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How AI Is Reshaping Corporate Sustainability · C-Suite Perspectives

• 0:00
  Welcome to C-Suite Perspectives, a signature series by The Conference Board.
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  I'm Steve Odland from The Conference Board and the host of this podcast
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  series, and in today's conversation, we're going to talk about AI and
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  environmental sustainability.
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  What are the risks?
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  What are the opportunities, and what comes next?
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  Joining me today is Dr. Andrew Jones, principal researcher
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  at The Conference Board's Governance & Sustainability Center.
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  Andrew, welcome, as always.
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  Thanks so much for having me, Steve.
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  A pleasure to be back once again.
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  Yeah, so you just released this new report just a couple weeks ago on
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  AI and the environment, and you did a great job of kind of outlining,
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  I guess, two different lenses here.
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  One lens dealing with the issue of how to make AI sustainable, and then the other
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  issue of AI used for sustainability.
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  Let's just talk through each one of these, Andrew.
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  Let's start with sustainable AI.
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  Give us an overview of what you learned in your study.
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  Yeah, sure.
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  Thanks, Steve.
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  And it's, yeah, it's a pleasure to be here and share more on this work.
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  Obviously, this has become such a timely issue, I think as AI has scaled, and
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  I think more stakeholders and more people in general are paying attention
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  to the environmental aspects of AI.
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  And as you just hinted at, there's multiple dimensions here, right?
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  Yeah, in our work, in our research, in our programs, and in our recent
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  report, we effectively break AI's environmental dimensions down
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  into these two lenses, right?
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  The first, as you said, is sustainable AI, managing and mitigating
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  AI's environmental footprint.
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  And the second, which I perhaps we'll come on to discuss later, is how
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  we can even use AI technology for positive environmental outcomes?
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  So I think in terms of the first lens, if you will, sustainable
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  AI, how do we understand and then, in turn, perhaps, manage and even
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  mitigate AI's environmental footprint?
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  I think this is a really fascinating area.
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  And I think we've more and more realizing that AI and the development, the
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  training, the running of AI models at scale can have quite big environmental
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  costs and environmental impacts.
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  Particularly, I think, around electricity consumption and water consumption.
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  And I think we can break down more what these mean in practice.
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  But I think the issue of electricity and energy is perhaps particularly interesting
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  as we're seeing data centers proliferate and scale at ever-accelerating rates.
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  So we did a lot of work, Steve, breaking down these different aspects and what
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  they actually mean in practice and what we might expect to see coming on the agenda
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  in a few years, and how that translates into C-suite and boardroom concerns.
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  And there are multiple aspects to AI's impact on the environment, essentially.
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  You've got, as you mentioned, a significant energy uptake.
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  There's also water, but then there are the emissions impacts.
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  Let's just take them one at a time.
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  They're building these data centers.
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  Well, we've had data centers for a long time.
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  The issue is on this one that these data centers, used strictly
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  for AI, consume multiples of the amounts of electricity than data
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  centers used for normal functions.
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  Talk about that.
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  Yeah, sure.
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  And it's worth saying, Steve, in our work, we did a survey of
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  corporate sustainability leaders.
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  And we asked them what aspect of AI's footprint concerned them the most.
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  And there was a lot of options, and very few of them weren't concerned
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  by something, but the top responses were pretty much data center, energy
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  demand and consumption, and the greenhouse gas emissions that can
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  potentially result from that energy.
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  So I think data centers are top of mind for a lot of people.
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  And as you highlighted, data centers have been around for a long time,
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  data centers really have been the backbone of cloud computing that
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  we've seen in the last few years.
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  But there's something different I think about the AI models.
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  And I think we all know that AI models, particularly when it's large
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  language models, general purpose, generative AI models, they're very
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  energy-intensive to train and run.
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  Training an AI model, particularly some of the more recent massive frontier models,
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  it consumes a vast amount of energy.
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  It really is brute force.
