Reimagining Overhead With AI Agents
Can you imagine running an agile business while carrying a mountain of overhead?
Overhead increases complexity, rigidity, and operating leverage, which in turn leads to missing opportunities, ignored vulnerablities, price capitulations and abrupt downsizing programs.
The only way to outsmart the competition is to sense, process, and act faster. In today’s corporate landscape, setting up a meeting can take up to two weeks—a clear indicator of organizational sluggishness.
While leadership often focuses on reducing variable costs and improving operational efficiency, overhead remains the real culprit. High overhead increases complexity, rigidity, and operating leverage, leading to missed opportunities, overlooked vulnerabilities, price wars, and abrupt downsizing.
Success in modern markets requires anticipating changes and adapting more swiftly than competitors. Insights can emerge from anywhere—the macro environment, customer interactions, or supplier relationships. However, traditional corporate structures are typically too slow and inflexible to effectively capture and act on these signals.
In the age of AI agents, survival hinges on three interconnected elements:
Model-based Enterprise Architecture: Replace fragmented documents and static presentations with an integrated analytical model of the enterprise. This model should connect real-time data, expectations, and projections, providing a unified and dynamic view of the organization.
Intelligent Ontology: Move beyond siloed functional hierarchies by adopting a ground-up, extensible, and composable graph ontology. This approach reflects real-world relationships, fostering better collaboration and understanding across the organization.
AI Agents: With a robust model-based architecture and intelligent ontology in place, AI agents can be implemented with very little effort and they can operate continuously and efficiently at near-zero marginal cost. These agents transform insights into real-time actions and value, enhancing responsiveness and decision-making.
© Saip Eren Yilmaz, 2024
