Strategy for Growing Self-Governing Artificial Intelligence: A Systematic Approach for Expansion in Agentic AI
In the rapidly evolving world of technology, one trend stands out as the No. 1 strategic technology trend for 2025: Agentic AI, autonomous systems capable of making decisions and performing operations without human intervention. However, the path to adopting agentic AI is not without challenges.
Gaps in infrastructure, security, and governance concerns, unclear starting points, and organizational resistance to change are barriers to successful implementation. To overcome these hurdles, a thoughtful, phased approach is essential. This approach, known as the "Three I's" model, involves ideation, incubation, and industrialization.
In the ideation phase, high-impact, low-complexity use cases for AI agents should be identified. These use cases should offer strategic advantage and align with an organization's goals.
The Forbes Technology Council, a community of world-class CIOs, CTOs, and technology executives, including Norman MΓΌller, a Forbes AI expert and digital transformation professional, is committed to helping businesses navigate this transition. The council brings together influential technology leaders, experts from business, science, and society, all focused on applied artificial intelligence and digital transformation.
The incubation phase emphasizes building out core functionality, strengthening the solution with robust error handling, enacting comprehensive logging, and exploring lightweight integrations with existing systems. Organizations should re-architect their infrastructure for continuous processing, decentralized decision making, and secure, real-time access to data and compute resources.
In the industrialization phase, the solution should be scaled with full lifecycle management, strong governance, and enterprise-grade observability. It's essential to ensure that AI agents' decisions and actions are aligned with internal standards, legal frameworks, and regulatory policies by implementing policy-as-code procedures and adding fail-safe defaults.
Adopting event-driven microservices and edge computing can enhance agentic AI responsiveness and reduce latency. A robust agentic AI observability framework should be designed, capturing reasoning chains, decision confidence scores, tool usage, and logic paths. Agent access should be restricted only to those data sets and systems needed to execute its specific task to minimize operational risk, support data privacy standards, and conform with residency regulations.
Maintaining zero-trust security and dynamic authentication is critical in an autonomous environment. Layered monitoring with human-in-the-loop checkpoints is essential for scaling agentic AI safely and confidently. Shifting from batch processing to real-time processing can help agents operate seamlessly and in sync with dynamic environments.
Despite the potential benefits, fewer than one-third of enterprises currently have AI agents in production or are actively scaling their deployment. A disciplined, structured approach that is rooted in simplicity, control, and scalability is key to driving strategic advantage with agentic AI.
Ben Blanquera, the VP of Technology and Sustainability at Rackspace Technology, is one such leader driving this change. As we move forward, the successful implementation of agentic AI will be a critical factor in maintaining a competitive edge in the digital age.