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  It can involve weeks or even months of nonstop computation, specialized chips
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  running in parallel around the clock, constantly calculating and recalculating.
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  And this, just in turn, obviously, it's very power-hungry.
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  It burns a lot of power, and that's the training of the model.
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  And then also then, the term we use is inference, when the model
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  is actually used by people, that in turn can also have a big energy cost.
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  A query for an AI model might be very small, in terms of the energy it uses, but
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  when you have millions or, even billions of users making requests, that triggers,
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  in turn, trillions of calculations inside a model that must happen instantly.
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  So AI is running on these very specialized chips, designed for speed.
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  They're drawing much more electricity than standard CPUs.
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  And that high performance and high demand means high electrical load.
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  And as AI models are getting larger and more scale, we're
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  just seeing exponentially higher energy use, and it's requiring,
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  in turn, a lot more data centers.
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  And we're seeing a lot of capital expenditure by companies
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  on data centers right now.
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  They're popping up around the clock, really, as the AI infrastructure
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  gets built for the next era.
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  Well, the big AI folks are Google and Microsoft.
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  Some of these companies are actually contracting with energy companies
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  to dedicate whole nuclear power plants to running their data centers,
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  speaking to what you're saying.
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  And so, if you put in a normal search, it's just a tiny little bit of data, and
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  also a tiny little bit of energy usage.
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  But when you put in an AI search, what you have is that search scrubbing
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  all, doing all these searches at the same time and then forming an answer.
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  So what used to be just a little tiny bit of energy used on a search now
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  has been converted to an AI search, and I think a lot of people just do
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  it because it's a nice answer and you don't have to go through websites.
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  But the amount, the multiples of energy being used is, for what used
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  to be just a little search, is huge.
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  And I think what you've just described, I guess it's, part of
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  the transformation that is about to come and we're starting to see in
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  AI, AI's transformation of business, but also just everyday life, right?
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  As more consumers use it to find products or more people use it to find the things
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  that they used to find through other ways.
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  It's a birth of a new era.
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  Behind that, there is this huge appetite for, almost an insatiable
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  appetite for, power and energy and electricity to drive it.
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  And yet, it's worth saying, Steve, this landscape does change quickly, right?
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  We're constantly seeing new chips and new technologies emerge that maybe
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  may be more efficient, may reduce the cost, may reduce the energy costs,
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  but even by increasing efficiency, it simply, still leads to more electricity
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  demand, and more efficiency enables larger models and more workloads, right?
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  So I think this trend is here to stay.
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  And we are going to see over the next, just the next few years, just
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  in the US, we're going to see a lot more data centers, a lot of
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  capital expenditure in this area.
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  And if these data centers are running on fossil fuel, heavy energy grids,
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  as they often are, that higher consumption is going to translate
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  into greenhouse gas emissions, which as we know can drive warming.
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  So there is a strong environmental aspect to all of this that I think is
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  becoming more and more harder to ignore.
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  Well, there's also the heat generated by the data centers,
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  and that has to be cooled.
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  It's a spiral of things that goes beyond.
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  And so, on one hand, they're adding a lot of capital to add
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  the infrastructure to do that.
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  And we have not expanded the electricity production capacity in this
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  country for, I don't know, decades.
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  Mostly because the incremental usage required was offset by the
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  savings through conservation.
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  The, the move to smart appliances, LED bulbs, and all that, but that has now
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  hit the wall, particularly with AI, and you're seeing this massive upsurge in it.
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  And so there's carbon aspects to that, as you said.
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  But then you also mentioned in the report the use of water.
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  What does water have to do with it?
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  So you saw that water is a major part.
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  It's interesting, because I think the electricity side of things, the energy
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  side of things, and the emissions that follow has been probably the
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  main environmental issue, I think, that's captured attention when
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  it comes to AI and data centers.
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  But water consumption also seems to be moving up the agenda.
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  I think we're starting to see a bit more attention on this.
