Workiva Amplify 2026 | The CFOs Playbook

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Douglas Schosser

CFO

Northwest Bank

play_circle_outline Prioritizing AI investments: customer impact, efficiency, and risk reduction.

play_circle_outline AI Transformation Metrics: Trusted Data, Real-Time Trends, Cross-Functional Orchestration, and ROI

play_circle_outline Governance and oversight for AI: mitigate hallucinations, treat like new hires.

play_circle_outline Human expertise versus AI: humans handle explanations, judgment, and connecting dots.

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The CFOs Playbook

Douglas Schosser

CFO Northwest Bank

In this interview from Amplify 2026, Doug Schosser, chief financial officer of Northwest Bancshares Inc., joins theCUBE's Krista Case and Alison Kosik to discuss lessons from leading finance transformation at both a major bank and a growing regional institution. Schosser explains how Northwest prioritizes AI investments that improve efficiency, enhance the client experience and reduce risk, noting that the three goals tend to reinforce one another. He details how real-time production data helps surface trends that prompt faster business adjustments, and why a... Read more

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How does Northwest decide which AI investments to pursue, and what factors guide those decisions? add

How can an organization ensure it has high-quality, trusted data before building AI-enabled applications, and how can a CFO determine whether a transformation is creating real value rather than merely causing change? add

How should organizations approach governance, risk, and oversight when deploying AI technologies in the workplace? add

Why was Amplify chosen, and how is technology like Amplify expected to amplify human capabilities and change the role of people in finance? add

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Alison Kosik

>> Welcome to Workiva Amplify 2026. We are back and we are streaming live from Las Vegas. I'm Alison Kosik alongside Krista Case, and we're about to dig into a customer who's going to be talking about what he's learned from leading financial— from leading finance transformation at both a major bank and a growing regional institution.

Krista Case

>> Yeah, Alison, I'm really looking forward to this conversation because we've been talking obviously a lot about AI, and I'm really interested in, how he's thinking about connecting the numbers to potential implications for business decisions and risk.

Alison Kosik

>> Yeah, all right, well, let's bring him in. Doug Schosser, he's a chief financial officer with Northwest Bancshares. Welcome to theCUBE.

Douglas Schosser

>> Thank you very much. Good morning. Great to talk to you guys today.

Alison Kosik

>> Great to talk to you as well. So, you came to Northwest after helping lead a major finance transformation at KeyBank, right? Right. All right. So I'm curious what you learned from that experience and what you knew you wanted to bring with you as CFO.

Douglas Schosser

>> Yeah, no, great question. Actually, I led another major transformation at Northwest as well. So after we spent some time sort of learning how you could get information delivered so much more efficiently and so much more connected with operational data through using some other software, I decided to bring that experience over to Northwest. And it was interesting because the size and scale of the two firms are very different. Northwest had a lot of opportunities to kind of take their finance program to the next level. And so I was able to do that a lot with some of the software partners that we use, like Workiva and others. And it's been really rewarding. The other nice thing about it is because it's smaller, it's a little bit easier to get your arms around sort of the current issues more quickly than a big massive company that's got, things spread all over multiple states and everything else.

Alison Kosik

>> Yeah, yeah.

Krista Case

>> So, Doug, one thing that I'm interested in is when you think about these transformations, obviously there's always going to be some tradeoffs, in terms of how does the business make decisions about what to invest in, especially in this era of AI. There's all of these different projects that we could be chasing. So especially in your role as a CFO, how do we think about determining which AI investments kind of warrant investment in scale?

Douglas Schosser

>> Yeah, so at Northwest, we always think about what's going to impact the customer first and we kind of think about it a little bit that way. But there's a lot going on. So when we kind of think about AI investments and we think about, okay, what are the investments that can maybe do both things, improve the efficiency of the firm and also make the client experience better, either by removing friction or by offering them better options than they had before. So that's sort of how we've prioritized it. And then we also kind of look at it from the risk perspective. That'd be the other big pillar. When there's opportunities to reduce risk by either providing information faster or more comprehensively, we lean in there as well. So the three actually work pretty well together.

Krista Case

>> Yeah, and are there any kind of more specific metrics that you have in mind? That makes sense conceptually, but I'm also curious if there's any more specific metrics that you tend to look at.

Douglas Schosser

>> Yeah, we start to break it down. So the more quickly we can find out things that are happening real time so that we can actually make adjustments on the fly. So if you think about, I don't know, production per day at a financial center as an example, if we have opportunities to see trends in real time that maybe would create an insight that might make us wonder what's happening at a particular location or whatever, that's really where we're trying to get to. So again, how do we use the data and information at our fingertips to kind of find areas where we may be able to lean in a little bit more or maybe where we have to fix an issue?

