Recent advances in acoustic recorder technology and automated species identification hold great promise for avian monitoring efforts. Comparing these innovations to traditional monitoring techniques is vital to understanding their utility to researchers and managers. We compared bird detection among four acoustic recorder models and concurrent point counts and assessed the ability of the artificial neural network BirdNET to correctly identify bird species from AudioMoth recordings. AudioMoths performed comparably to higher-cost recorders, and three of the five recorder models detected more species than the point counts. A combination of long AudioMoth recordings, BirdNET, and human verification detected higher species richness than point counts conducted in similar habitats. These methods enabled us to survey avian community composition with low misidentification rates and limited need for human verification. Subsequently, we have expanded this methodology to 170 survey locations across diverse ecosystems in central and northern California in 2021 and 2022. This approach holds great promise for improving large-scale, multi-species avian monitoring to inform conservation and management of California’s bird species.