Technical project management is transitioning from administrative oversight to "predictive orchestration" through the integration of generative AI, LLMs, and agentic workflows. By utilizing multi-agent systems that ingest telemetry from Git commits, pull request comments, and CI/CD logs, organizations are automating routine coordination tasks. This shift has significantly reallocated time: manual status tracking has dropped from 25% to 8% of a PM's workload, and risk management has decreased from 15% to 5%. While Microsoft research predicts AI will automate 80% of routine administrative tasks by 2030, current implementation faces "garbage in, garbage out" challenges where inconsistent engineering data leads to model hallucinations. Furthermore, algorithmic over-reliance poses risks, as seen when ML models misinterpret emergency hotfixes as spare capacity or miscalculate task complexity due to poor documentation. Consequently, the role is evolving into a "Systems Architect" who manages a hybrid workforce of humans and intelligent agents. As routine tasks automate, the discipline is expected to split into specialized roles like AI Operations Managers and Team Enablement Managers, requiring humans to focus on high-value strategic judgment, emotional intelligence, and navigating organizational complexity that algorithms cannot replicate.