Tanya Kohen: Hi everyone and welcome to the Invention Mode podcast. I'm Tanya Kohen and this is the space where we explore how corporate professionals embrace the invention mindset to rethink their roles and reframe how work gets done. Often through small clever fixes that spark meaningful change and move organizations forward. Together with my guests, we look at what it takes to build a culture where new ideas can take root and how that capability keeps companies competitive and future-ready.

And future-ready organizations need future ready people. In Treasury, figuring out what that actually means in the age of AI has become one of the most urgent conversations in the function. Most of the discussion I hear focuses on technology itself. What can it do? How fast it's moving, which tasks are going away. That's definitely worth paying attention to.

And yet the question that keeps coming up is a different one. Treasury has always been a function built on precision, process, and judgment. As AI takes over more of the routine work, what does that expertise look like? Which parts of it become more valuable and which habits need to go?

From what I see, the teams that adapt most effectively are the ones who have been honest about what their role actually is and have deliberately built around that. The technology opens up space, and what matters is what treasury professionals choose to do with it.

My guest today has spent years working on exactly that question. She coaches treasury professionals, writes frameworks they can actually use, and has trained over a thousand people to think about their careers and their function differently. I'm very glad to have Jessica Oku here today. Jessica, thank you for joining me.

Jessica Oku: Thank you so much. It's such a pleasure to be here today.

Tanya Kohen: A few words about our guest background. Jessica Oku is a global treasury and finance leader with over 13 years of experience helping businesses optimize liquidity, manage risk, and drive financial efficiency across top banks and multi-million dollar corporates. She's the founder of three platforms dedicated to equipping treasury and finance professionals with the skills and frameworks they need to grow: Young Professionals Coaching, Scale Treasury, and Scale Up Coaching, where she has trained over a thousand professionals to date.

Jessica is also the author of the books, including The Cash Flow Prioritization Matrix, a decision intelligence system for resource allocation that she developed herself. She was named Woman of the Year for the Americans by the Treasury Today group, and her work has been featured in Treasury Management International, The Guardian, The Business Day, among others.

Happy for you, Jessica, to join us today, and so excited to have this conversation with you. Let's start from the beginning. Your career has taken you from leading treasury operations at major corporates to building coaching platforms and writing books that equip treasury professionals with these frameworks. At what point did you shift from doing treasury, doing the actual treasury daily work, to actively building tools that help others think about it differently? And what made that feel more like a very important work to you?

Jessica Oku: I think for me, the shift actually happened when I realized that many of the challenges that treasury professionals face are not actually treasury problems alone. They are thinking problems. And throughout my career, whether it was managing liquidity, optimizing banking structures, reducing finance costs, or implementing technology or even supporting strategic decisions, I noticed that organizations had or often had access to the same data, to similar systems and comparable resources. Yet the thing is they consistently produced different outcomes. The difference was rarely information, rather it was decision quality.

And that realization became even stronger as I moved into leadership positions. I found myself spending less time executing transactions and more time helping people think through uncertainty, evaluate options, and invariably prioritize risk management and also be able to reduce business uncertainty. That eventually led me to build frameworks, all of the coaching programs, being able to execute and ultimately write the books. Like you mentioned, the Cash Flow Prioritization Matrix. And the goal was never simply to teach Treasury. It was to help people develop structured ways of thinking about resource allocation, decision making, and value creation ultimately. Because I always say that the language organizations speak is a value at the end of the day.

And the thing is, Treasury sits at one of the most fascinating intercessions in business between making strategic decisions and eventually being able to increase the cash flow or liquidity position of an organization. So Treasury sees all of the consequences of these decisions long before many other functions do. And what really made this work important to me was realizing that if we can improve how Treasury professionals think, we improve how organizations allocate capital, manage risk, and ultimately create value. And one thing I see is that technology will continue to evolve. But better thinking is really what skills across technology across every technology cycle.

Tanya Kohen: Frameworks and structures, they definitely support better thinking, better decision making. And it is easy to fall into a trap of my gut telling me “let's go with this or let's go with that.” But for Treasury, managing cash and making decisions that are really impactful frameworks are very important. So it makes a lot of sense and I'm glad that there are people doing this work in the market for sure.

