Connecting the Dots: AI & the Future of Construction

Artificial intelligence (AI) is a friend — not an enemy. For now, it can’t exist without data and information created by humans, as the taxonomy of human learning, creation, and wisdom is based on the human intelligence learning sequence. In a follow up to the November/December 2023 article that laid out how AI will impact construction,1 this article expands on how AI is created.

“Transforming Construction: AI’s Role in Building the Future” discussed the future of AI as it pertains to the construction industry and dismissed fears of AI taking the place of human workers.2 As explained in previous articles such as “Founder’s Transition: The Time Is Now,” both AI and Agile IntelligenceTM need to be developed upon good, accurate data and information, collected about and by people.3

But the question, then, is how to develop the wisdom necessary to feed into AI — that is, how do you teach and transfer tacit knowledge to people in preparation for transference to processes, procedures, and, eventually, AI? To offer answers to this question, this article dives into the taxonomy of learning and the taxonomy of creation, as developed by Dr. Perry.

The Foundation for Learning & Creation

Exposure

The taxonomy of learning and the taxonomy of creation start from exposure, which ultimately differs from Bloom’s taxonomy of learning, which begins with remembering (recall, define, duplicate, memorize).4

While Bloom’s taxonomy applies well in a classroom environment, it does not account for the ability to learn through exploration and observation. It lacks the assumption of preexisting biases toward different types of information and focuses on the idea of learning as is most often found in children — learning about a completely new topic of which there has been no exposure.

For example, in learning to make an apple pie, Bloom’s taxonomy begins at teaching what an apple is and what the different types of apples are. It does not account for the knowledge of a worker from an apple orchard. Someone with more experience is going to have different goals for their learning than someone with no experience.

Agility

After exposure, the process of learning and/or creating can begin. The ability and pace to go through the next layers depends on the individual’s agility in both adapting their current frame of mind and connecting the dots through observation.

Consider the metaphor of seeing a circle when all you know is a square. People with no experience related to a particular subject (such as children) have no biases or barriers against learning new information and making changes to them. On the other hand, someone with these barriers and biases will therefore have resistance to new ideas and changes.

If the knowledge you want to pass on looks like a circle but the only thing a person knows is a square, then just showing them a circle is likely not going to help.

As shown in Exhibit 1, the first step to seeing the circle starts with rounding the edges. It allows the person to take a step toward the new direction (the circle) without losing their past reference point (the square). Depending on a person’s adaptability and resistance to change, they will eventually begin to see the circle.

A practical example of this transition is a foreperson who has been working in the field for decades and has established a routine for doing their work. This worker is going to have a different reaction to incorporating prefabrication into their work as compared to the reaction from an apprentice who has been trained on prefabrication from the beginning of their work in the field.

The foreperson has their own experiences and biases to work through in addition to learning about this new topic — and they will almost inevitably resist the new change. The apprentice has known no other way than the way being introduced to them now. In this instance, the foreperson can be represented by the square while the apprentice is the circle. The foreperson has the ability to work with prefabrication, and it is possible for them to carve the square into the shape of the circle. Different learning steps and processes can round the square into a circle over time.

For the apprentice, perhaps Bloom’s taxonomy would be acceptable for teaching; however, it was not built to account for previous knowledge and experience and would therefore almost certainly fall short in teaching the foreperson.

A revised question, then, is how do you develop learning in people who have already formed their own methods and ideas? How do you feed said learning into AI?

Taxonomy of Learning

Exhibit 2 shows how humans build on exposure by investigating or testing from their initial reactions and then learning something new.

For example, a foreperson turned project manager (PM) preparing for their first job cost review meeting will gain exposure to financial information. To learn, they need to investigate the concepts and their company’s specific application of job costing and financial projections, either through their own research or talking with their peers or company’s construction financial 
professional.

From there, the PM may test what they see and hear5 (usually by running scenarios in spreadsheets, but this can be overcome with DCI Construction®). Using the results of their learning, the PM may find this knowledge to be incomplete or incorrect.

This awareness brings exposure and new learning, and the PM will continue the cycle, eventually connecting all of those learnings to the circle, as in the correct reflection of their job’s financial status.

The Industrialization of Construction® will require significant shifts in mindsets from the current norms of construction to a new way of doing business in the field and office, including the role of vendors as logistics managers rather than material suppliers. These shifts can be accelerated with a better understanding of how humans learn.

Taxonomy of Creation

From this taxonomy of learning comes the taxonomy of creation. Both pyramids in Exhibit 2 start with exposure, but the difference in the exposure of AI is the exposure to humans for the information from which it needs to build. For humans to create, it’s critical that they use their exposure, observe issues, think through their history, and cross-pollinate from learning and experience. This allows them to build a theory and then test it.

Smaller business owners may supplement their gaps with others’ knowledge because no one will have their skills, making it more difficult for any single person to take over. Collective human knowledge doesn’t have an equivalent sample in their smaller businesses, as it always trumps individual genius.

If AI is put in place to study and anticipate obstacles in the work environment, then the decision-maker (e.g., doctor, pilot, electrician) can make an informed decision based on the collective experiences and learning.

Don’t confuse automation with AI; it’s just that — artificial. Only when humans are part of the input and output of the process does AI become used for agility (hence, Agile Intelligence™).

Data to Information to Knowledge to Wisdom

Data should always be part of the learning and creation processes. It may not be formal data, but any kind of observations or tests (e.g., “If this happens, then I expect that outcome”) will generate some findings. Translating these findings into information and then into knowledge and wisdom requires asking the right questions.

As covered in “Transforming Construction: AI’s Role in Building the Future” from the November/December 2023 issue, intelligence is not artificial; it is based on the human process of translating data to knowledge by building on others’ learnings and creations.6

Connecting the Dots

AI needs to have a capability to connect the three topics — the taxonomy of learning, the taxonomy of creation, and the data to wisdom transition — to create the information needed for use by humans. The capability to make this connection is dependent on the data source.

As mentioned, the current theories (in the case of learning) and usage (in the case of creation) have been built in construction based on the same foundation of exposure; however, the path upward is very tacit. Most construction technicians learn by doing and create by trial and error. There is no database housing these dots, but they can be connected through DCI Construction®.

The Dots: Data Sources

Through years of research by MCA recognizing where data exists in construction, DCI Construction® is built on these sources:

Centralized Databases

In construction, the most common of these databases is accounting, led by construction financial professionals who take care of the money in a data-driven environment. Most construction companies also have an estimating database. Neither of these databases are built nor intended to house field data.

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About the Authors

Dr. Heather Moore

Dr. Heather Moore is the Vice President of Operations of MCA, Inc. in Grand Blanc, MI. Her focus is on measuring and improving productivity. A previous author for CFMA Building Profits, she holds an Industrial Engineering degree from the University of Michigan and a PhD in Construction Management from Michigan State University.

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Dr. Perry Daneshgari

Dr. Perry Daneshgari is President and CEO of MCA, Inc. in Grand Blanc, MI. MCA focuses on implementing process and product development, waste reduction, and productivity improvement of labor, project management, estimating, and accounting.

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