Much of the research for AI in design and construction targets the project itself, making the most of the large volume of data collected during project life cycles. But the project team is also a good target.
“Our point of view is that technology is not delivering on its promises of productivity and innovation in the construction industry, not because of the technology itself, but the people involved,” says Jordan Cram, CEO of consulting firm Enstoa. His firm is taking machine learning and applying it to employee management, with the idea that a computer might notice team members’ hidden talents and preferences. “We have this ability to consider large amounts of data, so let’s apply these technologies to that organizational problem,” says Cram.
恩斯托亚（Enstoa）接受了人格测试的旧公司待机，但与其通过过度强调自己的最佳品质而不是让人们玩游戏，而是让人工智能在人们的工作方式中找到更多微妙的动机。克拉姆解释说：“我们在每个人方面获得12或15个维度，其中整个员工都在数据库中。”“因此，当我们需要一个具有X，Y和Z值的新团队时，我们可以使用机器学习和统计分析来为我们提供最佳的团队。”
Employees are evaluated on topics ranging from their approach to problem solving to how well they want their goals defined in a project. These personality profiles are put through Enstoa’s clustering algorithm to look for the right grouping for the job at hand. Cram says the algorithm can spot employee qualities that a manager overseeing large teams could miss, citing an example of a project team member who prefers highly technical work but falls behind when given too much latitude
一次执行多个任务。“我们在数据库中查看，发现会发现工作有益的人。我们与之合作的一家公司在沙特阿拉伯有一份EPC合同，需要一个拥有更具创业价值的人，例如，任务规模不会吓倒。”该公司的规模使很难找到合适的人，但是该算法发现了表现出兴趣的员工。“我们将这些结果放在经理面前，说‘您认为什么？’这是另一个观点，可能是错误的，但是在决策过程中考虑这些隐藏的联系很重要。”
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