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Climate Risk Moves from Background Noise to Strategic Advantage
In this FINTECH.TV interview from the floor of the New York Stock Exchange, EHAB CEO Josh Graham discusses how climate change acts as a "force multiplier" affecting every sector of the economy, and why businesses can no longer afford to ignore it. With 93% of construction projects now impacted by weather (up from just 45% in 2020), the cost of inaction is mounting rapidly across industries.
Graham explains how EHAB is helping construction companies, insurers, and financial institutions transform weather from an uncontrollable risk into a strategic advantage. The key? Embedding weather intelligence directly into everyday business decisions, not just for meteorologists, but for schedulers, procurement teams, operations managers, and financial analysts who make weather-impacted decisions daily.
Introducing WeatherWise Studio: EHAB's answer to businesses needing a data-driven approach to automate operations, financial decisions, and planning. Using AI and conversational interfaces, WeatherWise Studio acts as a "meteorologist in your pocket", enabling users to quickly structure custom weather signals, model operational impacts, and make proactive decisions without requiring specialized expertise. From optimizing construction schedules and procurement timing to building heating-cooling indices for financial modeling, WeatherWise Studio makes sophisticated climate intelligence accessible to any business user through simple conversation.
As financial firms increasingly recognize weather as a critical data point alongside traditional financial metrics, the momentum is building toward climate data becoming "business as usual."
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Full Transcript
FINTECH.TV - theIMPACT
Filmed at the New York Stock Exchange
JEFF GITTERMAN: Hi, I'm Jeff Gitterman. Thanks for joining us as we explore how to best take advantage of changing times and shifting markets. Today, I'm joined by Josh Graham, CEO and co-founder of EHAB, a weather intelligence platform helping infrastructure, insurance, and finance sectors turn extreme weather into better decisions. Josh, thanks for joining me.
JOSH GRAHAM: Thanks for having me, Jeff.
JEFF GITTERMAN: So we're on the floor of the New York Stock Exchange where interest rates, supply chains, markets, and tariffs are the talk of the day and the only thing that seems important when you're down here. And yet there's this kind of big gray elephant called climate risk that is looking down on the markets that the markets really don't like to take into consideration. Why should the global markets be more interested in what's going on with climate?
JOSH GRAHAM: Yeah, I think it's a really, really fascinating question. There's this thing that the Pentagon said many, many years ago, which is that climate change is a force multiplier. It affects every single different part of the economy and businesses in lots of different ways, and I think it's been kind of background noise for a very long time. It's of course been there, of course it's always been a cost, but it's now becoming something which is affecting enough businesses in enough ways that it's leaking into and filtering into all of the other aspects of how you procure, how you order things through the global supply chain, lots of different things. And I think that cumulative risk is now building to a point where it's completely crazy not to include it in the way that you think about things from a finance and insurance perspective.
And so I think we're really thinking about how can you embed weather decisions into everyone's jobs. Because you might have a weather specialist or a risk manager who it's effectively their job to deal with it. But they've got 101 other things going on. And yet there are so many decisions that are being taken across the business by lots of people who are just not meteorologists, who are not climatologists. And yet if they could just have a 5% better, 10% better decision making related to weather and climate and adaptation, then I think we'd all be in a much better place. So I think, for me, it's how do we embed this into the way that we do business just on a day-to-day basis.
JEFF GITTERMAN: Yes. To add a little bit of fuel to the fire, it all worked fine when weather was much more stable and insurance companies weren't walking away from risks all around the country. However, now we're in a different environment where weather patterns seem to be changing dramatically and insurance companies seem to be really good at backing away from that risk even before it happens, passing that risk on to the end business, the shareholder, and the markets. You guys started in the construction business. Talk to me a little bit about how you saw that data transposing into the financial markets and global markets and operations.
JOSH GRAHAM: Yeah, absolutely. I think, you know, to your point, the reason people are backing away is there's just too much uncertainty. And I think in construction, we've seen it, and now in other industries, we're starting to see it as well, which is that they don't really have a handle on genuinely how much weather risk they're facing. It's not been something that they've modeled in detail because they've never had to. But now, as we're working with construction companies and modeling out at a schedule level on every single project that they're taking on, actually, how much weather risk they have—first of all, it's a scary number and that's worrying them, which is a good reaction to have, right? They should be seeing the data and reacting to it by saying, 'We need to actually put a lot more importance on it.'
