Agentic AI is advancing far faster than previous waves of technology such as cloud and low-code platforms. In the past, organizations often needed years to build new solutions. Today, dozens or even hundreds of AI agents can emerge in a short period of time. Once the first projects prove successful, demand for additional AI solutions quickly grows. Business functions identify further use cases, existing processes are targeted for greater automation, and more AI agents enter the development pipeline.
This changes the nature of the challenge. The initial focus may be on successfully delivering individual AI agents. The priority soon shifts to coordinating a growing portfolio of AI initiatives efficiently and scaling it sustainably across the organization.
Why Scaling Becomes the Real Challenge
As the number of AI initiatives increases, so do the demands on IT. In addition to developing new AI agents, organizations must address architecture, integration, security, and operations. At the same time, demand from business functions often grows faster than IT can systematically assess and deliver new requirements.
Without shared standards and clear prioritization, a backlog quickly builds. Requirements compete for limited IT capacity, similar solutions are developed more than once, and existing components are not reused systematically. Development and operating costs rise, while growing complexity slows the business value agentic AI is intended to create. The real challenge therefore does not begin with the first AI agent, but with the hundreds or thousands that follow.
When requirements cannot be delivered quickly enough, business functions increasingly pursue solutions on their own. Because AI agents can now be developed with comparatively limited technical effort, initial applications often emerge outside established processes. What may first appear to be a sign of innovation can quickly become a new form of shadow IT: shadow AI. Agents are created without shared standards, accountability remains unclear, and risks often become visible only after solutions are already running in production.
More Development Teams Do Not Automatically Create More Impact
A common response to growing demand for AI is to add more development teams. However, this often addresses only part of the challenge. Additional capacity may increase the number of AI agents that can be delivered, but if ten teams develop agents independently, the result may be ten different sets of standards and ten different operating models. Similar requirements are implemented repeatedly, knowledge remains concentrated within individual teams, and successful solutions are difficult to reuse across the organization. Rather than improving scalability, this creates additional complexity.
Moving from the development of individual AI agents to their systematic operation is therefore primarily an organizational challenge, not a technology challenge. Successful scaling depends less on the number of development teams than on an organization’s ability to develop, operate, and continuously improve AI solutions according to shared principles.
Why Agentic AI Needs an Operating Model
At this stage, many organizations initially respond by introducing additional governance rules. Transparency across AI agents, clear accountability, and defined approval processes are essential. Governance alone, however, does not determine how new requirements should be assessed, prioritized, developed, and operated over time. To manage growing demand for AI sustainably, organizations need shared standards for prioritization, development, and operations. Requirements must be evaluated and prioritized, and solutions must be designed from the outset so that existing capabilities can support new use cases rather than being implemented as isolated projects.
One approach is to establish an Agent Factory. This is not a development factory designed to produce as many AI agents as possible. It is an operating model for agentic AI. As an organizational framework, it helps companies manage the full lifecycle of their AI solutions. New requirements are captured centrally, assessed, and prioritized. Teams then determine whether existing components or previously developed AI agents can be reused, or whether a new solution is required. Only then does implementation begin, based on shared standards for architecture, security, and operations. This ensures that AI solutions are not developed as disconnected projects, but as part of a scalable enterprise architecture.
This does not necessarily require a new organizational unit. What matters is clearly defining existing responsibilities, establishing shared standards, and creating a sustainable model for collaboration between business functions and IT. This enables new AI solutions to be developed efficiently, operated securely, and improved continuously.
Establishing the Right Foundations Early
Many organizations begin putting organizational structures in place only after numerous AI initiatives are already running in parallel and operational challenges have started to emerge. By that point, introducing standards and clarifying responsibilities retrospectively often requires significant effort.
Organizations should therefore establish transparency across existing AI initiatives early, assign clear responsibilities, and define shared standards for prioritization, development, and operations. One clear warning sign is when demand from business functions grows faster than the organization’s ability to assess, prioritize, and deliver it systematically.
Conclusion
For many organizations, developing individual AI agents is becoming routine. The critical success factor, however, will not be how quickly they can launch new agents. It will be their ability to manage growing demand for AI so that new requirements are delivered efficiently, existing solutions are reused, and AI applications can be operated reliably over time.
Organizations that establish shared standards, clear responsibilities, and effective governance processes early create the foundation for scaling agentic AI sustainably.
Do you have questions about the Agent Factory, or are you looking to scale agentic AI strategically across your organization?
Categories
#AI Transformation
#Data & AI
#AI Strategy
#AI Agents