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  As you highlighted, Steve, yeah, at big data centers, big AI-centric data
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  centers, water is used for cooling.
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  These chips are running very hot, and circulating water through is one way
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  to keep them at the right temperature.
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  The water that absorbs that heat is then transferred somewhere
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  else, and some of it evaporates.
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  And because salty water can corrode machinery, companies and data
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  centers are using potable water directly from municipal supplies.
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  So it requires a lot of water, to put it bluntly.
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  And as data centers scale, more water is required.
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  I do think, we say this in a report, we do need to keep this in perspective.
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  AI today actually only represents a very small share of total industrial water use.
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  Some studies, quite a few studies, like to point out that, for example, the amount
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  of water used to cool AI data centers is significantly lower than irrigating
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  golf courses or agriculture or so on.
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  So we should keep it in perspective, but water consumption is rising
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  as models become more complex and we build more data centers.
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  And it's a tricky and more complex issue, I think, because it's a
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  more local issue than perhaps emissions and electricity, right?
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  As data centers are often concentrated in very specific states and even within
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  very specific regions within those states.
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  And if they happen to be in locations that already have existing water stress or
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  existing water challenges, these increased water demands can be a big problem.
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  Water is municipal and community level, and often therefore, water withdrawals for
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  data centers can have big local impacts.
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  And we're starting to see those emerge, I think.
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  And we're starting to see debates emerge around this.
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  It's a very complex issue.
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  And I think another one that's here to stay.
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  Now I think this is moving up the strategic agenda, for sure.
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  Yeah.
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  And putting them in municipal areas, fight with water needs for
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  people and homes and all of that.
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  So the natural inclination is to say, well, just put them in rural areas.
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  However, that makes them compete with agricultural needs, which feeds the
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  country and secures national security.
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  And so it's a challenge regardless of where you put them.
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  But there's also this impact of the land use, and the spillover of that, too.
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  And you go to some of these areas where these data centers pop up.
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  I mean, these are not pretty buildings.
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  There are no windows.
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  They are just massive concrete bunkers, and they're using
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  acreage like you wouldn't believe.
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  That's a big impact.
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  It really is.
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  And I think is, perhaps, in our researching some of the land-use impacts
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  of AI perhaps are right now less of a concern for some of our sustainability
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  audience than the emissions or the water.
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  But I think rising up the agenda, and I think you're so right,
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  Steve, as we're seeing data centers get built out at scale.
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  And they require a lot of materials, and sometimes they can be very
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  carbon-intensive materials.
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  And they contribute to these local issues in all kinds of ways.
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  The rapid buildout, particularly, as you said, perhaps in rural areas or
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  semi-rural areas have triggered a lot of zoning debates, and have triggered
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  a lot of community, in some places, at least, some community pushback.
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  So I think this is something that the companies, and perhaps primarily
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  applies to the tech firms and the data center builders, but I think it
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  also applies to a lot of companies that are indirectly dependent on them.
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  It is something that has to be considered, and it is a risk, I
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  think, and a rising issue, for sure.
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  Well, there's sound issues, too.
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  A lot of these data centers emit high-pitched noises from
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  cooling fans and so forth.
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  So if you live near a data center, it's not dissimilar to windmills that have
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  bad bearings, but you're constantly dealing with this, it's either this
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  low hum or shrieking over time.
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  So, that's another whole thing with the local impact, as well.
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  There's also, as you pointed out in the report, e-waste and hardware turnover.
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  Talk about that.
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  Again, this perhaps might be one of those areas that's less recognized
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  in the broader discussions around AI.
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  But for sure, I mean, think AI, as you said, AI-centric data centers
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  are a little different, right?
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  In terms of the chips they use, the way they run, the way they operate, and
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  often they have quite an accelerated hardware refresh cycle, right?
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  So sometimes as short as sort of 18 months, 24 months, AI needs
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  to be turned over, and chips need to be turned over and refreshed.
