The information contained within this article serves to raise the awareness of possible benefits, implications, and impacts of using artificial intelligence solutions across the AEC industry; many ideas presented, though, do apply to a broader range of organizations.

AI and machine learning are elements of business intelligence (BI) strategies and technologies, which are used by enterprises for data analysis and information extraction. Traditional challenges, functions, or actions that AI techniques can address include reasoning, knowledge representation, planning, learning, natural language processing (and understanding), perception, and the ability to move and manipulate objects.

In each challenge area, AI technologies are proving to have significant performance benefits versus other traditional mathematical modeling approaches. For instance, the capabilities of Apple Siri, Amazon Alexa, and Google Assistant to understand human speech significantly outperforms interactive voice response (IVR) technologies used for decades.

“Artificial intelligence” itself usually describes the ability of a machine to mimic human actions or cognitive functions, such as problem solving or maintaining a conversation. This type of artificial intelligence is typically referred to as “strong” AI. Presently, strong AI systems are lacking. Other specialized applications of AI are termed “weak” AI or machine learning applications. Machine learning applications offer the potential to supplant human work in a variety of AEC functions, including schedule analysis, safety adherence, material control, client reporting, and data analysis.

As the technology continues to mature, there are thousands of companies competing for dollars in the AI realm. Similar to Big Data, hype surrounding the capabilities of AI is enormous. As time progresses, these technologies are likely to come closer and closer to “plug and play,” however, currently there is still a reasonably large barrier between the “dreams” of AI-enabled AEC solutions and the need for significant expertise and investment to make those “dreams” plausible.

As fast as the pace of development of AI tools and technologies is occurring, AI applications should find their way from modest experiments and pilot demonstrations to fully scalable applications in the near term. Common trends in AI development, according to Forbes, are:

  • Development of AI-specific hardware chips for embedding machine learning across widespread business and consumer products
  • Movement of machine learning models from centralized cloud systems to edge Internet of Things (IoT) devices
  • Interoperability among neural network modeling systems and frameworks via Open Neural Network Exchange (ONNX)
  • Automated machine learning with AutoML—speeding the process of building and deploying neural networks
  • Application of AI analysis to information technology operations
  • Evolution of chatbots and virtual assistants into more comprehensive, context-sensitive question and answer functions
  • Availability of machine learning services and software to professionals without deep software development and database management skills

Determining how to begin an AI course will be unique to your organization. As is true with any business process change, the basis for incorporating AI has three basic components:

  • A supporting company framework, policies, and culture
  • Processes, staff, and technology that support the program
  • Structured implementation of the system itself

The foundation of any successful program is first the company framework to support the activity. In the context of AI applications, developing the necessary organizational structure and functions for the AEC industry is an important element. After these enabling actions, the business processes for using AI technologies should follow more readily and be more effective due to a strong foundation in business processes. These processes should enable the AI programs to function at a high level initially and continue to adapt and improve as AI technology advances.

It is important to remember with any technology deployment that some of the dimensions are inherently more difficult to deal with than others, yet they all should be addressed to move forward. Failing to consider issues related to staffing and organization, for example, may result in your AI project being a pilot that is never integrated companywide.

A holistic AI program may be developed considering many hypothetical applications then scaled back to consider what might be accomplished with more realistic budgets and resources. Companies in the early planning stages can take the following steps:

  • Establish a steering committee to educate decision makers, staff, and strategic partners on AI and brainstorm potential applications and synergies
  • Discuss priorities, opportunities, and barriers to AI applications
  • Determine a short list of high-priority applications and a longer list of secondary-priority functions that address commonly encountered challenges and new capabilities. While many goals are generic, tailoring the AI strategy to pressing issues is typically useful in gaining broader acceptance