When we talk about AI in commercial real estate, the potential is huge.
Imagine AI-powered CRM systems that intuitively understand voice commands to deliver hyper-accurate search results. Or think of AI automatically abstracting complex lease agreements or generating data-rich marketing brochures with minimal human effort. That’s gold for a busy broker.
These are tangible applications of AI in CRE being developed and piloted today. According to McKinsey, GenAI has the potential to completely transform the real estate industry, possibly generating between $110 to $180 billion in value in the coming years.
But here’s the thing, with all that excitement, all that projected value, it’s easy to misstep. It’s one thing to see the potential but actually getting these AI tools into the daily grind, into those workflows, that’s where it gets complex. Without a clear strategy, firms risk investing significant resources into solutions that don’t deliver, leading to disillusionment and costly AI failures.
Understanding Why AI Projects Fail
The most common AI implementation challenges lie on the surface. And from my experience, recognizing these early on is key. Let’s take a closer look:
1. Lack of Clear Focus
A classic issue is what I’d call ‘boil the ocean‘ syndrome. The temptation is to try and do too much, too soon. The opportunities with AI are so vast that it’s very easy to get overwhelmed.
And when organizations attempt to tackle every AI application simultaneously, what happens? Resources get stretched, focus dilutes, and ultimately, projects often don’t reach completion or, more importantly, don’t deliver that meaningful impact we’re all looking for. This lack of clear prioritization is a primary reason why AI projects fail.
To overcome this challenge, the key is to start small and strategic:
- Focus on specific processes that would add the most value if automated.
- Choose AI a partner who truly understands your real estate niche.
- Ensure your partner can run a thorough audit of your existing processes and infrastructure.
- Establish clear success metrics and timelines before AI implementation begins.
Any AI implementation should follow a focused, step-by-step roadmap aligned with your business goals. That’s how you ensure every initiative drives ROI – not just activity.
2. Falling for Tech Evolution Trap
Another critical area is navigating the sheer pace of AI evolution. New models, tools, and techniques are emerging constantly. And while that’s incredibly exciting, it presents a significant challenge for implementation.
For instance, a team might dedicate considerable time and budget to custom train a current LLM model to, say, accurately interpret highly specific real estate jargon or complex valuation methodologies. Then, just as they’re nearing deployment, a new foundational model is released that handles those very tasks natively, with greater accuracy and less need for bespoke training. Suddenly, the months of specialized development work and the associated investment are significantly devalued, if not rendered entirely redundant.
This creates a real dilemma: do you proceed with the original plan, risking a solution that’s behind the curve on day one? Or do you pivot to newer technology, incurring further development costs and delays, and potentially facing the same issue again down the line?
It’s a tough strategic call, and without careful consideration, firms can either launch something that’s already outdated or get stuck in a cycle of continuous, costly redevelopment without ever fully realizing the benefits.
3. Finding the Right AI Skills – and the Right Partner
One of the most underestimated challenges in implementing AI for commercial real estate is the talent gap. Not just in AI skills, but in real estate-specific AI expertise. These skillsets rarely overlap, and that’s exactly where many projects break down.
We encountered clients who initially partnered with general consultancies, only to discover that their implementations fall short when applied to real-world real estate scenarios. These clients often come to us requiring significant rework, as their original AI solutions were developed without understanding the nuances of commercial real estate investment deals or leasing processes. This is what can make or break an AI implementation’s effectiveness in our industry.
4. The “Why” Matters: User-Centricity in AI
Perhaps the most fundamental reason, and a key to understanding why AI projects fail, is losing sight of the end-user and the core business problem the tech is meant to solve.
A lesson to learn: If an AI solution, regardless of its technical sophistication, is cumbersome or doesn’t address a genuine pain point for brokers, realtors, and analysts in the field, it won’t be adopted.
The lack of user-centricity directly reflects a well-known principle – you must start with the user problem and then identify the appropriate technology to solve it, not vice versa.
Practical Steps for AI Adoption in CRE
Addressing “what are the main challenges in implementing AI solutions” effectively means applying AI to business needs. Here are some practical steps we’ve found to be effective:
- Partner with real estate-focused AI specialists: Work with implementation partners who understand the unique complexities of CRE. They can help navigate industry challenges, recommend proven solutions, and accelerate deployment while avoiding common pitfalls that other consultants might miss.
- Prioritize data quality and accessibility: Investing in data cleaning, standardization, and centralized management systems is necessary.
- Ensure smooth integration with existing systems: Legacy systems prevalent in CRE can impede AI integration. Opt for AI solutions that are compatible with current infrastructures to minimize disruptions.
- Implement robust data privacy and security measures: CRE deals with sensitive information. Comply with regulations and set up clear data governance.
- Implement in phases: AI projects can be big and complex. Don’t try to do everything at once. A phased rollout is the smart way. It allows for manageable investments, learning as you go, and adjusting.
- Cultivate an open culture: Communicate clearly and consistently about how AI benefits your teams and the business. This helps get everyone on board.
- Keep humans in charge: AI is great for automating tasks and handling data, but real estate decisions always need human judgment and experience. AI should support your professionals, not replace them. This approach builds trust and ultimately leads to better results.
Without a doubt, AI in commercial real estate is a powerful tool. Either you’re just starting or have tried adopting AI, my biggest recommendation would be map out not just the ‘what’ and ‘why’ of your AI goals, but critically, the ‘who’ and ‘how’ of partnership.
By addressing these challenges, especially by making sure to team up with partners who really get the ins and outs of commercial real estate, you can tap into AI’s full power and make a truly meaningful impact.