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  It can create big volumes of electronic waste.
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  And not that many facilities, really in the US or globally, are
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  equipped to recover these sort of materials from high-density chips
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  and advanced memory at scale.
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  So I think e-waste, while right now it maybe isn't top-of-mind issue for
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  many people when it comes to AI's environmental impact, I think it
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  will rise, and it will become more of a sort of responsibility gap.
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  And I think there'll be more issue on recycling, and how do
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  we extend hardware life strategies?
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  How do we build resale markets and clearer end-of-life accountability?
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  I think all these issues are going to continue to rise and,
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  particularly, as we see the tech become quite obsolete quite quickly.
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  Yeah, all of that's a big concern.
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  You can't pick up a newspaper without reading about rare earth minerals.
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  And we have national security concerns with rare earth minerals.
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  We need them for the chips themselves.
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  We also need them for the batteries that go into all of this.
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  But that's another stress point on the environment because you either have to
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  mine them here, or you're mining them somewhere on Earth, which releases carbon.
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  And of course, all of the runoff and transportation issues with that.
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  So lots of rare earth issues, too.
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  That is so right.
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  AI hardware, right, it really does depend on a quite a narrow
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  set of critical minerals.
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  I know my colleagues at The Conference Board in the ESF Center have done some
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  fantastic work on critical minerals, often can be very environmentally
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  harmful to extract, can be concentrated in certain regions, can even be quite
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  geopolitically sensitive, right?
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  So I think this reliance creates a lot of potential risks.
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  And I think unlike electricity or water, these constraints are
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  maybe not immediately that visible.
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  They might not be halting projects or surfacing through community debates but
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  could represent, perhaps, a long-term environmental issue, a long-term scaling
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  issue, a long-term resilience issue, so the critical minerals aspect to
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  this is a really fascinating story.
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  And also, yeah, a potentially big environmental impact, as well.
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  Talking about the use of AI and the environmental
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  sustainability issues around that.
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  We're going to take a short break and be right back.
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  Welcome back to C-Suite Perspectives.
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  I'm your host, Steve Odland, from The Conference Board, and I'm joined
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  by Dr. Andrew Jones, principal researcher at The Conference Board's
• 13:56
  Governance & Sustainability Center.
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  So, Andrew, before the break, we were talking about all of the
• 14:02
  environmental or sustainability issues around the expansion of AI.
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  One of those you mentioned was the local areas where you see
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  the intensity of data centers.
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  For some reason, in Virginia seems to be the epicenter of that.
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  Also Texas, California, and to a lesser extent, Illinois.
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  Why Virginia, of all places, or Texas, of all places, for the
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  concentration of these data centers?
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  You're so right, Steve.
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  It's so interesting.
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  And at our latest count, we think there's about 4,000 data
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  centers across the entire US.
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  And about a third of these are in just three of the states you mentioned:
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  Virginia, Texas, and California.
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  Virginia alone accounts for about 15% of all data centers in the US, which
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  is a very remarkable concentration.
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  And a lot of them are actually within a specific area of Virginia, northern
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  Virginia, Loudon County, which is also known as "Data Center Alley."
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  So I think the way we have to understand this, Steve, is
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  it's not coincidental, right?
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  That data centers often are cited in strategic locations where they
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  can get good connectivity, reliable power, available land, proximity
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  to major demand, right climate and right environmental conditions.
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  And Virginia and Northern Virginia, I think, has emerged as a real global hub.
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  It's the largest data center in the market and the world, represents a
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  meaningful share of global capacity.
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  But we're also seeing concentrations elsewhere.
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  Dallas Fort Worth in Texas has grown rapidly.
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  And, of course, Silicon Valley in California.
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  I think there's reasons why this is, and obviously there's a lot
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  of positives to this, right?
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  They're in these regions creating huge amount of economic opportunity, tax
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  revenue new markets, and new customers.