Krista Case

>> And building on that so obviously having trusted data becomes more important as kind of that asset to make business decisions on. And so can you talk about how you make sure that you're getting the highest quality datato make these insights versus potentially a poor data stream, where the impacts of that could be exacerbated across the enterprise as we introduce AI because they're taking machine actions at scale.

Douglas Schosser

>> Yeah, no, you're absolutely right. I think we've also thought a lot about as we deploy AI, the first thing we want to do is make sure that the data that we're using is accurate first, which has always been a long-term program for all corporations, right? So I think the way we look at it is once we have the curated set of data that we can rely on, that's when we're going to start to build other applications on top of it, like AI-enabled type of applications or other things. So, um, for us, it's always been a big partnership between finance, IT, and lines of business, because a lot of data is created at the client interaction with the company, and that's more of a business, um, initiated, uh, interaction. So we would rely on them to have the better data, and then as we enhance the data or as we augment the data, then you've got some of the other functions in the back that feed into data quality. So it's a very large orchestration problem that we're solving and always trying to figure out where is the most logical area to get the best data first.

Alison Kosik

>> So as CFO, how do you know whether a transformation is actually creating value instead of just simply creating change?

Douglas Schosser

>> Yeah, it's a good question. I think we look at the metrics after it. So not right away, like most investments take a few weeks, months to really pay off and see that return. A lot of intuition goes in upfront which ones we choose, but then we start to really track and see the gain in momentum or the improvement in cycle time, things like that, to then measure that, hey, we are actually getting a return on this particular investment. And it's interesting, the big challenge right now with AI is how do you figure out if it's actually, you know, saving money? And it's hard because if you're redeploying activities into more productive things, I think you have to measure it on the revenue side too, or on the growth side.

Alison Kosik

>> Yeah, yeah.

Krista Case

>> And Doug, we've been talking a lot about your role as a CFO, but in your previous life you were a chief audit officer.

Douglas Schosser

>> Chief accounting officer.

Krista Case

>> Excuse me, chief accounting officer. So just interested in that transition in roles and any bridges to helping to make these decisions.

Douglas Schosser

>> Yeah. So the two fit together really well. I think in the accounting side, you really do get a very detailed sort of insight into data quality and controls into sort of the way the firm is constructed almost at the atomic level. At CFO level, you don't have the— you don't have the opportunity to get into that much depth. So I think it's sort of like one of those building blocks. So having been in that role for so long, I think you're able to quickly identify areas to focus on a little bit more quickly or maybe areas where intuition would lead you to believe maybe we need a little bit more information before we make a decision, or that information is 80% okay and it's time to sort of move forward and make the decision. So it's really a balance of the chief accounting role is all about sort of a lot of historical perspectives and making sure that all of the data and information was appropriately gathered and reported. CFO is much more how do we take that information, make a decision going forward.

Alison Kosik

>> Yeah, yeah. For CFOs today, I think the question is less about whether to use AI and it's more about where it can wind up creating measurable value.

Douglas Schosser

>> Absolutely. Yeah. And I think that's the biggest thing. And it's the area where technologists and finance people have had to get together a lot more than maybe historically. And I think that partnership has been building for years, but now it really is sort of, two sides of a coin in this case where both have to work together. Both have to try to steward the investments for the firm appropriately, as well as making sure that the return is there, and then making sure that the infrastructure can handle the change that you're about to push into it. So it's crucial that those relationships get built and are stable.

Krista Case

>> Yeah, and Doug, so as Allison was saying, and you were agreeing with, AI certainly is here to stay, but we're not going to be ripping and replacing these years of investments in underlying technology. When you think about evaluating whether the technology has a role for the future, if it's something that we should be ripping and replacing, what do you think makes a technology worth carrying forward to this AI era?

Douglas Schosser

>> Yeah, it really comes down to— I think we've got to go back to the basics of what's the business problem we're trying to solve, right? So just layering on a new tool on top of bad process is never a particularly good answer, and it may just lead you to automating something that's not very efficient or that's creating friction on its own. So I think what we've always tried to do at Northwest and at other companies I've worked for is what is the problem we're trying to solve and then what's the best way to solve it? And a lot of times you can solve the process piece and the technology piece at the same time and use the technology almost as the element that helps with the transformation, right? why do I want to change a process if I otherwise don't have to? That's effort too. It's like in order to get a larger return kind of on the back end. So it's helpful for us to really help lead business leaders through that analysis, like business problem, here's the solution, and how do we really kind of turbocharge the solution by making everything work more effectively together?

Alison Kosik

>> I'm curious if the speed of AI has changed your threshold to how you think about, technology investment, governance, and risk.

