Workiva Amplify 2026 | Sustainability Data for the AI Era

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Jen Huffstetler

CSO

HP

Kristen Siemen

Former CSO, General Motors, Senior Advisor

McKinsey & Company

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Sustainability Data for the AI Era

Jen Huffstetler

CSO HP

Kristen Siemen

Former CSO, General Motors, Senior Advisor McKinsey & Company

In this interview from Amplify 2026, Jen Huffstetler, senior vice president, chief sustainability officer and global market access at HP Inc., joins Kristen Siemen, senior advisor at McKinsey & Company, to talk with theCUBE's Krista Case and Alison Kosik about why sustainability data is becoming the rocket fuel for enterprise AI transformation. Huffstetler explains how HP's federated data lake and compliance intelligence platform bring governance and traceability to data spanning workforce, supply chain and climate risk. Siemen highlights why accurate, access... Read more

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How should companies manage and govern sustainability data to support climate transition plans, ensure transparency and accuracy across many jurisdictions, and what role can AI play in that effort? add

How should an organization effectively manage sustainability data that originates across multiple functions (e.g., supply chain, operations, facilities, HR, procurement) when there is no common data model for that data? add

How can enterprises safely use AI for financial and capital-allocation decisions, and what roles do data governance, data sovereignty, models trained on enterprise data, and edge deployment play? add

In what ways can AI create the most immediate value for sustainability teams? add

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Sustainability Data for the AI Era

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Alison Kosik

>> Welcome back to Workiva Amplify 2026. We're coming to you live from Las Vegas. I'm Alison Kosik alongside Krista Case. And with AI entering the room, I think there's a lot of talk about what companies need to get right as these stakes get higher.

Kristen Siemen

>> Absolutely.

Krista Case

>> We need trusted data, right, to make better business decisions.

Alison Kosik

>> Yeah. Yeah. Let's dig into it. I want to bring in our guests. We've got Jen Huffstetler. She's the Chief Sustainability Officer at HP. Welcome to theCUBE.

Jen Huffstetler

>> Thank you.

Alison Kosik

>> And Kristen Siemen, Senior Advisor with McKinsey, former Chief Sustainability Officer with General Motors. Welcome to theCUBE as well.

Kristen Siemen

>> Thank you.

Alison Kosik

>> First, ladies, if you wouldn't mind walking me through what your roles are.

Jen Huffstetler

>> Sure. So at HP Inc., 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 for the ever-increasing compliance reporting that is coming, especially in the sustainability domain.

Alison Kosik

>> Kristen.

Kristen Siemen

>> Sure. So 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. As Jen mentioned, a lot of the challenges around reporting and really instituting sustainability initiatives within corporations.

Alison Kosik

>> All right. Let me start out with a question to both of you. Jen, to you first. Why do you think sustainability data is more important, in this world of AI?

Jen Huffstetler

>> Oh, I will quote our CIO. I'm not sure where he got it from, but enterprise standardized data, and that includes sustainability data, it becomes, 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, the governance, the controls around the data that's going into our climate transition plan, as an example, or the recycled content. We have additional and increasingly complex and diverging regulations that demand that we have transparent and accurate data for the many jurisdictions. 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.

Alison Kosik

>> Okay, Kristen, your thoughts?

Kristen Siemen

>> Yeah, I think 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 the accuracy of that is so important, particularly in 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.

Krista Case

>> Yeah, absolutely, Kristen. And, we can potentially be making incorrect decisions, more of them at a greater rate, if we don't have accurate data. And Jen, I love your analogy of kind of, , standardized data as rocket fuel. But maybe we can double-click a little bit around a challenge that I'm envisioning, which is the fact that our sustainability data is originating across a number of different functions. We have supply chains, operations, facilities, HR procurement. So maybe we can start with Kristen, just given your advisory capability. But obviously, Jen, if you have thoughts as well, how do we think about, effective data management given this challenge, again, given the fact that there really is no common data model for sustainability data?

Kristen Siemen

>> For sure. Yeah. 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 all have this insatiable desire to move 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.

Jen Huffstetler

>> Yeah. And just to build on that, when I look at our role and it's encompassing everything from that compliance reporting to there's often raters 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.

Krista Case

>> So I'm reading into both of your comments, Kristen and Jen, around shared accountability, right, amongst stakeholders within the business. Can you talk about that from a sustainability perspective in particular? Maybe who owns what pieces of data would be a good place to start? Sure.

