Weekly Briefing

Success with Real Estate AI is a Journey

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Last time, I shared some use cases for applying artificial intelligence (AI), machine learning (ML) and automation to commercial real estate. While realizing the benefits of these technologies is a never-ending process, this time, I’ll share the steps we took to achieve the gains we’ve made thus far.

Start with the End in Mind

One of the biggest challenges in implementing new technologies is to avoid falling into the “shiny object syndrome” trap. Using technology just because it’s new or interesting can lead to the development of tools and products that do little to benefit users, which leads to challenges with adoption. Our prime concern in implementing AI, ML and automation is to drive better outcomes for our clients and professionals. As I mentioned last time, we focused on three key areas: unlocking efficiency, improving decision-making and enhancing experiences. This focus resulted in use cases where we applied the technology to solve real-life business challenges. Having clear goals and objectives and working back from there helped us realize business value.

Building Great Technology Starts with Talent

Realizing the benefits of AI, ML and automation requires foundational investments in the right talent. The first thing we did was bring in experienced data analysts, engineers and scientists. We needed people who had “been there, done that” with respect to building data platforms and advanced data models that could be leveraged to generate insights. Coupling the new talent with our existing talent helped improve knowledge on both sides – the new talent got to better understand the real estate industry and the existing talent got a better understanding of how data could transform the industry.

Getting Returns Requires Focused Investment and Adaptability

Using AI, ML and robotic process automation can sound like silver bullets to speed up work processes. There are several use cases where you can implement standard AI solutions and achieve quick wins such as with chatbots. However, with more challenging use cases like extracting and digitizing unstructured data from paper documents, a blend of AI, ML and traditional technology approaches will likely be required. In these cases, it’s important to be prepared to fail fast. In more complex use-cases, we found the need to combine AI and traditional rules-based techniques to maximize results. It’s also important not to underestimate the effort required to train AI models. In our experience, the long pole in the tent is to create training data that may require tedious annotation efforts upfront.

Never Underestimate the Power of Accurate Data

Another challenge is one that I’m sure all of us can appreciate – getting clean, quality data. The number of potential data points in the real estate industry is mind boggling: tax records, work orders, printed materials, etc. Raw, siloed data does little to enable insight-based decision-making and augmented intelligence. It’s a huge undertaking to harness copious amounts of data and turn it into something insightful that you feel comfortable basing business decisions on.

To ensure we had reliable, accurate data, we embarked on a mission to build an Enterprise Data Platform (EDP), a central location to gather, harmonize and store data. EDP is a first-of-its-kind data platform, which transforms 36 billion (and growing) data points from 300 global sources of information to provide insights for our clients. We connect and transform real estate data, embedding analytics and AI into the very core of our client’s real estate strategy and related business processes. Each client is unique, so we meet them where they are. We work with their existing technology and data sources, connect them intelligently and efficiently transform this scale and breadth of data into actionable insights.

Getting EDP to this point required building an industry-first comprehensive commercial real estate data model. To ensure agility, the platform was built cloud native and cloud agnostic to support our clients no matter which cloud platform they operate on. Data management tools were deployed across hundreds of markets globally to collect quality local data. The platform now empowers our expert analysts to securely transform data at scale into intelligent insights.

Continue Growing and Transforming

Realizing the potential of AI, ML and automation to transform business operations doesn’t mean everything must be 100% automated. In one of our data extraction efforts, our target was a 40% reduction in the time it takes to process an unstructured document; we never envisioned taking humans out of this process entirely. Technology can give people leverage to work on more complex tasks, businesses the agility to respond quicker to changes in the market and more. With the right level of effort, you can realize the benefits of implementing technologies like AI, ML and automation.

The industry’s top-of-mind topics, such as AI, machine learning and many others, will be part of the education program presented at Realcomm | IBcon 2022. The conference will be held on June 15-16 (Golf Outing: June 13 | Precon Events: June 14) at the Orange County Convention Center in Orlando, FL. Register today!

Sandeep Davé, Chief Digital & Technology Officer, CBRE Sandeep Davé is the Chief Digital & Technology Officer for CBRE. In this role, Sandeep leads the company’s digital strategy and all aspects of technology globally. He and his team provide the technology capabilities and infrastructure to support CBRE’s clients and professionals across the globe. Prior to his current role, Sandeep served as Chief Digital and Technology Officer for CBRE’s Global Workplace Solutions business with responsibility for digital enablement and transformation of the business.

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