In many companies, artificial intelligence has already moved beyond the experimental stage and into productive use. However, as its use grows, the role of software is also changing. Instead of individual tools, we are increasingly seeing systems that plan and execute tasks autonomously. This development is often described as agentic AI.
AI has made its way into businesses
Many organizations have already integrated AI into their operational processes. Its applications range from analytical tools and automation to assistive systems. The NVIDIA State of AI Report, based on more than 3,200 corporate surveys, shows just how far this development has already progressed.
Some key findings*:
- 76% of large companies actively use AI
- 22% evaluate the deployment
- Only 2% do not yet use AI
Economic benefits are also becoming apparent. According to the report, 88% of companies say that AI has led to increased revenue in certain areas of their business. These figures demonstrate one thing above all: AI is increasingly becoming an integral part of corporate infrastructure.
From AI Tools to AI Systems
Many organizations launch their AI initiatives with individual applications. These often include:
- Chatbots
- Copilot Assistants
- Automation of individual processes
Such applications deliver visible results quickly. At the same time, they often remain isolated solutions. However, as their use increases, a different problem arises. Companies must coordinate multiple models, data sources, and workflows. This shifts the focus from individual tools to AI systems that are integrated into existing processes. This is where the transition to agentic AI begins.
What Agentic AI Means
The term refers to systems that not only generate answers but can also perform multi-step tasks on their own.
For example, an AI agent can**:
- Research information
- Plan the steps
- control various applications
- Merge results
As a result, software solutions consisting of multiple specialized agents are increasingly being developed. Analyses by Kearney describe this trend as a new software architecture centered on agent-based systems.
The Emerging Enterprise AI Stack
Agentic AI is creating a new technical architecture. Instead of individual applications, multiple layers work together.
A simplified architecture typically looks like this:
| level | Function |
|---|---|
| Business Applications | Line-of-business applications and business processes |
| AI Agents | autonomous agents for tasks and analyses |
| Orchestration Layer | Coordination of multiple agents |
| Governance & Security | Audits, Policies, and Compliance |
| Data Platform | Data platform and models |
This structure shows that AI is increasingly being operated as a platform. The challenge lies less in individual models and more in the integration and management of the entire system.
Why Governance Is Becoming Critical
The more companies integrate AI into their operational processes, the more important control mechanisms become. Agent-based systems can prepare or automatically execute decisions. This gives rise to new requirements for control and transparency.
Typical questions include:
- Who oversees AI decisions?
- How are models validated?
- How are traceable audit trails created?
These topics are often grouped under the term "AI governance." Major technology providers are also increasingly integrating governance features into their platforms. For example, Microsoft is expanding its Copilot platform to include features for managing AI agents within enterprise environments.
Common Challenges in Enterprise AI
Many organizations underestimate the structural requirements behind AI systems.
In practice, several obstacles often arise:
- poor data quality
- Missing architecture for AI workflows
- unclear governance structures
- Isolated AI experiments without integration
In larger organizations in particular, this results in a fragmented system of tools and pilot projects. The transition to an integrated AI architecture therefore requires more than just new models. Data structures, platform architecture, and clear lines of responsibility are crucial.
Conclusion
In many companies, AI is evolving from individual applications to integrated systems. Agentic AI is giving rise to a new generation of software in which autonomous agents can plan and execute tasks. The real competitive advantage does not come from individual AI tools. What matters most is how well companies build a robust architecture for data, models, and governance. Organizations that develop this structure early on lay the foundation for scalable enterprise AI systems.
*Source: NVIDIA State of AI Report
**Source: Kearney, *The Age of Agents*
