While the promise and transformative capabilities of generative AI use cases may be enticing, implementation of the technology can be a difficult balancing act, with firm-wide strategies around data, operations, and talent typically being the cornerstones for an integration strategy. Specifically, real estate firms should ensure they take into account the following factors:
Data strategy and model validation
“Location, location, location” is no longer the only determinant of strategic advantage in real estate; firms increasingly realize that “accurate, timely, and comprehensive data” holds the key to building a competitive edge. This is especially the case as emerging technologies, including generative AI, revolutionize the way we interact with data. Building an enterprise-owned, differentiated data set and making data-driven decisions can be the distinctive features that may set the firm apart from its competitors.
Foundation models like LLMs are trained on generic information that can be found online. However, real estate use cases will likely require training data to include market-specific, enterprise-specific, and asset-specific information to reduce the risk of hallucinations and bias in the models. However, the lack of publicly available information on leasing, tenant data, or operating performance of individual assets makes it potentially difficult to access timely and quality information at volumes sufficient to train these models.
Before venturing into the generative AI journey, real estate firms should assess the overall AI maturity of the organization’s technical infrastructure and consider whether it currently has access to the quality data required to fine-tune and train models. Those responsible for the transformation should assign leaders to defined roles, including data governance, quality, and ethics. Firms should choose between data governance frameworks to help ensure data trustworthiness, protection, and compliance.
Generative AI relies on foundation models that train on substantial amounts of both structured as well as unstructured and unlabeled data. Generative AI applications can leverage self-supervision techniques, reducing the need for annotation costs.22 The front-end applications and prompts are also more user-friendly and natural language can be used for interactions, making the technology more democratized and accessible across an organization.
Factually incorrect data or outdated information can lead to misleading outputs and result in reputational or financial risk, including legal exposure. Model outcomes can potentially degrade over time if real-world shifting dynamics and patterns are not incorporated into the model, or if the training data is not representative and diverse. Building explainability into models on why and how they come to specific conclusions, validating models on a regular basis, and providing avenues for human feedback into AI models are all important to reduce statistical errors and better understand model predictions.23
Organizational culture
Firms should consider a well-thought-out roadmap with clearly defined goals and milestones for effective generative AI adoption. Management should identify and prioritize high-impact business use cases, size the generative AI value opportunity, and bring employees along the value-creating exploration. Before making a significant investment in any solution or technology, it could be advantageous to first review proofs of concept to help ensure its feasibility and plausibility. Instead of sprinkling use cases across business units, embedding a strategy that weaves across enterprise-wide applications may offer some competitive differentiation.
Firms should also remember that financial KPIs are not the only indicators of success in generative AI technology adoption. Still, nonfinancial metrics such as increased new tenant acquisitions or reduced wait times in property maintenance and tenant satisfaction, cross-selling services, or time saved in payment fulfillment can also be critical indicators for success.
The human influence
Depending on the approach, whether relying on external partners or codeveloping AI solutions in-house, firms should assess the requirements for a skilled workforce, the emergence of new roles within the organization, such as prompt engineers or fine-tuning experts, and jobs made redundant with technology integration. Teams should work as copilots, wherein the humans work alongside the technology.
Risks associated with these models may also call for upskilling and reskilling of emerging roles and teams, including compliance, ethics, and data governance. For instance, a generative AI application deployed for project or construction management will likely require a specialist with domain experience in project management to first curate a reliable database with a diverse information set to help ensure compliance and job safety onsite and to avoid project delays. A generative AI model could also propose building designs that may sound feasible in a virtual space but impractical when considering real-world fabrication or zoning or regulatory compliance, which could have been prevented with the involvement of sector specialists. Keeping humans at the center of AI decision-making can help yield more realistic outcomes and reduce bias or hallucinations.
