Agentic AI is reshaping IT and security teams, boosting efficiency but also posing a conundrum: how do you cultivate the next generation of experts when AI automates the very tasks that trained them? As organizations incorporate AI to handle work previously done by junior analysts and engineers, they face a workforce challenge as crucial as any technology issue. The decisions made today about integrating AI will determine who succeeds in the future IT landscape.
## The Role of Junior Analysts in the AI Era
For years, the journey to becoming a top-tier SecOps analyst or NetOps engineer was paved with repetitive tasks. These included triaging false positives, sifting through dashboards for context, and late-night log reviews that often turned up benign. While often seen as tedious, these tasks formed the bedrock of an apprenticeship, crucial for building deep analytical skills over time.
However, agentic AI is now automating these foundational tasks. While this shift reduces burnout and costs, it also dismantles the traditional training ground for developing intuition and expertise. Organizations should embrace AI for reducing toil but must also find new ways to cultivate operator skills. Those who do will develop the experts needed in the coming decade; those who don’t may find themselves with fast systems but a lack of deep operational understanding.
## Automation and the Accountability Gap
Another lesser-discussed issue is how automation impacts accountability, especially in regulated environments. Frameworks like SOX, PCI DSS, and HIPAA rely on a chain of human judgments behind control decisions. Auditors interview people, not models, to ensure decisions are sound and controls are in place.
As the pool of professionals who understand these systems thins, the risk isn’t immediately apparent. Controls might still pass audits, but the organizational memory and understanding begin to erode. This isn’t just a tooling issue—it’s a workforce skill and design problem. For organizations rapidly adopting AI, this risk is more imminent than it seems.
## Governing AI with Human Expertise
As AI takes over parts of the accountability layer, humans must adopt new roles in governance. This involves creating automated guardrails for AI behavior, designing escalation criteria to filter anomalies, and implementing tools to review machine decisions for drift or bias. These tasks require judgment honed over years, a skill set traditionally developed through the apprenticeship model.
The challenge for organizations is to ensure that operators remain skilled enough to govern AI systems effectively. This means rethinking workforce design and training, so that expertise can be developed in new ways. As AI continues to evolve, the ability to manage it effectively will become a critical differentiator.
### What’s Next for Organizations
As AI reshapes the workforce, organizations must balance automation with the development of human expertise. Founders, engineers, and investors should consider how to integrate AI without losing the deep operational understanding necessary for long-term success. This means investing in new training models and governance frameworks to ensure AI systems remain accountable and effective. For those willing to tackle these challenges head-on, the future holds promise—but only if they act now to prepare.
