Of the industry sectors A.I. can't wait to get its cocky, know-it-all, quasi-sentient algorithmic mitts on residential construction and its related fields set up to be a doozy.

While the dirt (i.e. lots) remains susceptible to a byzantine complex of codes, zoning rules, environmental conditions, neighbors' resistance, and extremely wide variability on the investment valuation front, give that part of the riddle time for the likes of generative artificial intelligence.

When it comes to the other parts – i.e. matching specific consumers' wants, pocketbook, needs, desires, and preferences to specific homes that get designed, engineered, built, priced, and sold – the future of A.I. is practically algorithmically itching to declare itself in a big way.

In some cases it has, profoundly.

Three-year-old Canada-based Openhouse.AI has integrated its advanced matching solutions into homebuilders' websites, simplifying and streamlining – removing friction – in customers' shopping and selection process. At the same time, the platform cuts out design, purchasing, and construction build-cycle inefficiencies from the builder's operational model – and crystallizes better demand forecasting -- due to clean, real-time, constantly-refreshed customer-specific knowledge.

OpenHouse.AI founder and ceo Will Zhang likens the double-benefits to both customers and builders to one of the economy's dominant users of machine learning and generative A.I. on an applied basis: Amazon.

Amazon asks us questions and listens to all of our buying behaviors, our zip code, our education details, our occupation, our reading, etc., and organizes its enormous logistics, distribution, and warehouse systems, so that we can order and receive what we want, often in a day or less," says Zhang. "The solutions, products, services they bring to market align in a closer and closer way to what it is we'll be interested in or need to buy. That comes from insight into what customers value at a deep level."

To get a big, well-informed take on enormous and evolving implications, opportunities, and known and unknown caveats around a juggernaut whose ultimate impact on business and life – it is said – could eclipse the industrial revolution and the internet itself, we chatted with Chris Laskowski, marketing director at New Home Star.

Here are some of Chris' takes, particularly insightful as they shed light on builders' need and initiatives to both elevate buyers' motivations to take the plunge now and lower the barriers in the way of their doing so. Authors and Northwestern University/Kellogg School of Management professors Loran Nordgren and David Schonthal speak of raising motivation and lowering barriers as "adding fuel" and "removing friction" to cause someone to change. Laskowski observes that – as regards engagement, shopping and purchasing – fuel and friction are practically ideally set up for AI to come into play.

A Game Changer

Laskowski offers four examples of A.I.'s immediate or near-future applicability, given that these areas are both repeatable and already well-documented in digital records.

1. One can navigate mortgage applications using a chatbot. Not only could you apply and get approved, but as you walk different homes, you could have an A.I. assistant give you real-time monthly payments, as well as to store them in a dashboard for you. Then you can do a comparative analysis across builders and different homes.
2. AI can optimize setting appointments for viewings. Again, the bot could optimize your travel based on schedules, and provide all of the information in one place so you could be as efficient with your time. As you visit each home, they will be automatically added to a portal for you to ask follow-up questions about the different homes and have all the collateral in one place.
3. Third, new homes can be easier to envision. Nowadays, visualizing to-be-built homes either requires you to use your imagination because the specific features and colors are not available in their rendering library, or if a builder has invested several weeks into a visualization platform that is difficult and expensive to maintain. A.I. changes the game as it can edit any rendering without all the infrastructure, giving builders immediate access to these tools as new finishes, features, and options roll out.
4. A last example may only be a few years away. It causes many builders headaches: Managing and building website content. You can now ask A.I. to build you a website, and two minutes later, you have one. This technology is only going to improve and eliminate many jobs in web development. You would be able to add communities, update pages, and add new promotions using a chat interface, rather than going through several layers of individuals with different skill sets, one can cut down costs to implement this tasks in a significant way.

A Use Case Study On Impact

Says New Home Star's Chris Laskowski:

AI creates a more consistent, high-quality customer experience that is scalable and cheaper than doing manually."

Laskowski cites an instance of one company that implemented A.I. into residential leasing applications, impacting the organization and the industry as a whole. The company, Dynasty [now part of Appfolio] automates leasing applications with a chatbot — everything up to the actual showing. Previously, leasing was an entirely manual process, including lots of emails, documentation, and uploads — the perfect place to inject A.I.

Essentially you had the chat data input, Laskowski says. "That went to a neural network. If the data came through at a high enough confidence level, it then would spit out the results. If not, the data would flow through to a human. They tested it out, and it outperformed the human interaction across all major metrics like NPS, conversion rates, and time-to-lease when they tested it with a pilot.

As they began pitching their product, many employees of these companies revolted. Initially, they did not succeed in gaining businesses that were heavy on culture. They found better luck with companies that prioritized performance.

Eventually, their AI was so successful that they cornered the market, and now virtually every single leasing company uses this approach.

As builders consider one of many A.I. solutions that are popping up, here are three critical early learning-curve lessons-learned in this space, according to New Home Star's Chris Laskowski.

Find Human-Like Solutions

Dynasty [Appfolio] tested virtually every possible variation for their chatbot. Do they tell the person that they are talking to a bot? Do not say anything at all? Do they give the A.I. a human's name and photo? After all of the tests, the best results always came from humanizing the A.I.. In fact, the biggest lift they got was to add an artificial delay to the response time that mimics the time it would take for a human to type up that answer.

Adapt Your Workforce

The leasing companies quickly saw that the people they used to hire – i.e. the ones who were good at organizing paperwork – were actually not the best ones for the new role of just showing the homes. They adapted their hiring practices to bring on individuals who were great with people and had strong sales skills, which further maximized the performance.

Maintain Clean Data Records

A.I. is not possible without a clean system of record. In fact, in some cases, because the data was not well organized and complete, the A.I. would learn things based on a bad foundation (i.e. garbage in/garbage out).

Finally: Speed Bumps

As we look forward, A.I. is not only here to stay," says Laskowski. "It will fundamentally change every industry in this decade, including homebuilding. The speed at which it takes over will be determined by three factors. First, how simple is the task? If it's highly repetitive and creates a big lift for an organization, it will be first in line for disruption. Second, what kind of regulation will come around A.I.? This is a big topic, and there are a lot of questions about what Congress will ultimately do. Lastly, what is the legal liability around AI? As an example, medical consultations have a wide risk of financial exposure due to lawsuits. The more legal ramifications, the more resistance to relying on A.I.