Jen Huffstetler

>> No, it's a great question. I love the shared accountability model. I'm sure Kristen had the same thing 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, 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, in the workforce data, it can be, the composition of our workforce, where they're located. For our supply chain, obviously procurement, we can have data that we are working on around our climate risks 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 and bringing it together in a harmonized way. At our company, we have a federated data lake. We're building ComplianceIQ, 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.

Kristen Siemen

>> Yeah, and I would just add, I think as you talk about the governance and controls are so important. Because as this world continues to expand and we think that data is so readily available, the ability to know where the golden source is and the assurance in the governance around the accuracy is so critical to what you're supplying, whether it be internally or externally.

Alison Kosik

>> So do you think, to both of you, that sustainability data should actually be held to the same rigor as, let's say, financial data, meaning like common definitions, repeatable processes, and strong controls? Jen, you first.

Jen Huffstetler

>> Yeah, 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 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.

Krista Case

>> Yeah.

Kristen Siemen

>> Yeah, I would agree. I think 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 process and the sign-offs were all consistent. But how that gets translated externally and compares Company A to B, and particularly when you get into rankings, ratings, and those types of things, that standardization doesn't necessarily exist.

Krista Case

>> And I think it's kind of a standardization and compliance conversation, but also, I think Jen, in particular, you were mentioning The sustainability data is going to impact decisions around how capital is allocated or, different operating models within the business. Would you agree with that?

Jen Huffstetler

>> Yeah, 100%. Whether it's around, climate adaptations or mitigations for our physical plants or where our suppliers are located, it's also for investment in the portfolio. And so, one of the things we think about is, as we're building our data infrastructure governance and controls. What is the information that's continued— the increasing requirements from customers? What are they asking for, and how can we simplify giving them the data they need to meet their sustainability goals? So 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, McKinsey, 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?

Krista Case

>> 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, the data that's underpinning potentially some of these AI models that are helping us to make these decisions?

Jen Huffstetler

>> Do you want to start?

Kristen Siemen

>> Yeah, I think it goes back to that same governance and management and integrity within your own processes that you're doing, whether it be financial data or sustainability data, and the word sustainability tends to get a bad rap these days, but 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 can be backed with an assurance level, whatever is appropriate for that, to be able to really drive the decisions across the company.

Jen Huffstetler

>> Yeah, and I'll just— I'll take one more step. I really think financial decisions within a company are typically being built 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 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 provideinformation updates toour various decision makers and business units or finance, that's really a critical component is that that is being run at the edge.

Alison Kosik

>> So we are here at Workiva Amplifywith finance, risk, and sustainability leaders trying to figure outhow to use AI. Where do you see AI creating the most immediate value for sustainability teams? Jen, you first.

Jen Huffstetler

>> Well, in my own team, it's in wrangling the vast amounts of data that we're trying to bring together for these various reports. So that's just real-time agents being built to help pre-populatewhether it's submission forms. We're also using it in every business they're using it to lower the energy of whatever the device is using. So in our productsthat is helpingthe products to lower their footprint overall. Companies are using it. There's one other example and it's escaping my mind.

Alison Kosik

>> We can switch over to Kristen, and then if you remember it, come back to it.

Kristen Siemen

>> Sure. I think AI is enabling, just like the information technology enabled more information, and it's the ability to access the information quickly, to use a golden source and apply it to multiple reporting analyses. It also allows you to, I think, get a much 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 matching those two, I think is really coming to the forefront here. Yeah.

Krista Case

>> And where is human judgment going to be most important in this conversation as we do start to lean more on AI?

Jen Huffstetler

>> 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. There's so many sustainability regulations that are coming to the forefront. And then the other piece I believe in that human aspect is in the data quality and governance, really making sure that pipeline has clear traceability. Transparency is becoming foundational for market access.

Kristen Siemen

>> I agree. I was going to say the exact same thing, that the governance and the data accuracy 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 more important than ever. As you become more reliant on AI and other tools to make it easier and quicker.

Alison Kosik

>> Jen, any closing thoughts?

Jen Huffstetler

>> Closing thoughts. I think what we're learning in this AI era is that if AI needs this standardized data, then 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 where to point to leveraging that core asset of that data.

Alison Kosik

>> Kristen, any closing thoughts?

Kristen Siemen

>> Yeah, again, I think very consistent here. Going back again, as an engineer, data integrity and compliance and standards have always been super important to me. 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.

Alison Kosik

>> All right. Really enjoyed this discussion. Thanks for your time. Thanks for stopping by theCUBE.

Jen Huffstetler

>> Thank you so much for having us.

Alison Kosik

>> And you're watching theCUBE, the leader in live tech coverage and in-depth expert analysis. We'll be right back.