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Using data, AI, and cloud to transform real estate

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Using data, AI, and cloud to transform real estate


Sandeep: Sure. Using an example is great because this is such a wide field, both commercial real estate and the application of AI/ML in commercial real estate. In the area of smart buildings, we are focused on enabling three outcomes for our clients: energy, efficiency, and experience; which is how do they manage their energy usage, how do they get more efficient in everything that they do with respect to managing a property? And then what is the workplace experience for the employees in a building?

And let me just take an example of efficiency. There was a certain way in which buildings were managed previously. And with the application of cloud native global technology solutions, that we have that are infused with AI/ML, we are now able to manage facilities in a smarter manner, what we call Smart FM. We are able to look at occupancy and dynamically clean the environment rather than having people cleaning the environment on a regular schedule, we are able to save our clients a lot of money with respect to dynamic cleaning. We are able to detect anomalies in how we manage buildings and assets, which can then further reduce the false alarms and the number of truck rolls that need to happen with respect to managing a building. So there are so many different ways in which we infuse AI/ML.

Laurel: That’s really interesting. So according to a 2019 International Energy Agency global status report, the real estate industry contributed 39% of global carbon emissions. Could you offer us an example of how smart technologies, like what you’re talking about now, could boost operational efficiencies and then also help reduce emissions and improve sustainability?

Sandeep: Yeah, absolutely. I think there are two ways in which we look at this space. As you indicated that 39% of carbon emissions are contributed by real estate, and so therefore the industry has a huge role to play. Part of those emissions are at the time of construction itself, and the remainder is for the life cycle of the asset. Right at the time of construction, we’ve built capabilities where we are able to design and redesign based on a certain energy emission target for a building. We are able to select our suppliers based on a certain energy emission target for the building.

And then at the time of managing the building, there are many solutions that offer instant gratification, stick sensors up, light up a building, and they all work well if all you need to do is to light up a building. But in order to meet the scale and the global net-zero targets that our clients have set, our solutions need to be at portfolio scale and need to be multidimensional.

And so therefore what we do is we have the ability to ingest data from various different sources, from sensors, and are able to harmonize that and land it against a standard taxonomy. And then we are able to assess that in many different ways. We are able to bring together different aspects of looking at energy and looking at occupancy and managing the building based on the occupancy in the building. Those interventions, for example, at one of our clients recently, meant we were able to stand up those interventions at 25-plus buildings. And that led to a reduction in peak usage energy for them and also reduction in reactive maintenance work orders, reducing truck rolls, and supporting their energy goals.

Laurel: So you also are talking about this on a portfolio level. And CBRE’s own corporate responsibility and environmental social and governance or ESG goals are as follows: scale to a low-carbon future, create opportunities for employees to thrive through diversity, equity, inclusion initiatives and to build trust through integrity. How is CBRE using emerging technologies like artificial intelligence and machine learning to then become more efficient and also meet those ESG goals?

Sandeep: I think a lot of the ESG problem is a data problem. Today, if you talk to most who are trying and most are grappling with this problem right now, what they’ll say is that do they have a clear line of sight of what their, for example, scope 1 and scope 2, scope 3 emissions are? Are they able to capture the data in a reliable manner, audit it in a reliable manner, and then report against it? While they report against it, can they also manage usage? Because if you are able to look at the data, then you will know where corrective actions are required. Building on the foundation of the data platform that we’ve built on, which is 100% cloud native, by the way, we can then, on top of that, apply these technologies where we can apply ML models to detect anomalies. We take a digital twins perspective to map our data against the buildings and manage the end-to-end lifecycle of that real estate process.

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The hunter-gatherer groups at the heart of a microbiome gold rush

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The hunter-gatherer groups at the heart of a microbiome gold rush


The first step to finding out is to catalogue what microbes we might have lost. To get as close to ancient microbiomes as possible, microbiologists have begun studying multiple Indigenous groups. Two have received the most attention: the Yanomami of the Amazon rainforest and the Hadza, in northern Tanzania. 

Researchers have made some startling discoveries already. A study by Sonnenburg and his colleagues, published in July, found that the gut microbiomes of the Hadza appear to include bugs that aren’t seen elsewhere—around 20% of the microbe genomes identified had not been recorded in a global catalogue of over 200,000 such genomes. The researchers found 8.4 million protein families in the guts of the 167 Hadza people they studied. Over half of them had not previously been identified in the human gut.

Plenty of other studies published in the last decade or so have helped build a picture of how the diets and lifestyles of hunter-gatherer societies influence the microbiome, and scientists have speculated on what this means for those living in more industrialized societies. But these revelations have come at a price.

A changing way of life

The Hadza people hunt wild animals and forage for fruit and honey. “We still live the ancient way of life, with arrows and old knives,” says Mangola, who works with the Olanakwe Community Fund to support education and economic projects for the Hadza. Hunters seek out food in the bush, which might include baboons, vervet monkeys, guinea fowl, kudu, porcupines, or dik-dik. Gatherers collect fruits, vegetables, and honey.

Mangola, who has met with multiple scientists over the years and participated in many research projects, has witnessed firsthand the impact of such research on his community. Much of it has been positive. But not all researchers act thoughtfully and ethically, he says, and some have exploited or harmed the community.

