Augmented reality (AR) has proven to be an invaluable interactive medium to reduce cognitive load by bridging the gap between the task-at-hand and relevant information by displaying information without disturbing the user's focus. AR is particularly useful in the manufacturing environment where a diverse set of tasks such as assembly and maintenance must be performed in the most cost-effective and efficient manner possible. While AR systems have seen immense research innovation in recent years, the current strategies utilised in AR for camera calibration, detection, tracking, camera position and orientation (pose) estimation, inverse rendering, procedure storage, virtual object creation, registration, and rendering are still mostly dominated by traditional non-AI approaches. This restricts their practicability to controlled environments with limited variations in the scene. Classical AR methods can be greatly improved through the incorporation of various AI strategies like deep learning, ontology, and expert systems for adapting to broader scene variations and user preferences. This research work provides a review of current AR strategies, critical appraisal for these strategies, and potential AI solutions for every component of the computational pipeline of AR systems. Given the review of current work in both fields, future research work directions are also outlined.