Many companies are already using generative AI in their daily operations. Yet the impact on results often remains limited. This is where AI agents become relevant. Not as another tool, but as a lever for real AI value realization. However, this requires companies to adapt both their workflows and their management logic.

The bottleneck lies in the workflow, not in the model

McKinsey describes the issue clearly: Generative AI has arrived in many organizations. However, the economic impact often fails to materialize. At the same time, “agentic AI” is seen as the next stage. This refers to AI agents that process information, plan actions, prepare decisions, and integrate directly into workflows.

The key point is not the model. It is the workflow. If the process remains unchanged, even the best tool will deliver limited value.

This is why the topic is not purely an IT issue. At its core, it is about leadership. It requires clear roles, clear decisions, and clear accountability. Only then can AI create impact in day-to-day operations.

In our article “AI Transformation: Leadership & Culture Win”, we outline exactly this. Many companies invest, experiment, and launch pilot projects. Yet the breakthrough often fails to occur. The root cause is rarely the technology. More often, it is the lack of clear roles, sound decision-making, and a shared understanding of value.

Where operations get stuck

In many organizations, existing processes simply continue unchanged. Responsibilities remain unclear. In addition, AI is still too often delegated to IT, even though it affects the entire business.

For CFOs, the message is clear: Without a defined value logic, AI remains a cost center. It creates effort but no measurable contribution. PwC also shows that many companies have not yet translated their AI investments into tangible economic outcomes. At the same time, organizations with strong foundations are more likely to achieve measurable financial results.

For the C-level, this means: The bottleneck is not the model. It is the workflow and the management logic that governs it. AI agents must become part of the operating system. This requires clear accountability, defined control mechanisms, learning routines, and robust measurement of outcomes. Tool usage alone is not enough.

AI value realization requires a clear decision framework

If AI agents are to become EBIT-relevant in industrial companies, executives must answer three key questions: Which value streams first? What minimum governance is required? And how do we measure impact?

PwC describes uneven returns from AI as a reality for many companies. This is precisely why prioritization and clear steering are essential. More pilots alone will not solve the problem.

Select the right value streams first

Focus on three to five value streams that truly matter for your business. Avoid starting with a long list of tools or isolated use cases.

The Fraunhofer Production Alliance highlights practical application areas, especially for SMEs. These include quality inspection, production planning, energy optimization, supply chain transparency, AI in engineering, and AI-supported maintenance.

A pragmatic starting set may include:

  • visual quality inspection in manufacturing
  • adaptive production and material flow planning
  • predictive maintenance
  • supply chain transparency
  • generative AI in engineering and service

These are not experimental playgrounds. The VDMA underlines the strategic importance of AI in mechanical engineering. Many companies are already working on concrete applications.

Governance before scaling

Before scaling, clear governance is essential. This means simple but binding guardrails: Who is accountable? Which rules apply? How are results validated? What risks must be managed?

Governance is not a barrier. It is the prerequisite for moving pilots into operations.

Bitkom outlines key elements for practical implementation. Companies should clearly define where AI is used, for what purpose, and how systems are classified by risk. Higher-risk applications require greater transparency and documentation.

Three aspects are particularly important in practice. Employees must understand how AI systems work and what impact their outputs may have. Access rights, usage rules, logging, and data sharing must be clearly defined. In addition, companies must determine whether and to what extent AI systems may influence or monitor employee performance or behavior.

Data protection is also critical. AI often relies on large data volumes, including personal data. This creates inherent tensions. Wherever possible, data should be anonymized. Data subject rights must be respected. Outputs should also be regularly checked for bias.

Measure outcomes, not usage

For management purposes, focus on outcomes, not logins. The key question is not how often a system is used, but whether it creates measurable impact.

STRIM puts it simply: Measure time savings, quality improvements, customer value, or other tangible effects. Without a clear impact hypothesis, progress remains invisible.

From a CFO perspective, the implication is straightforward: If impact does not show up in a small set of EBIT-relevant KPIs within the management cycle, AI will not scale. It will merely be administered. This is where true AI value realization begins.

Bringing AI agents into operations

The real difference is not created in the next pilot project. It emerges when AI agents are embedded into core operations. This is where many initiatives fail. Solutions remain isolated and do not become part of a managed value stream. A structured starting point and a clear execution logic are therefore essential.

Step 1: Clarify the starting point with IP A

The process begins with the Transformation Readiness Scan (IP A). It creates transparency on the current state. Which processes are stable enough for automation? Where are the gaps in data quality, governance, or accountability? And which value streams are suitable for making AI agents economically effective?

Without this clarification, activity may arise. However, robust AI value realization does not emerge this way.

Step 2: Build AI agents with IP C

Next comes the AI-Ready Organization (IP C). This step focuses on operationalizing AI agents. Processes are redesigned. Roles are clearly defined. Governance is established to ensure that decisions, results, and risks are manageable.

The focus is critical. It is about a small number of prioritized use cases with clear ownership—not broad, unfocused experimentation.

Step 3: Ensure execution with IP E

The third step is consistent execution through the Strategy-to-Execution Office (IP E).

At this stage, individual initiatives become a manageable portfolio. KPI logic is formalized. Decision routines are established. Bottlenecks are made visible and actively resolved.

This is how AI agents are not only introduced, but sustainably managed—aligned with impact, priority, and clear management accountability.

An example from mechanical engineering

This can be illustrated with a typical example from the mechanical engineering sector. A company has launched initial generative AI pilots in engineering and service. Usage exists, but the impact remains unclear.

With IP A, the company first clarifies which value streams matter most and where readiness gaps exist. With IP C, workflows, roles, and governance for AI agents are then established. Finally, IP E embeds execution into a structured management cycle.

The logic is straightforward: first understand, then build, then steer. This is how AI usage turns into real AI value realization—not just another technology initiative.

Conclusion

AI agents only become EBIT-relevant when companies redesign their workflows and treat governance as an enabler. The bottleneck is rarely the technology. It lies in processes, roles, and management logic.

Organizations that prioritize effectively create the foundation for measurable impact. They define their key value streams, establish governance minimums, and manage performance through a small set of meaningful KPIs. This is how usage turns into true AI value realization.

If you want to prioritize your key value streams, define governance minimums, and set up a robust starting point for AI agents, get in touch with us.