You spoke a little bit about that, but Treasury automation isn't new. AI is changing both the pace and the nature of what gets automated. As someone who works closely with treasury professionals at different stages of their careers, do you believe that treasury leaders should be thinking about workforce transformation right now? What changes and what turns out to be more durable than people expected, in your opinion?

Jessica Oku: I believe Treasury leaders need to stop viewing workforce transformation as a technology initiative and start viewing it as a capability transformation. For years, Treasury automation focused primarily on transaction efficiency: we automate payments, we perform reconciliation, we optimize reporting and then connectivity. AI is different because it is beginning to automate elements of analysis and also decision support. So that then creates understandable anxiety because some of the activities professionals spent years getting good at are becoming increasingly automated. But what they find interesting is that the most valuable capabilities are becoming even more important, not less.

Critical thinking is one of them. Critical thinking has become more important now, judgment as well has become important, like you highlighted earlier. So even risk assessment as well as stakeholder influence. The most important one I always talk about is business partnering, because I see Treasury as a strategic business partner. And that's why when I'm coaching, in my coaching programs, I always talk about the two key mandates that Treasury professional has, which is to ensure the company is solvent and then to protect profitability.

So what I believe is that the Treasury professionals of the future will spend less time gathering information and more time interpreting the information they gather. And in many ways, Treasury is moving from being an information processing function towards becoming a decision intelligence function. The Treasury leader should therefore begin to focus less on protecting tags and more on developing capabilities that machines themselves, which is AI, struggle to replicate just like strategic thinking, communication skills, commercial awareness, leadership, sound judgment, especially under uncertainty. We know the global macroeconomic environment is quite uncertain. Policies change and all of that. So I believe that the organizations that will really win in the future are not necessarily the ones that have the most technology or AI. But they will be the ones with people who really know how to work intelligently alongside AI and technology.

Tanya Kohen: I also believe in AI complementing the workforce rather than replacing. And then I very much agree with what you just said — let's spend less time on the tasks that don't bring value. And in my mind, it's like retrieving information, trying to understand how to run reports and systems. This used to be such a big thing, right? You start a new job as a Treasury professional, first thing you're asked — have you worked with the system? Why is it important? Because each system has its own rules and this is how you run reports. This is what you can access, this is what you can do. So I think in the future maybe this type of work shifts a little and we have machines do more of that for us, but this is not where the value is. The value is what do you do with this information, how do you analyze this report?

Jessica, you've built frameworks, the Cash Flow Prioritization Matrix, career path tools, operating model sheets that sit in the intersection of process design and human decision making. As AI takes on more of the process layer, how do you see the relationship between people, processes, and technology shifting inside Treasury? And does it change what good process design looks like in the first place?

Jessica Oku: Historically Treasury transformation has often been viewed as the balance between people, process, and technology. And what I think is that AI changes that relationship significantly. Why I say this is that technology is no longer simply executing processes, it is increasingly participating in the analytical layer of the process. And as a result, process design itself becomes more important, not less important.

The future of Treasury operating model will not be designed around what humans do. It will be designed around what humans should do and what machines should do. So that's segregation and separation of work. And the thing about good process design in this AI era starts with understanding where judgment is required and where judgment is not required. For example, if a decision requires interpretation of business context, risk appetite, stakeholder relationships, regulatory considerations, or strategic objectives, humans should remain deeply involved. But if the tasks involve pattern recognition, better aggregation, exception identification, or repetitive analysis, then technology, I believe, should take the lead.

I often describe it as moving from workflow automation to judgment optimization. The most effective Treasury teams will create operating models where technology expands human capacity rather than replaces human accountability. The real competitive advantage I see will not come from just having AI alone, but it will come from designing an environment where people and AI together can operate in areas where they create value the most. Remember, I said, the language organization speaks is value. So it's that working together, that partnership will ensure that the value or maximum value is created.

