AI-RISC is a scalable RISC-V processor developed using hardware, ISA (Instruction Set Architecture), and software co-design methodology to extend the open-source RISC-V architecture for accelerating edge AI applications. AI-RISC tightly integrates hardware accelerators as AI functional units (AFU) inside the RISC-V processor pipeline to allow seamless processing of both AI and non-AI tasks on the same hardware. AI-RISC also extends the RISC-V ISA with custom instructions which directly target the added AFUs and expose the hardware functionality to software programmers. Additionally, AI-RISC generates a complete software stack including compiler, assembler, linker, simulator, and profiler while preserving the high-level programming abstraction offered by popular AI domain-specific languages and frameworks like TensorFlow, PyTorch, MXNet, Keras, etc. AI-RISC accelerates the processing of vector-matrix multiply (VMM) kernel by 17.63x and of ResNet-8 neural network model from industry standard MLPerf Tiny benchmark by 4.41x compared to RISC-V processor baseline. AI-RISC also outperforms the state-of-the-art Arm Cortex-A72 IoT edge processor by 2.45x on average over the complete MLPerf Tiny inference benchmark. Additionally, AI-RISC provides 3.93x improvement in energy-efficiency over the baseline RISC-V processor and 11.49x energy-efficiency improvement over state-of-the-art Gemmini systolic array accelerator.