# LeanAI: A method for AEC practitioners to effectively plan AI implementations

arXiv: https://arxiv.org/abs/2306.16799 · HTML: https://mlsanity.com/paper/2306.16799/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 29 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [AI-based Prediction of Independent Construction Safety Outcomes from Universal Attributes](https://mlsanity.com/paper/1908.05972/) (2019)
- [Digital Twin: From Concept to Practice](https://mlsanity.com/paper/2201.06912/) (2022)
- [Trust in AI and Implications for the AEC Research: A Literature Analysis](https://mlsanity.com/paper/2203.03847/) (2022)
- [Digital Twin in Practice: Emergent Insights from an ethnographic-action research study](https://mlsanity.com/paper/2203.07030/) (2022)
- [A new perspective on Digital Twins: Imparting intelligence and agency to entities](https://mlsanity.com/paper/2210.05350/) (2022)
- [Digital Twin: Where do humans fit in?](https://mlsanity.com/paper/2301.03040/) (2023)

## Abstract (arXiv)

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.