The multi-billion dollar market is exploding: What "Agentic AI" is and why waiting is no longer an option

When AI stops asking questions and starts acting: The quiet upheaval in the world of work

The silent revolution that nobody saw coming – and that is now changing everything

Artificial intelligence is currently undergoing a radical transformation that will forever change the foundations of modern business management: away from passive chatbots and towards autonomous systems that make independent decisions and act proactively. So-called "agentic AI" is considered the next major stage of digital transformation. While tech giants like SAP and Siemens are already deeply integrating this technology into their core processes, and the market is projected to explode to almost $50 billion by 2030, a harsh reality of unexpectedly high costs and complex compliance issues is also emerging. The following article examines how autonomous AI agents are already reshaping supply chains and corporate structures behind the scenes, where the hidden risks of this hype lie, and why simply waiting for companies to react is strategically risky in light of this rapid development.

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From answer machine to decision-making authority

It is perhaps the most profound shift in the history of enterprise technology, yet it is largely unfolding behind the scenes. While public debate still revolves around chatbots and text generators, artificial intelligence in businesses worldwide has undergone a qualitative leap, redefining the very foundations of modern operations. Agentic AI—autonomous, actionable AI systems—is no longer a concept confined to research labs or science fiction scenarios. It is operational, it is scalable, and it is currently transforming the way decisions are made in supply chains, customer service, financial planning, and production.

The difference between this and what most people understand by artificial intelligence is fundamental. Classic AI models respond to requests. They answer questions, complete texts, and analyze images when prompted. Agentic AI, on the other hand, doesn't wait for a prompt. It continuously monitors data streams, recognizes patterns, derives necessary actions from them, and independently executes steps to achieve a predefined goal. It selects its own tools, validates intermediate results, adjusts its approach when conditions change, and escalates exceptions to human intervention only when absolutely necessary. Consequently, the technology analyst Gartner has identified this development as one of the defining strategic technology trends for 2026.

The jump from 5 to 40 percent: Why the market is exploding

Market data paints a clear picture. As recently as 2025, less than five percent of enterprise applications had embedded, task-specific AI agents. By the end of 2026, this figure is projected to rise to forty percent—an eightfold increase within twelve months. Customer service company Ada reported in March 2026 that it had more than doubled its revenue year-over-year, driven by exploding demand for its agentic platform, with a 108 percent growth rate in recurring AI revenue. Infrastructure provider Colt Technology Services, in collaboration with Microsoft, demonstrated in a field test how an agentic AI engine condensed the complex pricing process for enterprise customers from several days to just a few minutes, with 99 percent accuracy.

The overall market for agentic AI is projected to grow from $5.1 billion in 2024 to $47.1 billion by 2030. A study by Jitterbit, in its AI Automation Benchmark Report for 2026, concludes that 78 percent of ongoing automation projects using agentic AI are actually delivering measurable added value – a figure that seemed unimaginable just two years ago. Technology services provider PwC reports that 79 percent of the companies surveyed are already using AI agents in some form. Market researchers estimate that by 2028, autonomous agents will be making 15 percent of all work-related decisions in companies.

How SAP and Siemens are ushering in a new era

The most concrete evidence of the industrial maturity of agentic AI is currently being provided by major German technology companies. In March 2026, SAP published an official strategy paper outlining how AI agents are already being used in its customers' supply chains. The example of supplier onboarding is particularly illustrative: Agents independently verify supplier information, validate compliance with regulations, and automatically integrate them into the network. This reduces onboarding time by up to fifty percent compared to the manual process. In predictive maintenance, AI agents continuously monitor the condition of production facilities and proactively trigger maintenance measures before a breakdown can occur. SAP customers report a thirty percent reduction in unplanned downtime as a result.

When short-term disruptions occur in the supply chain—due to a supplier failure, a port closure, or a sudden surge in demand depleting inventory—AI agents independently analyze the situation, model scenarios, and initiate corrective actions. They automatically place orders, optimize inventory levels, and reduce lead times by up to 25 percent, while always maintaining human oversight. In March 2026, Siemens presented its own agentic AI system, "Fuse EDA AI Agent," at a technology trade fair for semiconductor and PCB workflows. This system autonomously coordinates complex design tasks in semiconductor manufacturing. SAP's competitors and rivals in the enterprise software segment have also recognized that the next generation of their products will simply not be competitive without an agentic architecture.

