  Recent developments in Artificial Intelligence (AI) provide unprecedented
automation opportunities in the Architecture, Engineering, and Construction
(AEC) industry. However, despite the enthusiasm regarding the use of AI, 85% of
current big data projects fail. One of the main reasons for AI project failures
in the AEC industry is the disconnect between those who plan or decide to use
AI and those who implement it. AEC practitioners often lack a clear
understanding of the capabilities and limitations of AI, leading to a failure
to distinguish between what AI should solve, what it can solve, and what it
will solve, treating these categories as if they are interchangeable. This lack
of understanding results in the disconnect between AI planning and
implementation because the planning is based on a vision of what AI should
solve without considering if it can or will solve it. To address this
challenge, this work introduces the LeanAI method. The method has been
developed using data from several ongoing longitudinal studies analyzing AI
implementations in the AEC industry, which involved 50+ hours of interview
data. The LeanAI method delineates what AI should solve, what it can solve, and
what it will solve, forcing practitioners to clearly articulate these
components early in the planning process itself by involving the relevant
stakeholders. By utilizing the method, practitioners can effectively plan AI
implementations, thus increasing the likelihood of success and ultimately
speeding up the adoption of AI. A case example illustrates the usefulness of
the method.
