Tanya Kohen: Welcome to the second episode of the Invention Mode podcast. In episode one, we explored how invention differs from innovation, why it often delivers deeper impact and what it takes to create an environment where ideas can truly grow. Today, I’d like to look at how personal readiness fuels organizational transformation and why going beyond automation is essential.

Change often begins with a single step, a spark of curiosity when you notice that others overlook and a gut feeling that things can and should be different or simply the drive to challenge yourself. What's crucial is that even the smallest shift in the process can create ripple effects across an entire organization.

Let's dive into the power of hidden potential and that often goes untapped within individuals, within teams, and within the data organizations already have. Joining me is Tracey Knight, a seasoned expert in treasury transformation, to share stories of how a fresh perspective on existing data led to breakthroughs that reshaped the way organizations operate.

Tracey Knight: Hi, Tanya. It's so great to be with you today. We've known each other for a while, so this is a lot of fun.

Tanya Kohen: Exactly. And we've been co-speaking for quite some time now, so I'm also very excited to have you on this episode today. Let's jump right in. Let's talk about transformation. Many companies talk about being ready for transformation. What does it mean to you? And what's the role of personal readiness in this case?

Tracey Knight: You know, you said ready organizations. What does it take for a company to be ready? I think for the company level it's planning a strategic plan at an organizational level, especially around the data. A company that's really ready for transformation has already established a company-wide plan for how they're going to manage AI, how they're going to manage their data budget for it. They've got executive level approval, not just approval, but promotion for this movement forward. But that's at a company level.

At an individual level, I think personal readiness is equipping yourself for the changes that are happening. There are way too many resources, free ones even, available to all of us to learn more about AI and what it can really do, to learn how we can begin to harness it ourselves, to hear from others some of the wins and frankly failures that other companies have had. So I think personal readiness is about embracing the fact that all these changes are here now. We're no longer talking about the future, it's the present. And so making sure we equip ourselves and learn enough and educate ourselves so that we can embrace the change that's happening.

Tanya Kohen: That's a great perspective. It's a combination, right? It's never just about one person being this well, there are people that are catalysts of change. But I think for a real noticeable impact, it has to come both ways. Companies — culture-wise and resources-wise — need to be ready, but also on a personal level, I agree that we should do more to prepare ourselves and it's up to each and every person to get there, get into this mindset.

You've worked with many organizations on treasure transformation. What patterns do you see when it comes to hidden opportunities in their data?

Tracey Knight: I think the opportunities are around expanding, not contracting. And it seems like when we often think about the opportunities we have, especially in the finance department, we often think about saving money, how to reduce expenses.

But I think we need to spend as much time or more thinking about how we can do more, how we can maybe generate more revenue, how we can expand our operations rather than looking for savings and looking for ways to contract. And so the hidden opportunities in data are just a way of looking at things in a new way and perhaps looking at things more holistically. We often just think about our own small area, our own department, our own silo. But the hidden opportunity in data is if we're able to see a better view of our whole company, start seeing how things relate to each other. Something that's happening in a plant or a factory and how does that impact cash down the road? What things are they doing that maybe the treasury could be involved in, if we're looking at this from a treasury perspective? So I think we need to think more outside the box and stop just thinking about saving money. Let's think about earning more instead.

Tanya Kohen: I love this, it's really a more opportunistic approach. And I think there are so many synergies and opportunities for departments to work cross-functionally. I've been a big fan of cross-functional projects for a while, and I think for treasury, thinking about cash cycle and cash cycle conversions and improvements in this regard, that's a key to a lot of successes. But what you just mentioned about understanding the business a little more, it's very important too, because we've been talking about how important it is to bring treasury to the table early in any project, how important that part is. But how about us in treasury trying to proactively understand different processes and how we could contribute to each one of them. Because to me, cash is very much involved in almost every process and operation, either it starts with it or it ends with it. So there is an impact that treasury can make with bringing banking services and products and just more automation, more cross-functional collaboration.

Do you think there is always room for treasury to insert themselves into projects and processes and would that be helpful?

Tracey Knight: Most definitely. I think the way we do it though is by getting outside of our office and being focused on what our company really does. What's our main mode of generating revenue and then beginning to understand that process more. So whatever it is that your company does, if you’re in services — understand more about the services that you offer and the people who do it. If you're manufacturing — then get out and talk to the plants and have lunches and just get out of the box and get to know more people around the entire company.

And then, beyond that, you can do things to understand more about where your company is going. If you're public, you can look at the investor briefings and say “What are we saying to the outside world? What are we telling the street about where we're going and our opportunities for growth?” And then say “Okay, how does that relate to us? How can we offer something to help the operations in these strategic plans that our company has?” Sometimes it is getting involved in foreign exchange in areas where maybe you haven't previously done it. At many companies, foreign exchange is still handled by procurement or some buying organization that has to do with the buying of whatever physical things you might be getting. But those can be bifurcated. You can handle the financial aspects separate from the physical delivery. And those are things that treasury can often do very well. Treasury has extra expertise in managing those foreign exchange exposures.

So there can be many different ways we might get involved in operations, but you have to first understand them. So getting out of your office is key.