And the way I think that that's filtering into other sectors is we work closely with insurers. If we can help them understand better the risks that they're actually insuring, then it means that they may—okay, maybe they're not going to be cheap for premiums—but maybe they can just actually bring that coverage to the places where they are more comfortable and confident, because we've put a better shape to the problem. And so that's what we're really trying to bring to other industries as well. We want to give them tools to truly model their assets and how weather affects those assets, so that they can take just better operational decisions, but also so that data can be passed on to insurers and financiers, so they can get more confidence in how those risks are being dealt with as well.
JEFF GITTERMAN: So you think you could go from kind of background noise and uncontrollable noise of weather and climate change to really operationalizing it as a strategic advantage for companies?
JOSH GRAHAM: Absolutely. I mean, I think I was at a climate event last week and the thing that they kept saying was that you were only as resilient as your neighbor. And I think the more and more that we see businesses using better data to drive decisions in construction, or ports, or logistics, wherever that may be—you know that makes everyone more resilient. But I think those who do it first clearly are going to be able to operate with better margins, or going to have potentially better coverage on their insurance, or maybe get better rates on their loans, whatever that might be. Those who are doing and taking those actions first and have them in place the longest, and it becomes the most embedded in their businesses—of course, naturally—are likely to win in a scenario where weather is getting worse.
JEFF GITTERMAN: I want to just hear a little bit about the pushback. Because certainly in the U.S., you still get a lot of pushback about climate change even being something to be concerned about or a problem. You're dealing with it on the ground, though, where it's actually already been getting priced into operations and manufacturing and risk. Can you talk a little bit about the reception that you're getting out of the industry as weather becomes more volatile and less predictable?
JOSH GRAHAM: Yeah, absolutely. I think a really fascinating statistic is that in about 2020, about 45% of construction projects were being impacted and delayed by weather. And now that number is 93% in 2024. So, you know, last year's data. That's a pretty steep jump. And so, look, if you're a scheduler and you're managing two, three construction projects, it's your reality. It's literally your reality. And if you were to not take it into account, regardless of your political affiliation, it just wouldn't make sense for your business.
So I think there's a real hard cost now that is being demonstrated through the things that are happening in the world, and we all know that being reactive is not very cost-effective. So therefore, the only option is to try to be as proactive as possible. And we're not going to get everything right. You can't forecast the weather perfectly and therefore avoid it. There is more risk. There is going to be more cost. But if you can be proactive in the way that you deal with that in terms of mitigation or changing the way that you schedule your project or whatever it might be—routes for logistics—then I think you can keep that number much lower than if you were doing nothing.
JEFF GITTERMAN: It's amazing because in the markets, they use forward-looking data sets on everything to talk about political risk and all kinds of market-related risks that, at best, historically has had less than a 50% rate of being right on those predictive analytics. And yet the markets eat all that data up. Climate data, which has been incredibly more accurate than any of the financial models that are used by a lot of the big risk firms, has been the last thing to be adopted. Always an interesting point to me that I just can't understand. I guess because it became a political issue rather than staying as a business and market risk, which would have probably made it get adopted much more quickly.
Talk to me a little bit about the fact that you look at traditional financial metrics and now you're looking at these companies that you're seeing on the street—look at weather as just as important as a traditional financial metric—and how are your data sets pulling that in?
JOSH GRAHAM: Yeah, I think it's really interesting. Just take agriculture, for example. We all know that weather is going to impact my grapes or my soybeans or whatever it might be. But it's actually, I think, up to 70% for certain crops can be the effect of weather—a contributing factor towards its yield. And in many other industries, you know, it's between 15 and 20 percent of the total cost of something can be influenced heavily by weather.
And so I think, in terms of from a finance perspective, it's been really difficult, probably, to get granular enough information quickly enough when you are not necessarily a meteorologist. So some of the products that we're trying to bring in are around: how do you lower the barrier to entry? How do you make it easier? And that's what I think where AI comes in. Because, as we all know, it becomes quite easy to use a tool if all you have to do is talk to it, instead of learning that I have to click this button and click that button.