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Multi-agent systems: When agents are organized into teams

The most technologically advanced form of agentic AI is the so-called multi-agent system, an architecture in which several specialized AI agents work together in a coordinated and collaborative manner. In such an architecture, for example, one agent might handle data acquisition from internal and external sources, a second might assess risks and develop courses of action, a third might produce the final documents or initiate automated process steps, while a central coordination agent monitors the overall process and aggregates decisions. The result is no longer rigid, linear automation, but rather an autonomous digital process organization that adapts to changing conditions.

McKinsey's 2025 global AI survey observes a significant shift from mere experimentation to the deep integration of autonomous systems into core production processes, with the explicit goal of structurally increasing resilience and efficiency. A survey of over a thousand business leaders conducted by the Capgemini Research Institute found that over 80 percent plan to integrate agentic AI into their core processes within the next three years. Nearly two-thirds of these executives expect autonomous agents to significantly improve customer service and customer satisfaction.

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The blind spot: When efficiency promises meet reality

Despite these impressive growth curves, there is a downside that is often overlooked in the enthusiasm. The IBM CEO Study 2025 soberingly reveals that only 25 percent of Agentic AI projects have achieved their initial financial targets, and a mere 16 percent have been successfully scaled company-wide. IBM itself executed a remarkable strategic U-turn in mid-March 2026: The company, which had previously aimed to replace thousands of jobs with AI, announced it would triple its recruitment of entry-level employees because the anticipated efficiency gains had been largely negated by high technology costs and implementation expenses.

The reality of costs is more sobering than the marketing promises. In Germany, an AI agent pilot project with true ERP and CRM integration will cost between €30,000 and €80,000 in 2026, while a company-wide rollout will cost between €90,000 and €200,000. Over three years, the total cost of ownership amounts to one and a half to three times the initial investment when platform costs, maintenance, updates, and continuous development are factored in. Gartner also warns that by 2027, around 40 percent of all Agentic AI projects could be abandoned if sufficient risk controls and governance structures are not implemented.

What governance means when AI makes decisions independently

Perhaps the most important question arising from the proliferation of agent-based systems is not technological, but organizational. If an AI agent independently places orders, initiates contracts, sets prices, or allocates resources, who bears the responsibility for the consequences? Who monitors whether the objective assigned to the agent still aligns with the company's interests? Who prevents an agent in a multi-agent system from triggering a cascade of errors, where an incorrectly interpreted data signal is propagated through all downstream processes?

These questions are not rhetorical. They represent the real challenge of implementing agentic AI. Establishing a so-called "human-in-the-loop" principle, which mandates human involvement at defined decision points, is now considered a fundamental prerequisite for any responsible use of autonomous systems. Leading providers and research institutions emphasize that monitoring, compliance structures, and clear lines of responsibility must not be secondary considerations but rather embedded in the architecture of an agentic system from the outset. Those who neglect this dimension risk not only malfunctions but also legal consequences under the EU AI regulations, which will fully come into force in August 2026.

The strategic imperative: Why waiting is no longer an option

Many medium-sized companies are still observing the development of agent-based systems from a safe distance, overwhelmed by the complexity, costs, and compliance requirements. This reluctance is understandable, but strategically risky. The decisive competitive advantage doesn't arise from simply introducing AI agents, but from systematically identifying those processes where autonomy actually makes a measurable difference. A rule of thumb from practice is: if a process requires more than ten hours of manual effort per week and is structured enough to be described using rules, then an AI agent is almost always economically justified.

German market leaders like Siemens, SAP, and Deutsche Telekom have long since made this calculation and are investing heavily in autonomous systems. For smaller companies, a realistic entry point today lies in simple, well-defined processes, such as email triage, automated reporting, or supplier communication, with investments starting at two to five thousand euros for a first, functional agent. The crucial insight is not which technology is chosen, but whether the time saved can be translated into genuine business performance. An agent that handles ninety percent of a support task will pay for itself within one to three months compared to a full-time employee.

The direction is clear: autonomy is becoming the norm

Agentic AI is not the final stage of technological evolution; it is the beginning of a new phase. The development from multi-agent systems to hierarchically organized, mutually controlling, and learning agent networks will fundamentally redefine the possibilities of what is achievable with software over the next three to five years. Processes that currently require human decision-making will gradually become autonomous—starting where the data is clear, the rules are established, and errors are tolerable.

In its strategic roadmap for 2026 and beyond, SAP has announced plans to integrate Agentic AI directly into all core business processes, from integrated enterprise planning and digital manufacturing to logistics execution. The goal is a world where planning is more predictive and execution is largely automated. What is considered an ambitious pilot project today will be the minimum standard that customers, partners, and capital markets expect from modern companies in three years. The strategic question is no longer whether to start with Agentic AI, but how quickly one can build a viable, well-managed, and scalable architecture from experimentation.

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