Why cookie-cutter consistency beats custom complexity for early AI adoption.

When most people think about AI in construction, they picture algorithms generating sweeping architectural curves or optimizing the structural engineering of supertall towers.

The assumption is that artificial intelligence belongs in the realm of the complex and the creative, helping architects push boundaries and engineers solve unprecedented challenges.

Alim Uderbekov, CEO and founder of Surfaice, sees it differently.

With a background that spans from optimizing satellite trajectories for the International Space Station to building construction material marketplaces, Alim has developed a contrarian thesis about where AI delivers the most value in construction.

The answer isn't in the Burj Khalifas or the Zaha Hadid masterpieces. It's in the McDonald's.

"The race car of construction isn't a prototype hypercar," Alim explains. "It's a mass-produced sedan." His company, Surfaice, is betting that the future of AI in construction lies not in automating the unique but in perfecting the repetitive.

His co-founder, Genevieve Davis, brings a different lens to this vision.

After 20 years leading store development at companies like 7-Eleven and Kate Spade, she arrived at business school convinced AI would eliminate jobs. "I'd never used AI before. Everyone was talking about it, and I thought, why am I even here? AI is just going to take all our jobs," she recalls.

But her perspective shifted quickly:

That connection between Alim's automation-first engineering mindset and Genevieve's pro-human industry experience shapes everything Surfaice builds.

For an industry drowning in administrative work and fragmented data, this balance represents a fundamental shift in how technology should be deployed.

Bottom line: AI wins where speed, scale, and predictability matter most—not where uniqueness does.

Alim's "race car" theory starts with a simple question: who in construction requires efficiency the most?

The answer revealed itself when he began analyzing the structure of different project types. McDonald's has built roughly 40,000 restaurants worldwide, nearly all following standardized playbooks with cookie-cutter specifications. Every location follows similar timelines, uses comparable materials, and encounters predictable challenges.

This standardization creates something rare in construction: structured data at scale. While most construction projects generate mountains of unstructured information buried in emails, spreadsheets, and PDF markups, franchise rollouts represent the most organized data ecosystem the industry produces. For AI systems that rely on patterns to function effectively, this structure is essential fuel.

The Burj Khalifa, by contrast, is a dataset of one.

It's a spectacular achievement of human engineering and creativity, but its uniqueness makes it nearly impossible for AI to learn transferable lessons. You can't train an algorithm on a sample size of one and expect it to generalize useful insights.

Genevieve understands this intuitively from her years in the field. "I joined a company in 2016 and introduced Excel and got employee of the year," she says. This anecdote reveals how low-tech store development remains. If spreadsheets qualify as innovation, the industry is ripe for transformation.

But Genevieve is also cautious about where that transformation should focus. Not every process should be automated, and not every problem needs an AI solution.

Surfaice's focus on repeatable projects has produced measurable results:

These aren't hypothetical efficiency gains. They represent the tangible outcome of applying AI where it works best: on the standardized, the repetitive, and the predictable.

Understanding Surfaice's approach requires distinguishing between two fundamentally different applications of AI in construction.

Most software in the industry today is what Alim calls "horizontal." Tools like Procore or Autodesk's Building Connected serve multiple project types across various sectors. A general contractor building luxury condos might use the same project management platform as a team rolling out retail stores.

Genevieve adds an important qualifier about Procore specifically:

She explains that businesses selling clothing or groceries aren't developers building stores as their core competency, so they don't invest in that level of IT infrastructure. This insight reveals a crucial gap in the market that Surfaice targets.

Surfaice is building what Alim describes as "vertically integrated AI" that functions more like a trained employee than a software tool.

The distinction becomes clear when you examine what the company calls "skills."

Traditional software helps you retrieve information. Surfaice's AI executes complete workflows. Consider the task of ordering a site survey for a new project location:

Traditional Approach:

Surfaice's AI "Skill":

The AI doesn't just help you do your job. It executes the job according to your company's specific playbook.

This shift from retrieval to execution represents what Alim calls "reasoning agents" that can self-reflect, self-learn, and adapt to the specific workflows of retail store development.