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  But I think, from an environmental perspective, can also have obvious
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  significant impacts when you've got so many data centers in a specific place.
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  And the obvious one is straining local energy grids.
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  Yeah.
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  I've driven around Ashburn, Virginia, which is in Loudoun County, as you said.
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  They are everywhere.
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  And it's just really kind of eerie when you drive down a road and you see
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  data center after data center, and you can tell the difference between data
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  centers and any other kind of commercial building, cause there are no ports for
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  trucks, as you see with distribution centers, there's not a lot of activity.
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  It's all buttoned up and automated and high security, and there's
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  noise, as we said before.
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  Right, and it's, as you said, Steve, not a huge amount of people.
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  They don't tend to require massive workforces, they are highly automated.
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  It's more sort of a specialized, job.
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  But yeah, they are in these particular reasons, huge concentrations, and that
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  in practice, that means certain utility territories are facing big sort of load
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  demands, big reliability concerns when it comes to providing reliable electricity.
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  And as you know, this can have strong local and even political effects, right?
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  It feeds into debates around energy prices and energy affordability and
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  energy access and energy security.
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  So, there's a lot of consequences and a lot of implications to
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  this sort of rapid, almost even exponential, build out of data centers.
• 16:51
  Well, and we don't talk about it, there are national security concerns,
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  too, because the more we're reliant on those data centers, and the more
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  they're sitting in one place, has a big target on it for the country's enemies.
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  And Ashburn, Virginia, is about a stone's throw from Washington, DC,
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  and the defense suppliers are one of the big users of these data centers.
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  It also makes it a target.
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  We've been talking about the impact of AI on the environment,
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  let's go the other way.
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  Let's talk about all the positive things that AI can do for sustainability.
• 17:20
  Yeah, happy to, Steve.
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  I think, as we started out this podcast, we noted our work separates
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  out into these two lenses, looking at sustainable AI and AI for sustainability.
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  And we even go so far as to argue you have to look at these lenses together.
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  You can't just focus on the cost, the environmental costs and tradeoffs of
• 17:36
  AI without also acknowledging that AI itself, and the technology and
• 17:39
  the tools, can play a big role in actually addressing environmental
• 17:43
  challenges, being applied for positive environmental and sustainable outcomes.
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  I think normally AI, when you look at the underlying tech and what
• 17:49
  it really does, is well-suited to environmental challenges, right?
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  Because many environmental problems are, they're data heavy, they're complex,
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  they're dynamic, they're interconnected.
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  And AI often excels at detecting subtle patterns in messy data, which
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  makes it perfect for some of the big sustainability challenges of the day.
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  So I think AI has a lot of potential here, and I think corporate sustainability
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  leaders, while it's still perhaps relatively early days, this is still a
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  new technology, are starting to integrate AI more into their work and apply it.
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  Some of those big challenges we actually found in our survey that more
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  than 60% of corporate sustainability leaders are already using AI for
• 18:27
  something environmental-related.
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  It's worth saying that these are perhaps still early, nascent use cases,
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  that they're tending to apply them for perhaps the most obvious uses,
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  like reporting, automating reporting, automating data analysis, writing
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  reports, disclosure, carbon accounting.
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  And these are natural early applications, where you've got fragmented
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  data sets, repetitive workflows.
• 18:47
  AI's great for bringing the efficiency gains, but we've heard from our
• 18:51
  sustainability audience, and we note this in our work, that perhaps
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  the more advanced applications are yet to come, where it comes to more
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  sophisticated operations and climate risk analysis in all these areas.
• 19:02
  I'm happy to dive into those a little bit further.
• 19:04
  Interesting, you said that 60%, I think 61% of companies tell us
• 19:09
  that they're using AI for some sort of sustainability reasons.
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  But 40% are not.
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  That's a pretty high number.
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  I mean, I know it's more than half are using it, but gosh, you would
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  think that, what is this, five years into the adoption of AI, that it
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  would be nearly a hundred percent.
