Sandeep Davé understands the value of experimentation as well as anyone. As chief digital and technology officer at CBRE, Davé recognized early on that the commercial real estate industry was ripe for adoption of AI and machine learning enhancements, and since then, he and his team have been testing numerous use cases.
These experiments have paid off. Over time, CBRE has successfully reduced manual lease processing times by 25% and reduced false positives at managed commercial facilities by 65% by leveraging machine learning and AI. CBRE also uses AI to optimize portfolios for multiple clients and recently launched a self-service generative AI product that allows your employees to interact with CBRE and external data in a conversational manner.
Recently, CBRE announced a major milestone: the deployment of CBRE’s AI-enabled Smart Facilities Management Solutions at more than 20,000 Global Workplace Solutions customer sites, totaling 1 billion square feet. Even so, Davé said "we're still in the early days" when it comes to artificial intelligence.
Davé and his team’s achievements in the field of AI are largely due to creating opportunities for experimentation and ensuring that these experiments are consistent with CBRE’s business strategy. While many CIOs may still be wondering how to get started on their organization’s AI journey, Dave’s work at CBRE shows that driving experimentation, even when there may be failures, can lead to huge successes.
Here’s Davé’s take on how to make AI experiments work profitably for CBRE, and his advice for IT leaders looking to do the same in their organizations.
Many organizations are eager to deploy AI, so use cases need to be defined and sequenced first. But those who want to succeed in AI know that training data is key. So a better approach might be to build a data foundation and give employees time to take the lead in exploring possibilities.
When Dave and his team realized the potential of large-scale data, they began to implement this plan. CBRE holds vast amounts of transaction data, as well as vast amounts of asset intelligence generated from sensors, workflows, and billions of square feet of physical space it manages globally. Through this early work, they successfully automated business areas such as leasing abstraction or work order classification.
While the hype around generative AI was heating up, the CBRE team developed a multi-large language, The self-service generative AI platform enables employees to use generative AI to perform a range of tasks, such as gaining insights from proprietary data and documents, using chatbots to solve various problems, generating new content and transforming forms, etc. Davé said that through widespread use of the platform, "we've generated interest and attention across the organization, [the product] now has hundreds of users and growing every week, and it's unlocked a lot of productivity," adding Laying the foundation for more innovation across the company.
Despite this, Davé still emphasized the importance of AI safety restrictions. He said: “There is a lot of caution in how [AI] is used and how to educate users, human intervention is still necessary and verification is necessary. It is important to be aware of technical limitations (e.g. hallucinations) as well as legal obligations on how customer data is used. ”
Once you’ve given your employees the time and resources to experiment, and you’ve got great ideas, pick the best opportunities to Realized, the key is to separate the glitz from the substance. “We see a lot of initiatives that are done for the sake of technology and technology leads to failure,” Davé said. He suggested two ways to avoid this mistake: Set up a prioritization model that is consistent with business strategy and strategic partnerships.
Starting with a model, Davé and his team adopted a simple and age-old method of filtering use cases: plotting them in a two-dimensional grid with "value" and "feasibility" as the axes . Davé started with high-value and high-feasibility cases and quickly achieved success, thereby igniting stakeholder excitement and recognition. “These technologies have the greatest potential because they often leverage data that we have access to and are already leveraging,” he said. “With AI, many of these technologies can drive productivity and eliminate manual and repetitive processes.”
Next, Davé focused on two quadrants: “high value, low feasibility” and “low value, high feasibility”. The choice depends on their goals, which require a choice between easy results and significant investment. For artificial intelligence, the high-value quadrant is where the most predictive models can be found. “While it’s not easy, if you do it right it can have a huge impact,” said Davé, adding that IT leaders should consider choosing a use case from these two quadrants: one that is high value, One is highly feasible. This way, your team can demonstrate early results and provide momentum for larger initiatives
While this value-feasibility matrix is great, it also has a serious drawback: unlike almost all prioritization models Likewise, this matrix suffers from ambiguity. After all, how do you assess the value and viability of use cases that rely on emerging technologies that are little-known, or require building functionality that may not yield immediate benefits? This is where partnerships can play a huge role in mitigating risk and shortening time to market.
Finding the right technology partner can greatly improve your assessment of value and feasibility. The best partners can leverage deep experience with their respective technologies and tools to ensure you don't underestimate use cases that are too difficult, nor underestimate any use cases that are successful quickly.
A great partner can help you create things you can't realized value. That's why partnerships have become an integral part of CBRE's strategy. Davé said: “We have always adhered to the concept of ‘Build-Buy-Partner’. We don’t have to do everything to accelerate time to value. We have identified a series of priority areas where we see CBRE as a Center for interesting AI innovations and identified potential partners for each area. Alison and her team have been instrumental in this."
Rewritten content: What he was referring to Bell, head of global digital and technology strategy acceleration and digital partnerships at CBRE. Bell and her team are committed to supporting many powerful features that many other companies are trying to build into the workplace. She and her team develop digital and technology strategies, research emerging technologies and businesses in the proptech space, and evaluate how to tightly integrate the best technologies and businesses into CBRE’s ecosystem." Bell said: " When you look at the partnerships or investments we make in the PropTech space, we partner or invest to capture strategic value. All of our partnerships or investments are focused on delivering on our core business and customer outcomes."
Through these strategic relationships, CBRE and its partners create something that they can neither build nor buy themselves—a symbiotic relationship in which both parties learn from each other and empower each other. Be more competitive and become more unique. Davé believes this is an evolving trend that will differentiate current digital leaders from those of tomorrow. "The traditional CIO role... is about execution, digital is very much about strategy and being a trusted business advisor, accelerating revenue growth and embedding technology that transforms the core business," he said.
##By integrating artificial intelligence into strategy-led operational workflows and combining it with a network of strategic partners that are deeply integrated with the data foundation, Davé, Bell and their team drive CBRE beyond cost cutting and some mundane ideas and move toward more compelling innovations. This capability will serve them well as new technologies emergeThe above is the detailed content of Real estate giant CBRE CDTO talks how to accelerate AI ambitions. For more information, please follow other related articles on the PHP Chinese website!
![[Web front-end] Node.js quick start](https://img.php.cn/upload/course/000/000/067/662b5d34ba7c0227.png)
