Build vs. Buy in the Time of AI

How Real Estate Leaders Should Think About the Decision

For years, the answer to the "build versus buy" question was relatively straightforward.

Building enterprise software required large development teams, long implementation timelines, and significant financial investment. Unless your organization had a truly unique business requirement, purchasing an established software solution was usually the smarter decision.

Artificial intelligence changed the equation.

Today, employees can create applications, automate repetitive tasks, and prototype workflows in hours instead of months. The barrier to building software has never been lower.

Now, the real challenge isn't whether your organization can build something, but knowing whether you should.

AI Has Lowered the Barrier to Software Development

Modern AI tools are enabling business users to solve problems that once required dedicated development teams.

Teams can now automate workflows, create their own internal productivity tools, analyze data, and generate reports and dashboards, all with little to no barrier to entry, and these capabilities are empowering departments across commercial real estate to solve problems faster than ever before.

That's exciting, but it also creates a BIG new challenge.

Without a clear framework, organizations can quickly find themselves maintaining dozens of internal tools that were never intended to become permanent software products.

The result? More technical debt, more maintenance, and more complexity.

A Better Framework for Build vs. Buy

Rather than viewing every project as either "build" or "buy" problems, organizations should think about software investments in three categories.

1. Build Quick Wins

Some of the best opportunities for AI aren't enterprise applications at all. They're small improvements that eliminate repetitive work.

These are the kinds of projects that save individual employees time, automate manual tasks, and improve reporting. These kinds of lightweight automations can deliver meaningful productivity gains without requiring extensive governance or long-term development.

They're often the ideal place for organizations to begin experimenting with AI.

2. Build Your Competitive Advantage

The second category is much smaller.

If a workflow is genuinely unique to your organization—something that directly contributes to how you outperform competitors—it may be worth building internally.

Examples might include:

• Proprietary investment analysis
• Specialized acquisition workflows
• Unique reporting methodologies
• Internal processes that reflect your firm's strategy

The key question is simple:

Would another company gain an advantage if they copied this process?

If the answer is no, it may not be as proprietary as it seems.

Many organizations believe they're solving a unique problem when they're actually addressing a challenge shared across the entire industry.

3. Buy Core Business Systems

Core enterprise software is where many organizations overestimate what AI can realistically replace.

Accounting systems, ERPs, and other enterprise platforms involve years—often decades—of development around:

• Security
• User permissions
• Scalability
• Compliance
• Integrations
• Performance
• Auditability

AI may make it easier to build a prototype.

It doesn't eliminate the complexity of maintaining software that hundreds or thousands of employees rely on every day.

In most cases, these systems are better purchased from established vendors whose sole focus is maintaining and improving them.

The Hidden Cost of Building Software

One of the biggest misconceptions surrounding AI is that development is the expensive part. In reality, development is often the easiest step. Maintenance is where the real cost begins.

Every internal application eventually raises questions like:

• Who owns it?
• Who updates it?
• How is access managed?
• What happens if the original creator leaves the company?
• How do we ensure it remains secure and compliant?

These ongoing responsibilities are easy to overlook during the excitement of building something new. They're also the reason many organizations discover they have unintentionally become software companies.

Don't Discourage Innovation

Perhaps the most important takeaway is that AI experimentation is already happening.

Employees are testing new tools, building automations, and finding creative ways to improve their work, sometimes without leadership even realizing it.

Trying to eliminate that experimentation entirely rarely works.

Instead, organizations should focus on creating guardrails that encourage responsible innovation while protecting company data and governance requirements.

The goal isn't to stop people from building, but to help them build the right things.

Build Where It Matters. Buy Where It Counts.

Artificial intelligence hasn't eliminated the need for software vendors.

Instead, it has changed how organizations should think about technology investments.

The greatest opportunities often come from empowering employees to automate everyday work while relying on proven software platforms for the complex systems that keep the business running.

Understanding the difference between those two categories is becoming one of the most important technology decisions organizations will make over the next several years.

If you'd like to hear this discussion in more depth—including practical examples from technology leaders who have built and evaluated software at enterprise real estate organizations—watch our Build vs. Buy in the Age of AI webinar on demand, or download the companion ebook for additional insights.