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  Is that just because the use of AI is mostly analytical, and people
• 19:31
  have their own tools and the frameworks, and it's just inertia?
• 19:35
  I think it's a really great observation.
• 19:37
  I think you're right that you might've expected that number to be higher
• 19:40
  given, how mainstreamed AI has been and how both the urgency of the focus
• 19:45
  across the entire private sector.
• 19:47
  I think what it reflects is not an inertia or a lack of commitment on the part of
• 19:51
  our sustainability audiences, but I think just more a case of still building up
• 19:55
  that kind of that internal literacy, that internal fluency, that really identifying,
• 20:00
  OK, where are the real use cases here?
• 20:02
  Where are the real returns going to be?
• 20:04
  How do we perhaps integrate AI into what are already quite complex
• 20:09
  processes and also quite fragmented and perhaps opaque data sets?
• 20:14
  I think there's also a sort of a laying the groundwork for effective
• 20:16
  use of AI, but I think it is building and I think if we ran that survey
• 20:20
  again at the end of this year, I'm sure that 60% would be a lot higher.
• 20:24
  And I think the momentum is there, and we see that both in sustainability and
• 20:28
  some of the related functions, as well, like social impact and philanthropy.
• 20:32
  So I think it shows that the adoption of AI does require some work, right?
• 20:35
  It's not always easy.
• 20:36
  It's not always straightforward.
• 20:37
  There is some sort of integrative tissue and fabric that needs building,
• 20:40
  but it is happening, for sure.
• 20:42
  And I think it will continue to mature as a discipline within sustainability.
• 20:46
  So the biggest use that's in these reporting and analytical
• 20:50
  functions, you've said that, but there's also use in operations.
• 20:54
  Talk about that, for energy efficiency, waste and water management, all of that.
• 21:00
  A hundred percent.
• 21:01
  And we think and we hear from our members that the potentially
• 21:04
  highest-value sustainability and environmental use cases are here.
• 21:08
  That they sit beyond reporting and disclosure.
• 21:10
  They sit in really how we can really transform operations and logistics and
• 21:14
  supply chains and, for example, to use energy efficiency and grid optimization.
• 21:18
  We know that targeted and effective use of AI through an analysis of
• 21:23
  data and looking at for efficiencies can really cut energy use, whether
• 21:26
  it's in buildings and industrial sites, even within data centers.
• 21:29
  And we've heard stories of AI systems actually reducing the power usage and
• 21:34
  cooling usage within data centers as they're applied to those data sets.
• 21:38
  And I think can also in the same vein be used to forecast energy use and
• 21:43
  improve the mix and improve efficiency.
• 21:45
  So there's so much potential there to be integrated much
• 21:48
  deeper into corporate operations.
• 21:51
  And obviously that goes beyond just the sustainability team.
• 21:53
  The sustainability team has a key role to play there, but it's really the entire
• 21:56
  business and the entire sort of the business units and operations, supply
• 22:00
  chains, logistics, et cetera, that all have an important role to play in bringing
• 22:04
  in AI in an effective and measurable way.
• 22:08
  And you could add a lot more examples to that, I think of what AI can do for
• 22:11
  optimizing, or for logistics, routes, network op systems, supply chains,
• 22:16
  materials, innovation, recycling.
• 22:18
  There's so much here that AI can be applied for.
• 22:21
  And it's never easy.
• 22:22
  There's always bottlenecks.
• 22:23
  There's always challenges.
• 22:23
  But I think in the years to come, we'll start to see companies realize some quite
• 22:28
  measurable and tangible environmental gains from the application of AI.
• 22:33
  In our latest C-Suite Outlook survey, one of the key issues for CEOs and
• 22:38
  CFOs in the application of AI was demonstrating some sort of ROI.
• 22:43
  It's gone from, it's a typical kind of startup where few years ago, nobody could
• 22:47
  spell AI, and then it was rapid adoption.
