CBRE's AI journey: Shaping the future of real estate

Despite promising applications and benefits, integrating Artificial Intelligence (AI) into the real estate sector presents challenges. These range from capturing and utilising appropriate data to educating and empowering employees. Companies implementing AI must also keep up with technological advancements and safeguarding requirements. While this may seem daunting, it is not insurmountable. At CBRE, we have started to integrate AI into our business to enhance operational efficiency and help us to make better informed decisions. AI has already made significant improvements to efficiencies and decision-making at CBRE, and we are on a journey to embrace its transformational impacts.

At CBRE, we aim for employees to leverage AI benefits regardless of their technological capabilities. To this end we developed Ellis AI; an AI co-pilot for CBRE. Ellis AI automates routine tasks, proofreads, and analyses documents, and summarises articles and minutes to extract key findings. Using large language models (LLMs) directly involves risks such as leaking proprietary information. Whereas Ellis AI ensures that prompts and documents remain secure, saving employees vast amounts of time and allowing them to focus on more value-added tasks.

Other applications include AI-powered dashboards like ThoughtSpot, which streamlines data retrieval and analysis, answering specific queries about data. These dashboards provide real-time analytics and actionable insights, ensuring decision-makers have accurate and up-to-date information at their fingertips. Everyone is empowered to generate data insights and can do so in a conversational way by using natural language processing to query the data.

There are also more sophisticated uses. For instance, CBRE’s lease extraction engine leverages AI and machine learning algorithms to automatically extract and standardise data from lease documents, significantly reducing the time and effort required for manual data collection. This enables faster and more accurate reporting and flags discrepancies and potential issues within lease agreements, supporting a proactive approach to lease management.

We also use AI technology to streamline and improve our facility management operations. Our tool Smart FM leverages AI through an application known as ‘Service Insight’—a computerised maintenance management system (CMMS) that uses advanced algorithms to analyse data from historical records and real-time inputs from IoT devices. This tool can predict potential system failures, such as determining when an air conditioning system might fail based on usage patterns and maintenance history. This enables better maintenance planning and response, enhancing operational continuity and efficiency.

During our journey we have learnt various lessons, from the critical importance of good data management at the micro level to the macro change management strategy that integrating AI requires. Here are some key considerations for other businesses:

AI and Data

The effectiveness of AI depends on data quality, which in turn will help us get data driven insights. Ensuring accurate, clean, and well-integrated data is a foundational step in harnessing AI’s full potential. Different types of data require different procedures for collection, collation, and cleaning. Data can also exist in silos and need to be unified. Another consideration is where the data is stored. To leverage AI technology effectively, data needs to be held within modern architecture to ensure scalability, performance, security, integration, reliability, and availability. As we become more reliant on data, robust data management practices are essential to ensure accuracy. Additionally, using non-biased datasets when training models is crucial to prevent existing biases and ensure fair outcomes. Human validation and judgement are often necessary, especially in high-stakes scenarios.

Data privacy and data security has been a critical concern for incorporating AI at CBRE. Ensuring compliance with regulatory requirements, protecting client information, and retaining control of proprietary data have been paramount. We have implemented strict controls and regular audits to maintain data integrity and security.

For example, Ellis AI operates as a "pond" connected to a larger "ocean" of data from an AI provider. A sophisticated data isolation mechanism ensures that while data can flow into the pond, it cannot flow back out, safeguarding CBRE's proprietary and client data. This data isolation is essential for protecting sensitive information while leveraging AI's power.

Technological Expertise

We recognise that we are primarily real estate professionals, not AI specialists. To harness AI's full potential, we collaborated with leading AI providers. These partnerships enable us to access cutting-edge technology and integrate it into our solutions.

Scaling Up

With robust data established we can apply the technology, but the range of potential use cases can be overwhelming. It can be challenging to know where to devote resources, particularly for large, business-wide projects that could ultimately yield the largest return on investment opportunities. Experimentation is a core pillar of our AI strategy. Our approach was to start with small proof-of-concept projects to validate effectiveness before scaling up. Starting with manageable projects to demonstrate value allows for refining and adapting the concept based on feedback, market trends and unforeseen nuances. It also ensures robust data quality and integration practices are in place before broader implementation. This phased approach ensures substantial ROI and operational effectiveness when expanded.

Our AI strategy is built on four pillars: education, experimentation, execution, and ethics. Education involves training our employees to understand and utilise AI technologies. Experimentation encourages innovative thinking and trial projects to test AI applications. Execution focuses on implementing successful AI projects on a larger scale. Ethics ensures that our use of AI aligns with regulatory standards and ethical considerations, protecting client data and maintaining trust.

Adaptation and Change Management

AI implementation within an organisation requires significant cultural and behavioural change. Employees might resist adopting new technologies due to fears of job loss or scepticism about AI's reliability. Reducing resistance is vital for successful AI integration and effective change management, which includes investing in skills development and addressing behavioural concerns.

CBRE focuses on educating employees about AI's benefits and providing training for smooth adoption. We established an AI Champions Network to encourage employees to use AI in their daily tasks and support them in understanding and leveraging AI tools effectively. This initiative fosters a culture of innovation and adaptability, easing the transition towards more AI-driven workflows. Transparent communication about AI's impacts has built trust and mitigated fears.

Reflecting on CBRE’s journey, it is clear that AI has transformational potential. Combining technological prowess with human insight will enable the real estate sector to navigate challenges and realise it. CBRE’s ongoing innovation suggests that even more sophisticated solutions are on the horizon. These advancements will improve real estate operations' capabilities, promising to enhance efficiency, preparedness, and responsiveness to client needs. These improvements will streamline operations and set new industry benchmarks, ensuring AI's practical applications at CBRE drive real value and sustainable growth for the real estate industry.