Davé and his team made did just that when they recognized the power of data at scale. CBRE has a vast amount of transaction data, as well as a plethora of asset intelligence from sensors, workflows, and human interaction with physical space within the billions of square feet the company manages globally. This early work has enabled automation in areas of the business such as lease abstraction or automating work order classifications.
With hype around generative AI exploding of late, the CBRE team developed a multi-LLM, self-service generative AI platform that enables employees to use gen AI for a range of tasks, such as gaining insights from proprietary data and documents, using chatbots to work through problems, generating new content, and translating forms. By making the platform widely available, “we’ve created an appetite and an interest across the organization,” Davé says. “[The product] has hundreds of users and is growing weekly. And it’s unlocking a lot of productivity.” It’s also setting the stage for more innovation across the company. When the technology is available in a self-service manner, “lightning can strike according to its own schedule.”
Still, Davé stresses the importance of safety restrictions when it comes to AI. “You’ve got to be careful how you use [AI] and how you educate your users,” he says. “Human intervention is still necessary. Validation is still necessary. And it’s very important to remain mindful of technical limitations — such as hallucinations — and legal obligations with respect to how we use client data.”
Choose use cases aligned with business priorities
Once you’ve given your people time and resources to experiment and you’ve captured good ideas, it’s time to select the best opportunities to pursue. Here the key is to separate the flashy from the substantive. “We’ve seen so many initiatives fail when it’s technology for technology’s sake,” says Davé, who suggests two means of avoiding this mistake: prioritization models aligned to your business strategy and strategic partnerships.
Let’s start with the models. Davé and his team filter use cases using a simple, time-honored method: plotting them in a two-by-two grid that takes as its axes “value” and “feasibility.” Davé starts with cases that are both high-value and high-feasibility for quick wins to generate excitement and buy-in from stakeholders. “These have the most potential because, typically, they draw on data we already have access to and that we’ve already made good use of,” he says. “In the case of AI, many of these are productivity enhancers. They eliminate manual and repetitive processes.”
The quadrant Davé’s team attacks next is either “high-value, low-feasibility” or “low-value, high-feasibility,” depending on their objectives. It’s a choice between low-hanging fruit and big investments. For AI, the high-value quadrant is where you’ll find most predictive modeling. “These are not easy, but they have a big impact if you get them right,” says Davé, adding that IT leaders should consider picking a use case from each of these two quadrants: one that’s high-value and one that’s highly feasible. That way, your team can demonstrate early results while helping to develop momentum for the larger initiative.
