr. Andreas Kyek is a data science and AI expert with over 25 years of experience in data-driven product and process development. With his background in physics and his experience in leadership roles (including at Infineon), he combines technological depth with strategic implementation. As Senior Principal Data Scientist and Practice Lead at Alexander Thamm [at], he is expanding the data science and AI practice, with a focus on agent-based AI systems, multi-agent architectures, semantic knowledge models, and RAG in complex industrial setups. He leads large-scale data/AI initiatives (industry, energy, mobility, infrastructure) and is actively involved in mentoring and training for the responsible use of AI.