The efficiency of photovoltaic (PV) systems depends critically on accurate sun tracking, but conventional dual-axis trackers, while boosting energy yield by up to 40%, involve high mechanical complexity, energy consumption, and costs. This paper introduces a novel solar tracking solution developed through a hybrid methodology that integrates generative artificial intelligence with TRIZ-based inventive reasoning. The approach, termed AInnovation, systematically resolves engineering contradictions by merging data-driven exploration with symbolic problem-solving principles. The resulting system employs a single-motor azimuth mechanism combined with a passive cam-based elevation adjustment, eliminating the need for dual-axis actuation. Cam profiles are optimized by generative AI using solar ephemerides and manufacturability constraints, while TRIZ principles guide simplification and contradiction resolution. The design achieves 85–87% of the energy yield of dual-axis systems, while reducing actuation energy consumption by ~ 70% and overall costs by ~ 42%. A prototype, validated through simulations and field tests, confirmed the robustness, reliability, and maintainability of the system under real-world conditions. The IoT-enabled architecture supports telemetry, anomaly detection, and remote configurability, ensuring suitability for both grid-connected and off-grid applications. Beyond solar tracking, the study establishes a generalizable framework for inventive electromechanical design, with potential extensions to robotics, energy storage, and medical devices where structural contradictions constrain innovation. This work demonstrates that AI can transcend automation and optimization, advancing toward invention-driven engineering and offering affordable, scalable, and efficient solutions for renewable energy deployment.