Video summary
The discussion at Workiva Amplify 2026 centers on the critical intersection of artificial intelligence and sustainability data, featuring insights from Jen Huffstetler of HP and Kristen Siemen, a senior adviser at McKenzie. Both experts emphasize that high-quality, standardized sustainability data acts as essential "rocket fuel" for corporate transformation in an AI-driven world. They argue that without accurate and reliable data, organizations risk making incorrect decisions at an accelerated rate, particularly when leveraging AI tools for capital allocation or operational strategy. The consensus is clear: as companies face increasingly complex and diverging regulations across over 180 countries, the ability to provide transparent and accurate sustainability information is no longer optional but a fundamental requirement for market access and business continuity.
A significant portion of the conversation addresses the challenge of data fragmentation across diverse functions such as supply chains, operations, HR, and procurement. Jen and Kristen advocate for a model of shared accountability where governance extends from the boardroom down to individual data owners, ensuring clarity on who owns specific data points and how they are maintained. They highlight HP's approach using a federated data lake within a compliance intelligence platform to harmonize data across the organization. This structure allows different departments to own their pieces of the "data pie" while maintaining consistent definitions and controls, which is vital for responding to granular customer requests and regulatory audits without creating silos that hinder efficiency.
The speakers also explore how AI can immediately add value by automating the wrangling of vast datasets, prepopulating reports, and optimizing product energy footprints, yet they caution against over-reliance on automated outputs without human oversight. Human judgment remains indispensable for interpreting complex regulatory landscapes, distilling relevant information, and ensuring data quality and traceability within governance pipelines. The dialogue underscores that while AI can process information quickly, the integrity of the underlying data must be assured through rigorous internal reviews and controls to prevent hallucinations or errors that could impact financial decisions. Ultimately, they conclude that sustainability teams have a heightened responsibility to ensure their data is high-quality and regularly updated, as this core asset underpins the credibility of any AI model used for strategic business decisions.
Read the full video transcript
Welcome back to Amplify 2026. We're
coming to you live from Las Vegas. I'm
Allison Casic alongside Christa Casease.
And with AI kind of entering the room, I
think there's a lot of talk about, you
know, what companies need to get right,
you know, as these stakes get higher.
>> Absolutely. We need trusted data, right,
to make better business decisions.
>> Yeah. Yeah. Let's dig into it. I want to
bring in our guests. We've got Jen
Huffetler. She's the chief
sustainability officer at HP. Welcome to
the cube.
>> Thank you.
>> And Kristen Seaman, senior adviser with
McKenzie, former chief sustainability
officer with General Motors. Welcome to
the cube as well.
>> Thank you.
>> First ladies, if you wouldn't mind um
walking me through what your roles are.
>> Sure. Um so at HP, Inc. uh where we have
a portfolio of products from
collaboration devices, headsets, PCs to
printers, and industrial print. I have
two main roles. Chief sustainability
officer looking after setting our
corporate strategy and implementing that
across the entire portfolio. And then
I'm also the senior vice president and
general manager of our global market
access and product compliance. So again
working across that portfolio in a way
to help ensure our products are both
compliant uh for the everinccreasing
compliance reporting that is coming
especially in the sustainability domain.
>> Kristen, sure. So, as since I've retired
from General Motors, I've been spending
my time doing both board work and
advisory work both across the technology
and automotive field as well as climate
tech. Um, as Jen mentioned, a lot of the
challenges around reporting and and
really instituting sustainability
initiatives within corporations.
>> All right, let me start out with a
question to both of you. Um, Jen, to you
first. Why do you think sustainability
data is more important, you know, in
this world of AI?
>> Oh, I I will quote our CIO. I'm not sure
where he got it from, but um enterprise
standardized data and that includes
sustainability data. It becomes, you
know, just the rocket fuel for a company
that's in transformation. So, if you're
looking at data across the company,
sustainability data is no different.
Whether we are trying to create, you
know, the governance, the controls
around the data that's going into our
climate transition plan as an example or
the recycled content. Um, we have
additional and increasingly complex and
diverging regulations that demand that
we have transparent and accurate data
for the many jurisdictions. We we sell
in over 180 countries and so data
becomes ever more important and we'll
talk about how AI can help in that world
as well.
>> Okay, Kristen, your thoughts?
>> Yeah, I think it as Jen said, the data
is critical and having accurate data
enables you to really understand where
you're at and really to put plans in
place to take action. Without having the
data, it's really difficult to even get
started. And so knowing where you're at,
knowing where you need to get to and and
the accuracy of that is so important,
particularly in the the stage that we're
in with AI and the ability to just get
so much information, but you have to
know what to do with the information and
be able to rely on it as well.