One enduring problem, says Mangola, is that scientists have tended to come and study the Hadza without properly explaining their research or their results. They arrive from Europe or the US, accompanied by guides, and collect feces, blood, hair, and other biological samples. Often, the people giving up these samples don’t know what they will be used for, says Mangola. Scientists get their results and publish them without returning to share them. “You tell the world [what you’ve discovered]—why can’t you come back to Tanzania to tell the Hadza?” asks Mangola. “It would bring meaning and excitement to the community,” he says.

Some scientists have talked about the Hadza as if they were living fossils, says Alyssa Crittenden, a nutritional anthropologist and biologist at the University of Nevada in Las Vegas, who has been studying and working with the Hadza for the last two decades.

The Hadza have been described as being “locked in time,” she adds, but characterizations like that don’t reflect reality. She has made many trips to Tanzania and seen for herself how life has changed. Tourists flock to the region. Roads have been built. Charities have helped the Hadza secure land rights. Mangola went abroad for his education: he has a law degree and a master’s from the Indigenous Peoples Law and Policy program at the University of Arizona.

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The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan

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The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan


Over the last couple of decades, scientists have come to realize just how important the microbes that crawl all over us are to our health. But some believe our microbiomes are in crisis—casualties of an increasingly sanitized way of life. Disturbances in the collections of microbes we host have been associated with a whole host of diseases, ranging from arthritis to Alzheimer’s.

Some might not be completely gone, though. Scientists believe many might still be hiding inside the intestines of people who don’t live in the polluted, processed environment that most of the rest of us share. They’ve been studying the feces of people like the Yanomami, an Indigenous group in the Amazon, who appear to still have some of the microbes that other people have lost. 

But there is a major catch: we don’t know whether those in hunter-gatherer societies really do have “healthier” microbiomes—and if they do, whether the benefits could be shared with others. At the same time, members of the communities being studied are concerned about the risk of what’s called biopiracy—taking natural resources from poorer countries for the benefit of wealthier ones. Read the full story.

—Jessica Hamzelou

Eric Schmidt has a 6-point plan for fighting election misinformation

—by Eric Schmidt, formerly the CEO of Google, and current cofounder of philanthropic initiative Schmidt Futures

The coming year will be one of seismic political shifts. Over 4 billion people will head to the polls in countries including the United States, Taiwan, India, and Indonesia, making 2024 the biggest election year in history.

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Navigating a shifting customer-engagement landscape with generative AI

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Navigating a shifting customer-engagement landscape with generative AI


A strategic imperative

Generative AI’s ability to harness customer data in a highly sophisticated manner means enterprises are accelerating plans to invest in and leverage the technology’s capabilities. In a study titled “The Future of Enterprise Data & AI,” Corinium Intelligence and WNS Triange surveyed 100 global C-suite leaders and decision-makers specializing in AI, analytics, and data. Seventy-six percent of the respondents said that their organizations are already using or planning to use generative AI.

According to McKinsey, while generative AI will affect most business functions, “four of them will likely account for 75% of the total annual value it can deliver.” Among these are marketing and sales and customer operations. Yet, despite the technology’s benefits, many leaders are unsure about the right approach to take and mindful of the risks associated with large investments.

Mapping out a generative AI pathway

One of the first challenges organizations need to overcome is senior leadership alignment. “You need the necessary strategy; you need the ability to have the necessary buy-in of people,” says Ayer. “You need to make sure that you’ve got the right use case and business case for each one of them.” In other words, a clearly defined roadmap and precise business objectives are as crucial as understanding whether a process is amenable to the use of generative AI.

The implementation of a generative AI strategy can take time. According to Ayer, business leaders should maintain a realistic perspective on the duration required for formulating a strategy, conduct necessary training across various teams and functions, and identify the areas of value addition. And for any generative AI deployment to work seamlessly, the right data ecosystems must be in place.

Ayer cites WNS Triange’s collaboration with an insurer to create a claims process by leveraging generative AI. Thanks to the new technology, the insurer can immediately assess the severity of a vehicle’s damage from an accident and make a claims recommendation based on the unstructured data provided by the client. “Because this can be immediately assessed by a surveyor and they can reach a recommendation quickly, this instantly improves the insurer’s ability to satisfy their policyholders and reduce the claims processing time,” Ayer explains.

All that, however, would not be possible without data on past claims history, repair costs, transaction data, and other necessary data sets to extract clear value from generative AI analysis. “Be very clear about data sufficiency. Don’t jump into a program where eventually you realize you don’t have the necessary data,” Ayer says.

The benefits of third-party experience

Enterprises are increasingly aware that they must embrace generative AI, but knowing where to begin is another thing. “You start off wanting to make sure you don’t repeat mistakes other people have made,” says Ayer. An external provider can help organizations avoid those mistakes and leverage best practices and frameworks for testing and defining explainability and benchmarks for return on investment (ROI).

Using pre-built solutions by external partners can expedite time to market and increase a generative AI program’s value. These solutions can harness pre-built industry-specific generative AI platforms to accelerate deployment. “Generative AI programs can be extremely complicated,” Ayer points out. “There are a lot of infrastructure requirements, touch points with customers, and internal regulations. Organizations will also have to consider using pre-built solutions to accelerate speed to value. Third-party service providers bring the expertise of having an integrated approach to all these elements.”

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