SERVIEW Research Insights | 2 From AI Experiments to AI Service Operations – Why Enterprise Service Management Is Becoming the Organizational Governance Model for AI

Three key developments are currently emerging:

1. AI is shifting from a technological issue to an organizational one

While many companies have primarily implemented pilot projects and proofs of concept over the past two years, the focus is now shifting to the question of how AI can be operated in a sustainable, controlled, and scalable manner. The challenge no longer lies primarily in the technology, but rather in governance, accountability, and operational models.

2. Enterprise Service Management is becoming the organizational hub for AI

More and more companies are realizing that AI cannot be managed in isolation within individual departments or innovation labs. Processes, services, roles, knowledge, risks, and compliance requirements must be managed across the organization. ESM structures already provide established mechanisms and lines of responsibility for this purpose.

3. New roles and operating models are emerging

In addition to traditional service and process managers, new roles such as AI Service Owner, AI Governance Manager, and Prompt & Knowledge Curator are emerging. In the future, companies will need hybrid organizational models that bring together ITSM, ESM, data, and AI expertise.

Trend Snapshot

What's happening in the market right now?

The first wave of generative AI was characterized by experimentation. Employees tested ChatGPT, Copilot solutions, and other GenAI tools largely on their own initiative.

Now the second phase begins:

• Companies are consolidating their AI initiatives, 
• AI governance is being established, 
• Risks and compliance requirements are operationalized,
• AI is increasingly viewed as a service, 
• Academic departments expect productive and reliable AI support. 

This shift is particularly evident in three developments:

AI Governance Is Being Institutionalized

Organizations establish central management units that define policies, standards, and approval procedures.

AI Becomes Part of the Service Portfolio

Instead of running individual AI applications in isolation, AI capabilities are increasingly being delivered as reusable enterprise services.

Operational capability is becoming more important than the pace of innovation

The central question is no longer:

"Can we use AI?"

but

"How can we use AI in a secure, transparent, and cost-effective way?"

Why is this relevant, and what impact does it have on ITSM and ESM?

The introduction of AI is transforming nearly every established service management discipline.

The Organizational Challenge

While many companies have specialized units such as AI labs, data teams, or innovation divisions, AI is often not clearly integrated into day-to-day operations. As a result, key responsibilities—such as those related to operations, data, model changes, quality, or user acceptance—remain undefined. This is precisely where Enterprise Service Management comes into play from an organizational perspective, helping to establish clear structures and lines of responsibility.

Emerging Organizational Model

As part of a new organizational model, the traditional role of the service owner is being expanded to become the AI service owner. In addition to managing AI services, this role also entails responsibility for quality, risks, governance, the entire model lifecycle, and economic aspects. As a result, the AI service owner is evolving into a central, key role, comparable to today’s product and service managers.

New Roles Within the Company

The New Operating Model

Many companies are currently moving toward a model consisting of three levels:

ESM serves as a unifying framework that bridges governance and technology. For many companies, the real challenge lies not in implementing individual AI applications; rather, the key to success is the ability to embed them permanently into existing organizational and operational structures.

The key insight is: 

AI does not scale through technology alone, but through professional organizational and operational models.

While the first phase of AI adoption was primarily characterized by innovation and experimentation, the ability to manage, integrate, and continuously improve AI will determine its long-term business value in the future.

Enterprise Service Management already provides the organizational structures, roles, and governance mechanisms necessary for the controlled and cost-effective operation of AI services.

The SERVIEW Perspective

AI Needs Less Tech Hype and More Service Management

Many organizations are currently investing significant resources in AI platforms, models, and assistants. However, the greater challenge often lies not in the technology itself, but in its sustainable integration into the organization.

From SERVIEW's perspective, competitive advantage in the future will depend less on the specific AI technology used and more on the ability to manage and continuously develop AI as a professional service.

The companies with the most successful AI initiatives will be those that:

• Integrate AI into existing governance structures, 
• establish clear responsibilities, 
• Making knowledge systematically usable, 
• Consistently apply service management practices, 
• and strike a balance between innovation, control, and value creation. 

As with the introduction of cloud services or digital platforms, it is clear that technological innovation alone is not enough when it comes to AI. Only once responsibilities, processes, and operating models have been defined can a promising technology be transformed into sustainable business value.

In our current projects, we have observed that many organizations have already identified numerous AI use cases. The biggest challenge, however, is embedding these solutions permanently into existing service and governance structures.

Takeout

1. Establish AI as an enterprise service

AI should not be pursued as an isolated technology initiative. Successful organizations treat AI capabilities as an integral part of their service portfolio, with clear responsibilities, defined quality objectives, and ongoing oversight.

2. Use Enterprise Service Management as an organizational framework

ESM already offers established mechanisms for governance, service accountability, knowledge management, and continuous improvement. These structures can be expanded in a targeted manner to ensure the sustainable operation of AI services.

3. Define new roles early on

As AI becomes more widespread, new responsibilities are emerging. Roles such as AI Service Owner, AI Governance Manager, or AI Operations Manager should be established early on to ensure transparency, quality, and compliance.

Conclusion

The next stage in the evolution of AI is not “more AI,” but “greater control.”

Companies that treat AI as a service and manage it through enterprise service management lay the foundation for sustainable scaling, governance, and business value. The future of AI, therefore, depends less on model training than on the operational and organizational models behind it.