Tanya Kohen: You put it really well. I'm going to take away some of your formulated things from today because it's two extremes right now, I think, in the market. People are telling “everyone is being laid off and replaced by AI” versus the other side — “AI is going to do much better than us humans.” And neither of this is true really, but what you're describing, working in partnership, and intelligently complementing human skills with machine capabilities, and then enhancing value at the end of the day, that's what's important for the organization. No one cares how much time you spend writing emails. You shouldn't spend too much. But what stakeholders do care about is how the decisions are being made.

And then to your point I also wanted to comment that there are policies, so whenever policies are involved the decisions have been already made so to speak. There is no need to spend additional time on judgment versus there are other instances when markets are changing and volatility is different. And this is the space where you should gather as much data information as possible and make those decisions. So definitely human judgment, I loved the way you framed all this.

Let's move to the next question. There is an approach to AI adoption in Treasury that optimizes entirely for speed, and an approach that uses the time AI frees up to do deeper analysis and better risk management. In your experience, what determines which approach an organization takes? And what does it actually take to protect a team's critical thinking capacity as automation increases?

Jessica Oku: I love this question actually. One of the biggest mistakes organizations make is assuming that automation reduces accountability. It doesn't. What it does is it increases the need for accountability. In Treasury, we know that every decision ultimately has an owner. AI can only recommend, AI can analyze, AI can prioritize, but accountability cannot be delegated to an algorithm.

Sound judgment, for example, begins with clarity around decision rights. And then everyone should know where AI is informing decisions. So what area we're going to leverage AI to inform decisions, where humans need to be involved in approving these decision recommendations, and where escalation is really required. I think it's just being able to find that balance right there. The failure mode I see most often is what I call “automation complacency”. And this is where teams begin trusting output simply because they are generated by sophisticated systems. Now the danger is not that AI makes mistakes. Humans make mistakes too.

I was on a webinar recently where this professional was talking about all of the AI capabilities and all of that. And then in the chat, someone just said that AI hallucinates. And he said, “you hallucinate as well.” So, it is what it is. But where the real danger is when people stop challenging assumptions. I mean, we know that good governance requires explainability, transparency, auditability, and regular challenge. So what I think Treasury leaders must do is to create environments where professionals are expected to question outputs, and not just simply accept them because AI generated them. So that's where human judgment, wisdom, critical thinking come into play right there.

Tanya Kohen: And you just touched on governance and human oversight. On this note, can you expand a little more on AI-driven decisions? What does sound governance look like in practice, and what are the failure models you see most often when organizations keep this conversation?

Jessica Oku: I often tell Treasury leaders that efficiency is not the objective. Really, when we're speaking about governance, we see how that governance is very important when you are leveraging AI. Because at the end of the day, you want to ensure that the right framework or structure is put in place. Because leveraging AI and all of that is what you want to leverage, all of that information, the data, the capability to be able to make better decisions. That definitely achieves the organization's objectives or creates values. And one of the things that I often say is that we know that efficiency simply creates capacity, but the question is what do you do with that capacity.

Some organizations use automation to reduce headcount or accelerate execution. Others may use it to deepen analysis, strengthen forecasting, improve scenario analysis, and enhance risk management. But the difference comes down to leadership philosophy. And also that's where the framework or the background, I would say, foundation is where governance sits in. So if leaders view Treasury as a cost center, they optimize for efficiency. If leaders view treasury as a strategic function, then they reinvest the efficiency gains into higher value decision making.

But one of the key things to do, even around the governance, it’s very important to put that foundation in place, protecting critical thinking requires intentionality. Teams need to be exposed to complex business problems and then be able to work with AI to see how these business problems can be resolved. They definitely need to do scenario analysis, they need to collaborate with cross-functional teams. Otherwise, there's a risk that people become just operators of systems rather than thinkers who challenge those systems. And everything in terms of looking at the governance perspective is the foundation upon which they can do all of this effectively.

Tanya Kohen: I love how our conversation revolves around AI and new technology, but you keep grounding it in critical thinking and in how people should think about their work. The mere fact that the person should stay the final decision maker and then it's only a question of process.

And you also spoke about the importance of process design and I agree as well, if you design the process so that the end point is always where due diligence sits and where the human eyes are, that's the most important thing. Errors will happen and, as you just said, humans make errors probably even more than machines do. And so if there is a good process in place and it can be corrected and caught in time, that's what's important. And then never lose the goal. So whatever organization is trying to achieve, that's the most important part.

