Construction AI

What are agentic workflows, what do they mean for generative AI, and what does it have to do with construction? We begin to explore that this week, looking at how one type of agentic workflow can boost the quality of generative AI content for you. Also, find more bullish reports on AI’s growth in the construction industry.

To improve the output of generative AI models, we explore an exciting concept in AI development called Agentic workflow. Agentic workflows work differently than standard work with LLMs. Rather than an LLM generating its final output directly, an agentic workflow prompts the LLM multiple times, allowing it to build, step by step, a higher-quality output.

Artificial intelligence’s (AI) ability to discern patterns unapparent to the human eye is already beginning to help improve safety and performance on construction projects. And the future looks bright for more positive impact, according to panelists at a webinar sponsored by Sensera Sytems on April 23. 

The rise of generative artificial intelligence (AI) could mark a pivotal moment in the industry's digital transformation, building on the momentum gained during the COVID-19 pandemic, according to a new report from Deloitte. "Construction technology companies are actively exploring generative AI's potential for enhancing efficiencies and are working on solutions to harness this technology to suit their business needs," says the report.

This week find Chapter 2 in Part I of the eBook series AI-Powered Construction Management--use ChatGPT to redefine identification and management of project deliverables. Then, In our AI primer series: cat or dog? How does Machine Learning work? Next read about AI concerns—notably its energy appetite, along with several other relevant stories AI in the News! And finally, catch a prediction on the pace of change.

This 2nd chapter of Part I of the eBook series, "AI-Powered Construction Management" focuses on identifying deliverables during the construction phase. This phase, brimming with activities and complexities, demands precision and foresight to navigate successfully. This exploration will demonstrate how you can leverage generative AI to redefine the identification and management of project deliverables.

Did the pace of innovation in artificial intelligence shake you in 2023? In that case, you'd better hold onto your hat, Kris Lengieza, global technology evangelist for Procore, said during a recent episode of The Construction Record Podcast.

A summary of top AI news stories in business and construction. AI is transformative. It is driving a burst of new constructions projects while finding application within design & construction. But there are challenges. What about explainability? And AI processing requires tremendous amounts of electric power to run; will its growth be unsustainable? 

Machine learning is a branch of artificial intelligence (AI) that focuses on building systems that learn from and make data-based decisions. Here's a simple way to understand machine learning. Imagine teaching a child to distinguish between cats and dogs ...

What is generative AI? In this issue, see the new primer on the subject for the basics. Also in this issue, find a new eBook series: AI-Powered Construction Management. Part 1, Chapter 1 explores the use of generative AI in developing key project deliverables. Also, find out whether leading experts and venture capitalists are bullish or bearish on the application of AI to the AEC industry.

Welcome to Part 1, Chapter 1, of our new eBook series AI-Powered Construction Management.
As the landscape of project management continues to evolve, the fusion of artificial intelligence with traditional practices heralds a new era of efficiency and precision. 

Artificial Intelligence is a broad term comprising a variety of technologies. Applications that produce or “generate” new content, such as text, images, speech or code are called “generative AI.” Generative AI refers to a subset of artificial intelligence technologies and models that can generate new content or data based on data on which they were trained.