>> Absolutely, Kristen. And you know, we
can per we can potentially be making
incorrect decisions, more of them, at a
at a greater rate, you know, um if we
don't have accurate data. Um, and Jen, I
love your analogy of kind of, you know,
good data, standardized data as rocket
fuel, but maybe we can double click a
little bit around um, a challenge that
I'm envisioning, which is the fact that
our sustainability data is originating
across a number of different factors. We
have supply chains, operations,
facilities, HR, procurement. So, um,
maybe we can start with Kristen just
given your advisory capability, but
obviously Jen, if you have thoughts as
well. um how do we think about you know
effective data management given this
this challenge you know again given the
fact that there really is no common data
model for sustainability data
>> for sure. Yeah. I I mean I think one of
the challenges around sustainability is
that it touches the entire corporation
and your entire value chain and as Jen
mentioned your customers your suppliers
etc. And so really be able to have a way
to communicate that effectively and
consistently across all of those is
super important. It enables you to share
learnings as well as to share challenges
and maybe find where there's the biggest
opportunities for action. We we all have
this insatiable desire to to move and
and to make things go faster and to
solve problems. The challenge is knowing
where your energy is really going to
have the most impact. And so by having
that data readily available and being
able to share that across orgs really
causes that to be an easier
prioritization.
>> Yeah. And just to build on that when I
when I look at our role and it's
encompassing everything from that
compliance reporting to there's often
raiders and rankers that are looking for
similar data. We have customer RFPs
asking us for increasingly granular
levels of information about the PC
they're buying. And it's really
important that we build internally that
consistent set of data so that we can
deploy AI tools to help leverage and
ensure accurate responses in all of
those different use cases.
So, I'm reading into both of your
comments, Kristen and Jenna, and sort of
shared accountability, right, amongst
stakeholders within the business. Um,
can you talk about that from a
sustainability perspective in
particular, you know, maybe who owns
what pieces of data um would be a good
place to start?
>> Sure. No, it's a great question. I love
the shared accountability model. I'm
sure Kristen had the same thing in in
her former role as well. It's really
critical for a role like this that we
have strong governance from the board
down to the individual data owners and
that we have clarity on you know who
owns that data when it's going to be
updated how we ensure that it maintains
its quality and that we've got the
approvals up the stack. So when I think
about the types of data so in the
workforce data it can be um you know the
composition of our workforce where
they're located um for our supply chain
obviously procurement it can that we can
have data that we are working on around
our climate risks um and how that is
going to impact our overall financials.
What is the financial risk of climate
events for the company? This is critical
data that every company needs to report
on and how are we gathering and
reviewing that across every different
element of the company. Bringing it
together in a harmonized way at at our
company we have a federated data lake.
We're built built in a compliance IQ
this intelligence platform to really
ensure that we're putting the governance
and the controls in for everybody to own
their piece of the data pie.
>> Yeah. Yeah, and I would just add I think
as you talk about the the governance and
controls is so important because as this
world continues to expand and we think
that data is so readily available, the
the ability to know where the gold
source is and the assurance and the
governance around the accuracy is is so
critical to what you're supplying
whether it be internally or externally.
So do you think to both of you that
sustainability data should actually be
held to the same rigor as say financial
data you know um meaning like common
definitions repeatable processes and
strong controls. Uh Jen you first.
>> Yeah I mean we actually we have
expectations around the globe that we
need to meet levels of limited assurance
in our sustainability data already today
and we're seeing trends that that will
continue. I think for the ability to
compare across companies, we need more
work around the standardization. So
unfortunately today the way that folks
are doing their reporting, it can still
be different and there's many efforts in
many different industries to try to work
to harmonize for example how you look at
supply chain emissions as an example.
>> Yeah. Yeah. I would agree. I I think the
internally and externally if I think
back to my time at General Motors we ran
the exact same processes for
sustainability data and reporting that
we did for financial. So internally our
reviews our assurance the the process
and the signoffs was all consistent but
how that gets translated externally and
compares company A to B and particularly
when you get into rankings and ratings
and those type of things that
standardization doesn't necessarily
exist.
And I think it's kind of a standard
standardization and compliance
conversation. But also um you know I
think Jen in particular you were
mentioning um the sustainability data is
going to impact decisions about about
how capital is allocated or you know
different operating models within the
business. Um would you agree with that?
>> Yeah 100%. Whether it's around you know
climate adaptations or mitigations for
our physical plants or where our
suppliers are located. Um it's also for
investment in the portfolio and so you
know one of the things we think about is
as we're building our data
infrastructure governance and controls
what is the information that's the
increasing requirements from customers
what are they asking for and how can we
simplify giving them the the data they
need to meet their sustainability goals.