• 22:50
  And then last year in the survey, CEOs were most concerned about falling behind,
• 22:54
  and so it was like, let's invest like crazy so that we don't fall behind.
• 22:58
  This year they're looking for ROI.
• 23:00
  And that showed up in your survey here as it relates to the use of AI
• 23:05
  for sustainability tools, because there were not clear returns on
• 23:11
  the deployment of those tools.
• 23:13
  That's right.
• 23:13
  And I think the same, you're so right that I think we're seeing now, across
• 23:17
  the board, the issue of ROI is emerging as this central AI question, isn't it?
• 23:22
  And yeah, how do we, we've all adopted AI to some extent.
• 23:25
  How do we now integrate into our core processes, our core business in a way that
• 23:30
  can also be measured and show tangible returns and justify that investment?
• 23:33
  I think it's the same thing for the corporate sustainability function.
• 23:36
  It's interesting cause there are a lot of examples out there of very
• 23:40
  tangible and measured improvements in sustainability through the use of AI that
• 23:44
  also translate into business returns.
• 23:45
  I'm thinking, for example, when companies have applied AI to their
• 23:49
  logistics and have optimized the routes of their fleets and reduced mileage,
• 23:53
  reduced fuel use, reduced emissions.
• 23:55
  That has a big return to the business, as well, in terms of costs and saved costs
• 24:00
  and more efficient ways of operating.
• 24:01
  So there's a strong ROI element to this, too, right?
• 24:04
  I think if AI can be leveraged by companies to advance sustainable
• 24:09
  and environmental goals, it's also going to contribute towards showing
• 24:12
  that broader business case, right?
• 24:14
  As we see efficiencies improve and consumption of fuel and
• 24:18
  materials reduce, I think that will play an important part in that.
• 24:21
  I think sustainability leaders, there's an opportunity here for them to be at the
• 24:25
  table and be part of these discussions and really make their voice heard.
• 24:29
  Any final thoughts or insights you'd like to share from your report?
• 24:33
  As always, Steve, we've had a wide-ranging and a comprehensive discussion.
• 24:37
  I'd just add that we've also done a lot of work in The Conference Board's Governance
• 24:41
  & Sustainability Center on AI oversight and AI governance and responsible AI.
• 24:46
  And one of the findings in our work, which perhaps isn't surprising given some
• 24:49
  of the things we spoke about, is the environmental issues have often been much
• 24:52
  lower in those discussions than perhaps some other big risk areas of AI, right?
• 24:56
  Like bias and transparency and privacy and security and safety.
• 25:00
  Makes a lot of sense given those are the areas where
• 25:02
  regulators perhaps have of focus.
• 25:04
  But I think as AI adoption accelerates, and some of the things we've been speaking
• 25:08
  about today become more apparent, I think we'll see perhaps environmental
• 25:11
  issues move up that agenda, as well.
• 25:13
  So I think this isn't just a sustainability story.
• 25:15
  This is a governance and oversight story, as well.
• 25:18
  So definitely a space we'll be watching and monitoring
• 25:20
  closely at The Conference Board.
• 25:22
  All right, we'll be watching it, too.
• 25:24
  And Andrew, they can find your report where?
• 25:27
  I'd encourage anyone listening to visit the TCB website, tcb.org, and visit
• 25:31
  the Governance & Sustainability Center.
• 25:33
  You'll find our report there.
• 25:35
  Publicly available.
• 25:36
  It's worth the read, and lots of good data in there.
• 25:40
  Dr. Andrew Jones, thanks for being with us today and sharing
• 25:42
  the insights on your report.
• 25:44
  Thanks so much for having me, Steve.
• 25:45
  Always, always a pleasure to join you.
• 25:47
  And thanks to all of you for listening to C-Suite Perspectives.
• 25:50
  I'm Steve Odland, and this series has been brought to you by The Conference Board.