If machines get it, why don't we?

I asked ChatGPT to explain the problem that exist in the construction industry due to the lack of data driven decisions and how a product like Slate Technologies Inc. may help. Here is the answer:

Construction projects are complex undertakings that require a vast amount of decision making. These decisions can range from choosing materials, to determining the best construction methods, to managing timelines and budgets. However, many of these decisions are made without the relevant data, which can lead to costly mistakes and rework.

According to a study by the Construction Industry Institute, an average construction project makes over 6,000 decisions. This number can be even higher for large, complex projects. With so many decisions being made, it is crucial that they are informed by accurate and relevant data. Unfortunately, this is not always the case.

The cost of poorly made decisions on construction projects can be staggering. A study by the McKinsey Global Institute estimates that poor data quality and lack of data-driven decision making costs the global economy $3.1 trillion annually. In the construction industry specifically, poor data quality and decision making can lead to increased costs due to rework and delays.

One of the main challenges facing construction decision makers is the lack of access to relevant data. This can be due to a variety of factors, such as siloed data systems, lack of communication between project stakeholders, and lack of data management capabilities. As a result, decision makers are often forced to make decisions based on incomplete or outdated information.

One solution to this problem is the use of an AI decision assistant. An AI decision assistant can help to connect and enhance data from multiple sources, providing decision makers with a more complete and accurate picture of the project. By bringing together data from different systems and stakeholders, an AI decision assistant can provide situational, contextual, and valuable notifications with recommendations.

This can help to ensure that the right data is getting to the right people at the right time, which can lead to better decision making and ultimately, more successful projects.

For example, an AI decision assistant can analyze data from building management systems and weather forecasts to recommend the best time to pour concrete or schedule crane operations. It could also use data from project management software to identify potential delays and recommend corrective actions. By providing real-time, actionable insights, an AI decision assistant can help decision makers to make more informed and effective decisions.

In conclusion, construction projects are complex undertakings that require a vast amount of decision making. However, many of these decisions are made without the relevant data, which can lead to costly mistakes and rework. The use of an AI decision assistant can help to connect and enhance data from multiple sources, providing decision makers with a more complete and accurate picture of the project. By bringing together data from different systems and stakeholders, an AI decision assistant can provide situational, contextual, and valuable notifications with recommendations, which can lead to better decision making and ultimately, more successful projects.