So we we literally just recently in June
launched something where we took that
compliance intelligence platform and we
fed it through to a software solution
called the workforce experience platform
where IT decision makers are managing
their whole fleet and they now have
visibility to the carbon the dynamic
carbon footprint of the fleet of PCs. If
you think about a company, General
Motors, McKenzie, hundreds of thousands
of PCs in many cases, how do you give
them the tools to help manage to their
own goals and their own compliance
needs?
>> And that goes well beyond simply
reporting, right, for the sake of again
compliance or regulations, right?
>> It's impacting decisions about the
business. So, how do we make sure that
we can kind of trust and defend, you
know, the data that's underpinning
potentially some of these AI models that
are helping us to make these decisions?
>> Do you want to start?
>> Yeah. I I mean I think it goes back to
that same governance and and management
and integrity within your own processes
that you're doing whether it be
financial data or sustainability data
you know and the the word sustainability
tends to get a bad rap these days but I
I really think if you take a step back
all of this is talking about governance
risk management future proofing of the
business and making sure that the data
that you're putting into those decisions
is credible and and can be backed with
an assurance level, whatever is
appropriate for that to be able to
really drive the decisions across the
company.
>> Yeah. And I'll just I'll take one more
step. I really think financial decisions
within a company are typically being led
on that data of the company which has
clear governance and controls and then a
private enterprise model that is
leveraging that data. So this single
source of truth exists and the risks of
hallucinations for the capital
allocation are much lower and that's
part of why we're seeing you know this
increased need for data sovereignty data
privacy and models that are trained on
your enterprise data and when I think
about how we leverage AI inside an
enterprise to make those decisions to
provide you know information updates to
you know our various
um decision makers and business units or
finance that's really a critical
component is that that is being run at
the edge.
>> So we are here at work amplify um you
know with finance risk and
sustainability leaders trying to figure
out you know how to use AI. Where do you
see AI creating the most immediate value
for sustainability teams? Jen, you
first.
Well, in my my own team, it's in
wrangling the vast amounts of data that
we're trying to bring together for these
various reports. Um, so that's just real
time uh agents being built to help
prepopulate,
you know, whether it's submission forms.
Um, we're also using it um to, you know,
in every business they're using it to
lower the energy of whatever the device
is. So in our products you know that is
helping
you know the products to lower their
footprint overall companies are using
it. Um there there's one other example
and it's escaping my mind. Um
>> we can switch we can switch over to
Kristen and then if if you remember it
come back to it.
>> Sure. Yeah, I think the um I think AI is
enabling just like the information
technology enabled more information and
it's it's the ability to access the
information quickly to use a golden
source and apply it to multiple
reportings analysis. It also allows you
to I think get a much um quicker view
externally understanding competition or
new technologies that are coming out.
And so it's both an internal I would say
efficiency gain as well as an external
information availability and ease of of
matching those two I think is really
coming to the forefront here.
>> Yeah.
>> And where is human judgment going to be
most important in this conversation as
we do start to lean more in AI?
>> Yeah. The use case I didn't bring up is
around this regulatory detection and
distillation of what really is impacting
your company. And so human judgment is
critical there to really look at what is
applicable to our company or not. Um
there's so many sustainability
regulations that are coming to the
forefront. Um and then the other piece I
believe in that that human aspect is in
the data quality and governance.
Absolutely. really making sure that
pipeline um has clear traceability,
transparency is becoming foundational
for market access.
>> I I agree. I was going to say the exact
same thing that the governance and the
data accuracy is is probably more
important than ever because when the
tools become easier, it becomes easier
to try and just take the shortcut and
rely on that. So building a governance
process and internal accuracy and review
I think is is more important than ever
as as you become more reliant on on AI
and other tools to make it easier and
quicker.
>> Jen, any closing thoughts?
>> Closing thoughts. Um I think what we're
learning in this AI era is that if you
know AI needs the standardized data the
sustainability teams their work now
becomes ever more critical to ensure
that that data is of high quality is
regularly updated and that everybody at
the company knows or is pointing to
leveraging that core asset of of that
data.
>> Kristen, any closing thoughts?
>> Yeah, again I I think very consistent
here. Um, going back again as as an
engineer, data and integrity and and
compliance and standards have always
been super important to me and and I
think this is again more important than
ever is that as we rely on a tool that
we ensure there's a governance behind it
that we can all stand behind and be
comfortable with.
>> All right, really enjoyed this
discussion. Thanks for your time. Thanks
for stopping by the cube.
>> Thank you so much for having us.
>> And you're watching the Cube, the leader
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