On this note, let's talk about the future probably, and you thought carefully about where this function is heading. So what does Treasury look like in 2035? Not the technology stack, but the role itself, the skills that matter, and the profile of people who lead it.

Jessica Oku: I love this question. Because when I think about Treasury in 2035, which is about nine to ten years away from now, I see the function or I see the Treasury functionality really going towards the strategic perspective. I talked about the two mandates of Treasury professionals and how I like to put it. Many of their operational activities we'll see will be highly automated. Cash positioning, reconciliations, forecasting inputs, risk reporting, exception monitoring, all of this will largely happen in the background because AI and technology will have the capability to do all of that.

The treasury leaders will spend significantly more time advising executive leadership on things like capital allocation, on enterprise resilience, liquidity strategy, geopolitical risk, or funding structures and anything that has to do with strategic growth decisions. So the most successful treasurers I see by 2035, will look less like transaction managers or operators, like people like to call it, and more like strategic business partners. And the skills that will really matter will be strategic thinking, systems thinking, data interpretation, influence, leadership, communication, and decision making under uncertainty. Because we're going to see more of that uncertainty in the global landscape. Now the Treasury leaders of 2035, finally, will not only be valued because they know where cash is, or they can identify risk, but they will be valued because they know what the organization should actually do when those circumstances or those events crystallize.

Tanya Kohen: I'm looking forward to this future for sure. And also personally I'm a big fan of cross-functional collaboration within an organization. So that's also my hope that maybe finally the silos stop existing in the future and more functions — even within finance, many functions are still siloed and people don't talk too much, and they should. But even cross-functional, in broader organizations, business units and procurement and all those functions do collaborate more. And because Treasury can be valuable in so many areas of organization, almost every process touches cash, either starts with it or ends with it. So there is room for improvement in so many ways, and the way Treasury internal training can help the business. So I hope that happens more often as well.

I have three final questions for you, our rapid-fire questions. You don't have to be very brief, but it's up to you. There are three questions I ask every guest, starting with what does an invention mindset mean to you?

Jessica Oku: That's a fantastic one, really. So for me, invention mindset is really the ability to look at a problem, and a problem probably everyone accepts and ask whether it should even exist in the first place. And for me it is about choosing to redesign rather than just simply optimize.

Tanya Kohen: What do you think is missing in today's tech stack for finance and operations?

Jessica Oku: The thing is, most technology helps organizations process information. We talked earlier about pattern recognition, aggregating, analyzing large sets of data. So all of this processing information technology does enable the capability to do that. But far less technology helps organizations prioritize decisions. So what I see, the next frontier will be decision intelligence, not just data intelligence like we have today.

Tanya Kohen: I love this, I have not heard this one before, interesting. I hope so too.

If you could improve one thing in a business, not necessarily tech related, what would it be?

Jessica Oku: We're coming back to that. What I just draw from what I said previously, I would improve decision quality. The reason I say that is that almost every organizational problem eventually traces back to decisions that were either delayed, misaligned, uninformed, or poorly prioritized. So I believe better decisions compounded to better outcomes everywhere else. This is really what I believe would improve businesses. If I was to talk to every business owner, or corporate, or enterprise, or institution, I believe in improving the quality of decisions because you get better outcomes by doing that.

Tanya Kohen: That's a great answer. Jessica, thank you so much for this conversation. What stays with me is that the invention challenge in Treasury right now isn't primarily about technology, but about people, in the best sense of that phrase. The tools are arriving and whether teams are ready or not, what determines the outcome is whether Treasury professionals have actively reinvented their relationship with their own expertise, what they rely on, what they let go of, and what kind of judgment they develop that no automation can replicate. Jessica has spent years building frameworks that help people do exactly that and this conversation is a great place to start. Thank you so much Jessica for joining me today.

Jessica Oku: It's such a pleasure, thank you. Such an insightful conversation and amazing work that you do. Thank you for having me.

Tanya Kohen: Thank you so much. And to our listeners, please subscribe to the podcast and leave your feedback and reviews. Share your story of invention with me on LinkedIn. And thank you so much for listening.