How to Choose the Right AI Technology to Support Operational Efficiency

This article is the first in a series summarizing points from the webinar “Key Steps to Ensuring a Better AI Implementation.” If you want to watch the full conversation, you can do so here. 

The entrepreneurial nature of the real estate sector often means that operators focus on growing their portfolios first, only to realize later that their back-office systems can no longer keep up. This often leads to a situation where companies have scaled too quickly and must then play catch-up with their operational infrastructure. When this happens, many companies turn to hiring to fill in the gaps. But at this point, adding more staff is not a sustainable solution (you can’t continue hiring in perpetuity). Instead, AI tools can be used to bring systems in line with the size and scope of the business.

Finding the Right AI Fit

The key to successfully integrating AI into your organization’s workflow is identifying the specific problems that need solving. Is it a labor shortage that AI can address, or is the organization dealing with large volumes of manual work, data processing, or transactions? For CRE companies in particular, growth can be rapid—often acquiring new properties at a pace that staff alone cannot manage. This is where AI can be helpful, automating tasks like AP processing, property onboarding, and data transitions, allowing the company to scale quickly without overburdening its workforce.

But no technology is a one-size-fits-all solution, and knowing when AI works (and when it doesn’t) is critical. 

Some examples of real estate work that can be handled by AI include: 

• Accounts payable: it’s knowledge-heavy and requires lots of manual, repetitive data entry that AI can learn from and adapt.
  
  
• Lease abstraction: it relies heavily on existing data and information, allowing AI to recognize patterns and provide paired-down information to key stakeholders.
  
  
• Property management: properties handle thousands of documents each month, from lease agreements to rental applications and maintenance requests; AI can automate document processing and improve data tracking. 

And in some instances, while a technology solution in general is the best course of action to solve a problem, the right technology for a job might not always be AI.

For instance, let’s look at accounts payable: some problems (like workflow approvals) are handled well using basic OCR (optical character recognition) software. But when it comes to actual invoice ingestion and coding–tasks that require a large amount of data entry and judgment–AI is the better solution because it can learn from patterns and processes and grow alongside your existing team. 

Strategic Planning for AI Success

Organizations must also be strategic in how they apply AI within their existing technology infrastructure. For example, AI can help speed up data transitions, but only if the technology is integrated thoughtfully into existing operations. Look for technology that natively integrates with, or at least complements, your existing tech stack. 

To do this, you must know what your existing tech stack looks like, and how well it is performing.

1. Have a strategy in place to evaluate systems. When companies grow too quickly without adjusting their infrastructure, they run the risk of creating inefficiencies and bottlenecks down the road.
   
   
2. Ensure current operations are aligned with their level of growth and adjust the use of AI tools accordingly. Is your team drowning in busywork? Are they unable to complete tasks in a timely manner? If so, how can AI step in to help them?
   
   
3. Assess your tools thoroughly and often. Be proactive and make changes before problems arise.
   
   
4. When evaluating new technology, ask how it integrates with your most critical tools. Will it fit seamlessly into your team’s day-to-day workflow, or will extensive customization and training be necessary? 

The Future of AI in Business Operations

AI has the potential to revolutionize the way businesses handle back-office functions, enabling growth at previously unheard of levels. But its success will depend on how well organizations understand their own needs and implement the technology in a measured, strategic way. Businesses that embrace AI thoughtfully and apply it to the right problems will be best positioned to scale efficiently and maintain a competitive edge in their industries.

As AI tools become more advanced and user-friendly, many companies are jumping on the bandwagon to improve efficiency. However, implementing AI comes with its own set of challenges and operational risks. Organizations need to ensure that the tools they purchase are not only helpful, but secure and sustainable. 

To help, you should evaluate: 

1. The business problem at hand. What issue needs to be solved?
   
   
2. Whether that issue is best solved by staffing or technology. Is it truly something that can only be solved by a human, or are there systems and patterns in place that can be learned by AI?
   
   
3. How any potential technology will fit into your existing tech stack. Does it natively integrate with any mission-critical tools you’re currently using, or will custom builds be necessary? 

When deciding whether to implement an AI solution, it’s important to recognize that many are still in the early stages of development, and often, businesses using these tools are serving as real-world testers. The potential operational improvements AI brings can sometimes overshadow small flaws, but that doesn’t mean the flaws should be ignored.

In industries like real estate, particularly in multifamily and senior living spaces, there is a critical labor shortage, making this technology a very tempting solution. Owners and operators are increasingly looking for ways to reduce back-office workloads, allowing on-site teams to focus on tenants.

The decision of where to allocate human hours is crucial, and AI tools can significantly help optimize operations. However, AI isn’t a magic bullet for all challenges. For many issues, traditional, tried-and-tested methods might still be more appropriate. Business leaders need to evaluate when AI is the right fit and avoid trying to force AI into roles where it may not be the best solution.