So for us, the data is there. But to get the data and then structure it into a way where you can genuinely get insights from it, regardless of the industry you're in, that's actually the tricky part. And so that's where I think for us, that AI piece and being able to have a conversation, start to structure things in the way that you need them or want them, and for it to also prompt you when it thinks that you're trying to do something and you haven't quite found the solution yet—that's really powerful.
JEFF GITTERMAN: Talk to us a little bit more granularly about what is this data that you're looking at. You can pick any—it could be agricultural. But like, where are you pulling the data from and what is that actual data that you're looking at?
JOSH GRAHAM: Yeah, absolutely. So we get weather data from all of the different models that are out there, from satellites, from weather balloons, from weather stations. And of course, the more data that you put into that system, of course, the more accurate the results are going to be.
There's a couple of things that we're trying to do differently. So first, we're not just focusing on forecasting—the next 14 days is really where forecast models are still accurate. We also have a longer-term model, which just looks at—instead of trying to say, 'Well, it's going to be seven degrees on the 10th of December 2029'—well, what's the probability that I can do my concrete pour on that day? Because if the temperatures are too low, I can't do my concrete pour, and there's a cost to then having to move that scheduled item.
So in construction, it's really about what are a load of different decisions we're going to have to make in the next 10 years of building this road or this nuclear facility or whatever it might be, and how can we optimize where things are scheduled and when we're going to be buying resources. Because of course I'm currently living in Canada—there's always a run on heaters in the winter, and the price spikes as soon as it starts snowing. Because of course everyone needs to go out and use them on their construction sites. So imagine if you could just pull that decision to buy those heaters a few days earlier, or have started to stockpile a certain number of orders, because you know winter is moving back by two weeks every year or whatever it might be, right?
And so I think embedding the data into the decision process is really the piece that we're trying to do. And it's in construction at least, and in port and shipping, it's been embedding that with an API into existing decision-making tools around schedule and risk that they already have. So again, to the point that we said earlier, so that they don't have to go into somewhere else—it's just embedded into the existing workflow that they have, but it gives them an edge in a certain decision that they're making.
JEFF GITTERMAN: Talk to me about the insurance industry and brokers and the kind of work you're doing with them. And it always seems like, at least on the forefront, since I've been looking at climate for a decade now, that insurance companies are always on the cutting edge of looking at it. Maybe there's a lot of help that they can get from companies like yours still.
JOSH GRAHAM: Yes, I think the interesting and fascinating thing for me is, of course, when it's an insurable risk—something indemnity risk—then they are going to be absolutely all over it. Modeling hurricanes, you know, they have all this data around NatCat. Fascinating as well to see that secondary perils, so floods and heavy rainfall events and heat and temperature and cold events, are becoming even more of a loss burden for the insurance industry.
And what's also interesting is that a lot of the uninsurable risk—so maybe the increased cost, the increased delay—is of course not traditional insured. So that's the space that we're really trying to help insurers get a better handle on, because you know, to some extent, they haven't been thinking about it because it hasn't been as much of an issue. But this is where the broker platform comes in, which is just trying to help—because brokers sit in the middle. They're the ones who have the relationships with both parties. And so they use our platform to first and foremost just help their clients better understand the types of risk they're facing, which is, you know, to the point I made earlier: just getting visibility in the risk.
Then, once we've assigned a value to it, the business can make a decision. Well, do we want to carry that risk ourselves or do we want to transfer it with one of these new parametric insurance products? And I think that's becoming a much more popular type of insurance. I'm pretty sure that it has been around for many years. It's been kind of waiting its moment, perhaps, and I think, for us at least, we're hoping that by giving that better visibility and by tying the risk more keenly to the operations of a business, by letting them upload information—that's what's going to help unlock better coverage and ultimately cheaper rates in the parametric space because the risk exposure will be just much better understood.
JEFF GITTERMAN: So we talked about insurance and brokerage. Let's step into the heart of where we are today, the New York Stock Exchange and financial firms. Are you seeing any uptake of financial firms starting to look at these types of data sets? And in what ways are you starting to see adoption there?
