Our client — a prominent capital construction manager for a major U.S. city responsible for building civic facilities and designing, maintaining, and improving public infrastructure — wanted to explore ways machine learning could make the organization more efficient and turned to Enstoa for help.
The client’s project controls team knew there was an opportunity to leverage their data more efficiently through machine learning, but recognized they did not have the in-house expertise to define and implement a sophisticated strategy on their own. As a result, they enlisted Enstoa, a trusted partner, to assist them with their data strategy and design a plan to get them started.
The initial engagement was consultative and strategy-driven. Enstoa's Artificial Intelligence and Machine Learning team met with our client's key team members to identify current data utilization, gaps, and opportunities to define the client's desired future state. These findings, along with a detailed future state roadmap, was presented to senior team members to obtain alignment. Following this, our client’s core team completed Enstoa’s Colonnade training course - AI: Machine Learning Essentials to prepare them for the journey to come. By the end of this initial engagement, the team was equipped with the foundational machine learning knowledge to execute their vision with a clear plan as they prepared to take the next steps.
After defining a strong data strategy in our initial engagement, our client was ready to tackle their first machine learning project: estimating project costs. Project cost benchmarking is a great use case for machine learning and a natural first step for managing complex capital projects. Our client had years of project data available to help estimate direct labor costs and recognized how machine learning could make that calculation more accurate.
Enstoa worked with the client team to develop a solution for inclusion in their standard dashboards and reports. Now, the client can better estimate labor cost based on key patterns in project profiles identified with the aid of machine learning. With this machine learning knowledge and practical experience under their belt, they continue to leverage machine learning algorithms and practices to drive efficiency and productivity in their portfolio of capital projects.
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