In recent years, researchers have relied heavily on data (historical and real-time) and digital solutions to support informed decisions. Consequently, data analysis has become an integral part of the design and construction process. Researchers spend a tremendous amount of time cleaning, organizing, and understanding the data. Artificial Intelligence (AI) can be used to help overcome human limitations in processing and enriching large volumes of data from a variety of sources. AI can encompass millions of alternatives for various design and project delivery solutions and ultimately improve project planning, construction, and maintenance and operation process. AI can be a solution to the severely under-digitized Architecture, Engineering, and Construction (AEC) industry, however, there are two challenges with respect to creating intelligent agents in AEC; (1) finding appropriate ways of gathering information from the environment and transforming them into internal context, and (2) selecting an appropriate AI technique to succeed in decision-making based on the relevant knowledge about the environment. This study focuses primarily on the second challenge by looking at potential applications of AI in AEC. The AI techniques in AEC can generally be classified into two main areas: (1) decision making methods and algorithms, and (2) learning methods. Regarding the first area, search methods and optimization theories are used when there is enough information to tackle decision-making and the problem is solved by the selection of the best action (with regard to some constraints and criteria) from a set of alternatives. The learning methods, on the other hand, are further classified into knowledge-based, reasoning, and planning methods (to learn how to adapt to changing conditions), learning probabilistic methods (e.g. Bayesian learning), and machine learning (e.g. supervised learning, reinforcement learning).