One of Surfaice's early customers gave the AI agent a name: Spike. Team members talk about Spike the way they'd discuss a colleague, asking "Has Spike processed those submittals yet?" or "Let me check with Spike on the permit status."

This anthropomorphization reveals something important about how the technology integrates into daily work. But it also points to Surfaice's core defensibility strategy.

In an era when AI code is becoming commoditized, Surfaice isn't trying to win on technology alone. As Alim puts it,

The company's moat is methodology combined with something Genevieve understands deeply: trust. Her approach to go-to-market illustrates this perfectly.

At an industry conference, she met a woman in the bathroom who said, "Genevieve, I've always wanted to work with you. Are you still at 7-Eleven?" When Genevieve explained she'd started Surfaice, the woman's company signed within weeks.

This reveals something fundamental about construction sales that purely technical founders often miss.

Alim acknowledges this: "Construction industry is the only industry where people don't care about the product, they care about the trust."

A project manager who has spent 20 years building McDonald's locations can relatively easily pivot to building Starbucks stores.

The fundamental skills transfer even though the specific requirements differ. Surfaice's AI operates the same way:

This transferability accelerates with each new customer. The AI doesn't just get smarter about individual clients' data. It gets smarter about the process of retail development itself.

Here's where Alim's automation vision meets Genevieve's industry reality.

When Alim describes his ultimate goal of fully automated store rollouts, Genevieve pushes back, not because the technology isn't possible, but because she knows which tasks actually require human judgment.

In her experience leading store development teams at companies like 7-Eleven and Kate Spade, she observed a troubling pattern.

Project meetings would spend roughly 80% of their time on administrative topics like milestone updates, deadline tracking, and document status. Only about 20% focused on the substantive questions that actually improve the final product.

When freed from administrative burden, store development teams can focus on what truly differentiates their projects:

Genevieve draws on her experience at 7-Eleven to illustrate the power of proper support:

Most companies can't afford to hire massive administrative teams.

That's where AI fills the gap, handling the repetitive work while humans focus on negotiations, complex problem-solving, and the creative decisions that differentiate one store from another.

This division of labor points toward Alim's ultimate vision, inspired by construction practices in China where skyscrapers can be assembled in timeframes that seem impossible by Western standards.

The secret isn't magic or cutting corners. It's extreme pre-planning where every second, every worker movement, every material delivery gets mapped in advance with precision.

Retail stores are particularly suited to this approach because they're true "race cars" in the revenue sense. Every day a store remains closed represents lost sales.

Alim envisions a future where an entrepreneur could log into Surfaice, upload their coffee shop playbook and financial models, and simply instruct the AI: "Open coffee shops across the UK until the ROI drops below 125%."

That vision remains years away, but the foundation is being built with each standardized rollout Surfaice automates today.

The immediate opportunity in construction AI isn't in generating award-winning architecture or solving unprecedented engineering challenges. It's in what Alim calls "the McDonald's in your portfolio":

Bidding packages can be assembled by AI, but you still want a human selecting the bidders. Budget analysis can be automated, but negotiations with contractors require human judgment and relationship skills.

The partnership between Alim and Genevieve embodies this balance.

Alim sees the technical possibility of full automation. Genevieve knows which human touchpoints can't be eliminated without losing what makes great projects great. Together, they're building a system that maximizes both.

Let the AI handle the repetitive workflows, the document routing, the status updates, and the predictable coordination tasks.

Save human creativity, judgment, and relationship-building skills for the work that actually moves your business forward: the negotiations that save money, the design decisions that improve the product, and the client relationships that generate the next project.

The "race car" of construction isn't the flashiest project in your portfolio. It's the one that needs to cross the finish line fastest, most efficiently, and most predictably.

That's where AI wins today, and that's where the immediate value lies for any organization willing to embrace the boring while keeping humans where they matter most.

Watch the episode with Alim and Genevieve here 👇👇👇

What's the most repetitive, time-consuming process in your store development workflow? Reply with one answer. We’re collecting these for a strong repertoire of construction tech challenges and probable solutions in 2025 and beyond.

Pivoting to toy unboxing?

The evolution of the "origin story"

Value beyond the sales funnel