When McKinsey asked the chief operating officer of one of America’s largest home builders how generative artificial intelligence (Gen AI) could impact his industry, his response came down to two familiar words for home builders: opportunity and risk.

With $4.5 billion invested in Gen AI startups in 2022 alone, and McKinsey’s Global Institute estimating the technology’s impact to be up to $2.6 trillion to $4.4 trillion, there is significant value to be created across the economy. In fact, we identified over 50 distinct use cases where the new technology could transform the home building industry. At the same time, the technology is incredibly nascent, and we are only just beginning to use its application in at-scale use cases.

So, what does this mean for home builders practically? How much value can Gen AI create for home builders? What parts of the business does Gen AI truly elevate? And how do companies get started considering the risks?

How Much Value Can Gen AI Create for Home Builders?

Before we get too far, we should clarify what we mean by Gen AI. Gen AI is the latest frontier in the continued advancement of artificial intelligence. AI is the ability to mimic human intelligence with machines and, traditionally, has focused on problems of optimization and prediction (e.g., pricing); Gen AI goes further, enabling customized content generation (text, image, video, etc.) at pace and scale. This article will review both —AI and Gen AI, which we will collectively refer to as “(Gen) AI”—in terms of value for home builders.

As many of us know, consumer expectations are shifting; those looking to buy homes increasingly demand digital experiences, greater personalization and choice, and shorter timelines for new products. At the same time, market competition and macroeconomic pressures are forcing home builders to ask the age-old question: How can we deliver more for less?

The good news is (Gen) AI can help.

In fact, we estimate Gen AI and AI can unlock value of up to $18 billion for home builders, equivalent to almost 10% of industry revenues. Let’s break down where value is coming from and how your organization can take advantage of this opportunity.

(Gen) AI could add up to $18 billion in value to your sector – equivalent to 5-9% of revenue
McKinsey & Company (Gen) AI could add up to $18 billion in value to your sector – equivalent to 5-9% of revenue

What Parts of My Business Will Gen AI Truly Elevate?

(Gen) AI creates value across the entire home builder value chain. We identified over 50 potential use cases, which broadly fall into four “patterns” that we believe will add the majority of value for home builders:

  1. Home builder virtual adviser: Digital “co-pilots” across the value chain from design and construction to sales, resolving bottlenecks in real time;
  2. Creative content and design: Instant personalized content generation at high-speed and low incremental cost to support construction design, marketing campaigns, and more;
  3. Customer and vendor engagement: Better serve customers and interact with suppliers through real-time, hyperpersonalized interactions; and
  4. Prediction and optimization: Generate quantitative insights and improve decision-making across the value chain, from lot or floor plan selection to dynamic pricing.
Four main archetypes of (Gen) AI use cases for home builders.
McKinsey & Company Four main archetypes of (Gen) AI use cases for home builders.

Across these four patterns, there are a handful of use cases that are likely to create a majority of the value. We’ll get into each one:

1. AI-powered virtual advisers for home builders can take on a large proportion of the busy work, by consuming and synthesizing vast amounts of unstructured data to develop actionable insights based on text prompts. No more sifting through your many systems (CRM, inventory management system, contracts system, email) to assist a customer or check a building standard. Just ask your “Gen AI adviser” the question you need answered. Two types of flavors of this are especially promising:

  • A broker virtual adviser could assist brokers and agents in answering customer questions (e.g., on comps, property info., etc.) and even create first drafts of contracts and applications by ingesting customer and property data, reducing operating expenses throughout the sales transaction process.
  • A construction co-pilot is another use case. There are construction platforms emerging that apply computer vision and machine learning to jobsite footage. They can automatically track progress and productivity across locations and trades, enabling a project manager to spend less time walking jobsites and more time solving issues.

2. Generating creative content and design using Gen AI means architects will be able to draft first versions of designs automatically and focus their time on iterating more valuable details; interior designers can work with homeowners to rapidly visualize many options (e.g. colors, materials, etc.) before choosing specifications; and marketers can create hyperpersonalized materials for specific customers with little incremental effort.

3. Automated customer and vendor interactions are areas where Gen AI can unlock value—through automated, real-time, hyperpersonalized interactions. Currently, home builder sales representatives use phone, email, and text messages to provide manual updates on homes and answer questions from customers. This means no record of interaction, little analysis of recurring customer needs, and a lot of time sunk in repetitive manual follow ups.

  • A virtual customer service rep can respond to customer service requests faster or entirely automatically (e.g., when a home buyer inquires about when they should begin shopping for a loan), all while recording interactions, and predict upcoming requests and resolve them preemptively.
  • A supplier engagement rep can serve a similar function on the other side of the house, automating conversations and gathering quotes at scale with suppliers.

4. Finally, more traditional AI use cases in prediction and optimization can improve decision-making based on quantitative data. Out of a potential impact of up to $18 billion, we expect more than half of the value to come from these (non-generative) AI use cases.

  • Dynamic pricing of homes that maximizes overall revenue—using county and tax data, market trends, nearby homes, multiple listing service (MLS), proprietary sales data—is a hugely valuable but underutilized AI application.
  • Risk assessment with advanced analytics to assess financial risk of a potential tenant or to help underwrite a homeowner’s insurance policy.
  • Warranty optimization—through predictive analytics—can mean clarity around when best to address construction issues arising in the home building process, weighing the cost of resolving the issue "in phase" or later in a warranty claim.
  • Lot selection and acquisition is a clear potential area AI can optimize—a task usually left to a small team of land acquisition managers manually scouring the nation with incomplete information. AI can make this activity far more scientific, identifying the highest potential lots based on location factors, zoning, land ownership, utilities, comparable lots, and any other relevant dimensions.

How Do We Get Started Considering the Risks?

AI is the most accessible it has ever been. However, the risks of (Gen) AI need to be carefully considered, from models projecting bias in their outputs, data privacy with use of sensitive data or third-party models, and the concerns of explainability and “hallucinations” of the model.

At the same time, (Gen) AI is complex to adopt into existing processes. We recommend a holistic approach—reflecting principles of responsible AI—to begin experimenting and building to scale. This means:

  • Align on a digital vision and commitment upfront: Do you want to lead or follow on (Gen) AI? How much are you willing to invest?
  • Establish a responsible AI posture, inclusive of ethical guardrails, accountability for managing risks, organizationwide structure for controls and governance, and a commitment to continuously reassess your position given how quickly the space is moving.
  • Identify and prioritize use cases based on value potential and feasibility: What areas of your business will truly create value through AI? What is the cost and execution burden of transforming these areas?
  • Articulate a road map: How will you sequence initiatives and the required capability building? Are you comfortable with failures along the way? What does risk-conscious execution along a “gated process” look like? What are the “quick wins” that will help you generate momentum and excitement?
  • Empower an accountable senior leader: Who will be on the hook to deliver this? Do they have sufficient sway and influence across the organization? Can they drive adoption?