JOSH GRAHAM: You know the funny thing, Jeff, is that we hadn't been thinking about it whatsoever. That might be surprising to hear, but until the beginning of this year, when we just started getting basically inbound leads, phone calls from people in finance just saying, 'Can we use your model? How can we use your data?' We kind of thought, okay, that's never happened before. And so we started having conversations with these people.
We saw it with some firms who have made a big play in the weather data space and that's paid off for them. Certainly ICE, who owns the New York Stock Exchange, has made a big play in it. And I think a lot of people are taking notice of that. And so, of course, there's loads of firms out there, right? Don't get me wrong. You can buy weather data from so many places. You can either get it for free and start structuring yourself, or you can buy it from certain providers.
But I think that going back to that AI and conversational piece, that's what we're now getting into the hands of finance folks who probably traditionally are using Bloomberg Terminal and various things where there is, again, lots of data. But it's, how do I structure exactly the type of signal that I really want? If I want to build a heating and cooling days index for North America, it's probably going to take me quite a long time and a lot of effort, whereas with the AI, you just type it in, you ask the question, and a little bit of back and forth, and then you structure the signal exactly, pulling the type of information that you need or that you think you need. And you can back test it with historical data and of course you then get the forecast as well.
So I think it's the ease of use and trying to have the AI be something which is kind of like a meteorologist in your pocket—almost—that can provide that assistance on a day-to-day basis.
JEFF GITTERMAN: Also, you guys are crowdsourcing data from industries like shipping and construction. How is that helping in the work that you're doing?
JOSH GRAHAM: Yeah, I think everyone's got weather data, certainly, right? And we need more data, absolutely. More balloons, more satellites, please. That's, you know, hopefully we can get that. But it's also, you know, what we're trying to model is not just, 'Oh, weather happened or weather is going to happen.' And what is the impact of that? And I think 'what is the impact of that' is perhaps a grey area that hasn't been so focused on.
Of course you can say this is my threshold at which I can't pour concrete. This is my threshold at which I can't lift cargo off a ship. But the reality on the ground can be, it's more likely that there's a range. There's an upper and lower limit of operational effectiveness. It might not just be the wind, but it might be wind direction and gust speed that also combines together.
And so it's that kind of metadata, which we can crowdsource from our users, who are using it from an operational perspective and saying, actually, I want to change my threshold slightly lower because that's going to give me a better warning. Using some of that information to ultimately generate better signals that give us a better understanding of how weather truly impacts the activities that are modeled in the platform.
JEFF GITTERMAN: So let's take a peek into the future. I mean, you're using AI, you're using large language modeling. What do you see, two, three, five years out from now, in the world of climate risk being adopted by the financial industry, the insurance industry? What's your crystal ball telling you?
JOSH GRAHAM: You know what, I honestly get the sense that there is a momentum building with these industries where they're genuinely taking it more seriously and thinking that it's something that needs to be a go-to business as usual. One of many data points where they make decisions, but of course, a super important data point that influences the outcomes that they're going to have.
I think I have to say that I think AI will play a role there because it will mean that you don't have to be that expert meteorologist or you may not have to hire as many meteorologists or climatologists into your team. You know that you can first triage the problem with a conversation with your AI. Which, if there is some more complicated problem, of course, then you probably may need to go to an external consultant or whoever it might be.
But I just think, you know, we can see across all different parts of the economy where you start to just have this assistant in your pocket that gives you just improved decision-making capabilities. Because it's plugged into all these data sets that you could go find yourself, and you could go structure and build yourself. But you wouldn't have the time for a decision you need to make now or tomorrow. And so it's just going to give you that ability to improve that speed of decision making, which is essential.
JEFF GITTERMAN: It's going to be interesting to see if the markets can keep up with the data. That's going to be the interesting thing, because the data, as you said, is getting faster and faster. We're going to have agent AIs where someone's always telling you what your risks are going forward. The interesting thing will be how does the stock market absorb that here on the floor of the New York Stock Exchange. So you'll have to come back in the future and we'll see how these things keep changing rapidly. But it's been a pleasure to have you on the show. Thank you so much. Good luck with everything you're doing.
JOSH GRAHAM: Appreciate it. Thanks.
JEFF GITTERMAN: Thanks again for spending time with us as we continue looking at new solutions and innovative ideas always from the floor of the New York Stock Exchange.
