How A.I. And Data Transform A Messy Real World Of Building
It might as well be Natural Law No.-1 in building:
S#!t happens."
What happens before and after that inevitable role-player impacts end-to-end building life-cycles will determine what a construction project's value creation – residential, commercial, industrial, institutional, etc. – fails at or achieves.
Fact is, heavy odds of surprise lurk in plain sight in the myriad human hand-offs, local official decision and inspection chains, site ecological and topographical factors, climate conditions, SKU details and subcomponents, design and construction disconnects, and countless other variables. To describe building any vertical sheltered space as a "loosely coupled system," may be overly generous.
Construction Physics sage Brian Potter delves into building complexity here.
It's within this context that most construction projects – home, subdivision, masterplanned community, and on up – tend to become the latest in an ongoing case study examples of a Yiddish proverb:
Man plans, God laughs."
Beyond documented standards, the industry is also strongly shaped by what Dubois and Gadde refer to as a “community of practice” - a combination of informal norms, widely accepted techniques, craft skills, and unspoken assumptions that together create a common set of expectations on a project. When a team comes together for a project, even if they’ve never worked together before, there will already be a shared set of expectations as to how information will be represented on drawings, the deliverables the project will require, who will have responsibility for what, how the different systems will interact, and all the other items that might otherwise require time, effort, and expense to coordinate.
This community of practice is reinforced by contractual requirements and state laws which require contractors and designers to conform to “standard practice” or meet the “standard of care” - in essence, requiring you to do what a reasonable professional would do.
Tradition and culture in construction is thus load bearing - “the way we’ve always done it” is the answer to how we’re able to do it all. This method of organization stems from construction’s history as a collection of tasks performed by skilled trades, workers hired for their expertise (often acquired under an apprenticeship), and given wide leeway to accomplish their tasks."
Practically given wisdom in this "community of practice" in real life and the real world is our little cameo, supporting, or starring role player: S#!t happens.
The impacts – delays, cost overruns, loss or waste of physical resources, busted credibility and reputation, and heightened business risk, etc., not to mention opportunity costs – speak for themselves.
Research by the McKinsey Global Institute indicates that digital transformation can result in productivity gains of 14-15% and cost reductions of 4-6%." - Slate
Enter A.I., an emerging doorway into construction building life-cycle reality with the potential to improve real-life outcomes before and after the inevitable S#@t happens moment.
Here's a job description for applying large language learning models to the real-life construction build-cycle domain.
- Harness disparate data sets to make informed decisions and optimize resource allocation
- Leverage predictive analysis to anticipate and mitigate potential risks before they materialize, ensuring smoother project execution and minimizing costly delays
- Streamline operations through the automation of workflows and activities using advanced technologies, thereby enhancing productivity and reducing construction timelines
- Provide insights into how personalized data analytics empower stakeholders with actionable insights in a unified interface, enabling them to make strategic decisions that drive efficiency and innovation within the project
Joel Hutchines, CPO of Slate Technologies, an A.I.-powered construction solutions platform, believes builders can leverage innovation to propel larger projects forward, but only if they're prepared for when – inevitably – things don't go as planned, and don't go as planned in what may be the unlikeliest of instances or the least imaginable of moments in the cycle. For Hutchines, S#!t happens is just another way of discussing the unwieldy process and the unpredictable outcomes inherent in any building lifecycle.
You need to cut through the s#@t to get to what matters, and Slate enables that," says Hutchines' of the platform's Decision Assistant field solution. "Typically, we're so reactive in production. But with Slate, we connect all the layers of data and bring it to our fingertips, and with the tool, we can pull out the critical issue and say, 'This one matters.' That's the key chat partner. We overlay all these siloed information and data streams – BIM geometry, schedules, issues, etc. – and contextualize, filter, and, ultimately, highlight the issue that will affect the schedule. We provide tools that, in the morning, as the 'boots-on-the-ground' come in, they can interact with and query the data. They have to get value from it rather than adjust to a new chat widget that someone's launching into the market. We build tools that are valuable and solve real problems."
Slate's Decision Assistant, along with its Real Estate Intelligence platform, which ingests "terabytes" of macroeconomic indicators, data analytics, zip codes, regions, housing metrics, etc., and scouts out both community development and factory siting coordinates, along with a generative A.I.-powered product design, engineering, 3D digital twin, and construction documentation tool, make up Slate's three initial development and implementation focus areas, says CEO Trevor Schick.
We're trying to get in front of every part of the building lifecycle, that at any point," Schick says. "When a stakeholder has 100 problems they're struggling with on a given day, what we do is to say, 'Here are the biggest five problems you have; these are the things you need to address.' We'll also make recommendations and capture ways to understand the practice better, and improve it with our learning. When we're thinking about the platform, those are the pieces in the market used by customers now."
The implications in homebuilding and residential development for what Schick, Hutchines, and the Slate are currently applying in the domain of data center development and construction projects are vast. Data center construction is driven by the imperative to meet escalating demands for digital services. However, amidst this growth, developers and construction teams encounter a multitude of challenges. These include everything from intricate site selection and land acquisition to the unpredictability of supply chain management and adherence to stringent environmental and regulatory compliance standards throughout the country.
Sound familiar?
In residential building and development, I can see a world in the short term where you can leverage A.I. to help you acquire the land, and tell you what the best thing is to build on it," says Hutchines. "Then create the design and schedule, all while considering past experiences. You have the constructability of it. Some of these tools in generative design don't have any constructability knowledge and lack data from downstream of the real-life buildability. That's where we stand alone in the market: Our ability to understand the construction side and contextualize that to influence the decisions at the front end. So, there is a short-term to medium-term where I see the real value of construction knowledge as a critical criterion to help decide what, where, and how to build. And that is the future."