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Closing the Gap Between Business Problem and Tech Solution in the Age of AI

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At a recent CIO & Property Tech Forum hosted by Realcomm, I found myself in a room of senior IT executives from real estate and investment management firms wrestling with the disconnect between what new tech can do for them, and what they should actually do with it.

In an interesting role reversal, property owners were discussing how to build data analytics products, while a vendor whose company provides building sensors was asking what that data should be used to accomplish. No one had a clear answer about what to do with all the data being collected.

This has been a common theme cutting across real estate recently. There’s a persistent and growing gap between the what – the business problem being solved, why it’s difficult, and what the right outcome is – and the how – the technical and operational ability to execute on a solution.

Mind the AI Gap

Amid mounting pressure to adopt AI to gain a competitive advantage and protect shrinking margins, real estate companies are underestimating the size of that problem-solution gap, losing costly cycles trying to solve the wrong problems.

No one can blame the industry for wanting to take the initiative. We’re used to the gap being smaller. An accountant might know exactly what kind of report they want and have the Excel skills to build it regardless of what their accounting system can do. Gap closed. Problem solved.

But artificial intelligence changes everything. On one side. real estate growth is increasing data volume and operational complexity. On the other, AI and machine learning capabilities increase the difficulty of implementing a targeted, effective, usable solution.

Concerns about data security and quality, information architecture, and tech infrastructure compatibility inevitably emerge and further complicate these efforts.

Avoiding AI-DIY

I’ve seen several tech companies abandon AI projects as a result of having underestimated their difficulty. Yet real estate firms, despite having core competencies around business intelligence rather than artificial intelligence, keep insisting on bridging the gap themselves.

Real estate stands apart from other industries in terms of complexity, which is why we often rely on solutions designed with our unique challenges in mind. When there’s an urgent need, demand to leverage AI, and nothing in the market that solves your problem, building a solution yourself can seem like the only option.

Here’s where everyone gets stuck: the gap between the people who really understand the business side of the problem (the what), and those who can actually build a solution (the how) is widening exponentially. But the solution is not to become or necessarily hire AI experts.

No experienced IT leader would attempt to build their own ERP or CRM or accounting software. They would buy and configure a proven solution. Even complex custom implementations are falling out of fashion in favor of standardized cloud solutions to ensure flexibility and scalability.

However, watching AI take leaps forward week to week makes the whole enterprise seem deceptively simple. ChatGPT seems like it’s just plug-and-play, right? And if the market fails to deliver the right solutions and you can’t afford to fall behind, what do you do?

Define, Then Delegate

There’s good news: you don’t have to become a machine learning expert to leverage AI. You just have to be the expert in your business problem.

Whether there is a real-estate-specific off-the-shelf AI solution available depends partly on whether you’ve defined the problem clearly enough. That means defining it in simple, non-technical terms: we can’t retain Gen Z employees because they hate paperwork and want a career path—how can we reduce that? We cut checks only twice a month and keep paying out late fees when invoices don’t get approved fast enough – is there a way to avoid this?

Articulating the problem this way allows you to narrow your focus, which can help with a solution search, and gives you the appropriate metric for success. This becomes more important when trying to prove ROI.

PredictAP was born out of a frustration with an in-house accounts payable process problem. We didn’t start with an AI hammer that went looking for a nail – or the aspirations of building a tech company. We just needed a better way to get invoices paid on time. It turned out that AI was the right solution for reducing manual data entry and turning past AP work into automatically executable guidelines.

Once you define your desired outcomes, you will find it easier to leverage more targeted solutions (document processing, virtual credit card payments, AP automation) rather than hunt for complex all-in-one offerings, or worse, attempt building one.

This clarity will also help you work more effectively with experts who are better versed in how to leverage AI and to approach it in a more modular manner, i.e., help you choose tech that is tailored to a specific use case even when it’s not real-estate focused, or better define the technical requirements for success.

The gap between a problem and the tech to solve it widens as complexity increases on both sides, and what can be solved with some initiative and Excel on one end of the spectrum is best left to the pros on the other. If you’re planning to incorporate AI into your tech stack, make sure that falling behind isn’t the problem you’re trying to solve and that your expert eye is focused on what matters most: your business.

David Stifter, CEO & Co-Founder, PredictAP David Stifter has spent over two decades at the nexus of real estate, technology, and finance. In 2020, he founded PredictAP to address a gap in the real estate AP automation market with an AI-powered invoice capture solution. He previously served as Managing Director at Digital Bridge (formerly Colony Capital), overseeing data architecture, process improvement, and accounting and finance transition projects for major acquisitions.

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