Douglas Schosser

>> Absolutely. So it's definitely changed governance. So, we think about hiring a junior analyst and having a lot of oversight over that person early on in their job. For whatever reason, we don't think about technology that way, but I'm thinking they are doing similar tasks with similar data. I've seen some, I guess the term would be hallucinations that occur out of AI. So that need for really understanding what you're looking at and what you're asking it to do is still there. So that's kind of the governance and risk piece of it. And I really think we have to look at this just like hiring, 15 new people out of an internship program, right? You're not gonna let them make big decisions or create reports that go right up to the board without oversight. I'm not sure why we would do that with technology either, but technology can certainly make the effort less and can help you more quickly get to an answer. And then you still have to use judgment and all those things on the backend.

Krista Case

>> Yeah, Doug, that's been, I would say, a fairly common thread, across the guests that we've been speaking with, today here at Workiva Amplify. And I guess thinking in particular about, accounting and finance roles, where do you think there is going to be the need for that human expertise in particular versus where we can start to really leverage AI, more effectively?

Douglas Schosser

>> Yeah, well, I'm sure they didn't choose Amplify without thought, right? So I think really technology amplifies your ability to do things more quickly and that way you can get your human workforce to really do what they're best at, which is probably still detailed explanations and helping connect the dots. So as good as AI might be at taking two sets of data and mashing them together without that human person who's got experience maybe explaining this is why this is really important and this is what we should do as a result of it, which is where I think really finance is leaning in much more heavily now. So again, really thinking about how do we use the technology to amplify the human's impact, either get them more quickly the right answers or get them pointed in the right directions more quickly.

Alison Kosik

>> All right. Let's talk about what—

Krista Case

>> what—

Alison Kosik

>> why keep— why keep the technology that you've already had? So I know that you used Workiva at KeyBank and now at Northwest. So I'm just curious, when you move into a new organization, what makes a technology valuable enough that, you want to bring it with you.

Douglas Schosser

>> Yeah, I think Workiva being a great example, right? I've always been a big fan of the way that technology is built to enhance the collaboration of all the workforce, right? So when I think about trying to bring together gigantic documents, 10-Ks, 10-Qs, proxy statements where multiple departments are providing bits and pieces of the report, and then you've got a single platform that everybody can work in together that also provides a really great audit trail that your auditors can leverage. To me, that justifies its own cost just from the benefits that you get from that. And then your ability to start from, hey, here I was last quarter and roll it forward really effectively. And again, now you can see where AI can kind of attach to that and really make those processes really, really efficient. So again, I think software that works is just one of those things where everybody knows it and then it almost would be more why wouldn't you use it again as opposed to why would you?

Krista Case

>> Absolutely. And so, we're kind of seeing it as almost this layer that can connect these different systems and kind of start, leveraging and integrating that kind of tribal knowledge that humans have. So almost creating this kind of context layer, that folks like yourself can use to make better decisions. So would you agree with kind of how we're thinking about, not just Workiva, but, positioning the use of AI in general?

Douglas Schosser

>> Yeah, I would. And I wonder if, at some point, since you've seen all the iterations of a document and it's all saved, right? If you think about like how a large language model learns, I'm not so sure that at some point all of that information that you've captured over all these years can't be used to more quickly train an AI agent to sort of take over that task for you and make that more efficient. So again, anytime you've got a large repository of data over multiple periods, which is what Workiva would have. it sort of lends itself really nicely to being able to take all that information, dump it into another platform, or use a Workiva-built agent to really sort of leverage all of that context and information. So I mean, I think the future for all of this stuff is really exciting.

Alison Kosik

>> Yeah. What does the next generation of CFOs need to get right that wasn't nearly as important 5 years ago?

Douglas Schosser

>> That's a great question. You know, I think it's probably the, yeah, comfort with moving quicker. So I think CFOs are part of the risk infrastructure of any organization. So part of it is we always need to take a step back and be able to understand what's the big picture out there? What are the things we're not thinking about? What corners do we need to look around before we make this decision? With decisions coming more quickly because there's more information that's kind of democratized throughout the organization, I think CFOs are going to have to get even more comfortable with, being able to quickly assess how did we come to the conclusion that that's what we want to do, or that's the direction that we want to go. And I think, again, because it's happening more quickly and in more areas, that could be more of a challenge going forward. But again, I think it's great. Any time that we can make decisions more quickly is probably better. It's just balancing that off with, okay, are we really able to handle all of that change that quickly?

Alison Kosik

>> All right. Wonderful conversation. Enjoyed talking with you today.

Douglas Schosser

>> Yeah, it was great. Thank you very much.

Alison Kosik

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