Tanya Kohen: Exactly. I guess one of the results of all this involvement and collaboration would be collecting data, just trying to bring this back to the actual action items that we can work on after we've discussed all these things cross-functionally and understood. So we're collecting data points, right?

I’m trying to think through the next steps. How do you think data drives or even reshapes the way we invent? How do we think through the collected data? How do we work with it and how do we find insights in it? How do we invent?

Tracey Knight: I think the first is getting more data in one place. Companies that proactively figure out how they're going to manage a “data lake” or whatever you wanna call it, getting more data in one place so that as a company, you have access not to adjust to the data associated with your one department, but data associated with the rest of the company as well. And think about that, this is what AI does best.

AI is great when you have lots of data. It is able to do what we often are unable to do as humans, which is sorting through a ton of data looking for the indications, the correlations, the patterns. That's what AI does very, very well. And the thing about AI, when you do it properly, all AI is doing is saying “Did you notice?” And it does so without a hypothesis. This is what can make it better than the human equivalent of “did you notice?” When we as humans do it, we often have our underlying thought process as to what we want the data to say. We've already determined an outline and therefore we make sure the data says what we want it to say. You hear people say over and over again — data can say anything for anybody. They just manipulate it to reinforce their point. But when done properly, AI has this ability just to do it without hypothesis.

I’m not saying it's good or bad. It's just a “did you notice?” You know, an example of that might be in, let's say, AP payment data. “Did you notice that payment to this one particular vendor has increased by 50% over the last two years?” Now it could be “that's a great thing.” Maybe you're able to buy a whole lot more from a vendor because your business has grown tremendously and your purchases have increased from a vendor because they've got the right pricing.

So it could be a great thing. Yep, we didn't actually maybe notice it in dollars per se, but it is an expected trend or it could be the opposite — it could be maybe that fraud is happening and someone has been somehow paying money out to a vendor that they shouldn't be. So the AI doesn't know whether this is a good or bad thing. It's simply a “did you notice?”, and so the more you're able to bring data together from a wide variety of sources in a way that makes good sense that the system can use, then the more the system, the AI, is able to see correlations between different things. And it just becomes a “did you notice?”: “Did you notice that when thing A increases, subject B decreases? Is that something that you've noticed?”

Then that's where the human comes in. It says “did you notice this?” And then you say “Yes, I did, and this is why, or — no, I didn't, let me investigate more.” And so I think having a data plan as an organization is the key to all participants being able to think differently, being able to understand the big picture of a company so much better.

Tanya Kohen: I couldn't agree more. And what you said about hypotheses, that's really key here because for someone in finance, for an analyst to really come up with a hypothesis, to check it, right? This would mean doing some research and proactively trying to find some areas that need further investigation.

This is one approach and this is the approach we've been taking always, I think, in finance, especially when we talk about sometimes a little backward-looking approach. That's pretty much what we mean. What were you noticing? Something's off. We're trying to figure out what it is exactly, how to help it. Then we'll have this hypothesis. Okay, let's test it. Some assumptions, let's see, right?

But what you're saying now, it's a whole different approach to improvement. I think this ensures continuous improvements because this is exactly what machines can give us, this relentless research, non-stop insights into the data. 100%, this is the future. And I like how you're framing this “Did you notice?” The decision-making support, so to speak, from AI tools — this is something that so many teams need.

Treasury teams, in their daily work and their improvements of their daily work, do you think that treasury teams often overlook the repetitive tasks that could be automated? Is it something that in your opinion, treasury departments pay enough attention to?

Tracey Knight: We definitely overlook lots of things. I think for a variety of different reasons, one is just a habit. There are some things that we do out of habit. We've been doing them for a long time. It seems like it's nothing to us. And so we don't question it. Sometimes we think it's not enough time spent on it to worry about, but we don't really always think of the cumulative time that we're spending.

And so you'll say to yourself “Well, it's only 15 minutes” Maybe it's 15 minutes a day and you're thinking that's not that much but 15 minutes a day — I didn't do the math ahead of time, but I don't know how many hours is that? In the work environment, if you had all that time back, what would you have done instead? Is there something on the bottom of your list of things that you say “one day I'm gonna get to that if I just had more time”, where you could have more time, you're just not doing things to help bring that time together.

And I think that it's common that there are many repetitive tasks, but because we are in such a habit of doing them, that it sometimes takes someone from the outside, someone new, looking at what you're doing to actually call it to your attention. You're even unaware of the fact that you're doing it. So it's not always, it's not negligence, it's simply just a lack of awareness. And then above and beyond that, I don't think we always value our time, nor our roles enough.

By that, I mean, we often don't speak to or downplay the value of treasury. When your family members, when you see those that maybe you don't see often, when they ask you about what you do, do you minimize the impact of what treasury is to a company? I think many of us do. We'll say things like “Well, you know, we manage the company's checkbook, it's like all the bank accounts, just like you do with your bank and your checkbook”, and we'll blow it off and we don't talk about the the real value that we bring to our organization not just externally — to our families — but internally. To other people in the company, treasury is almost invisible.

And I think we are a big part of that, that we don't talk about it enough and that we don't talk about its importance enough. If we really truly valued the value we provide and the importance of the role our companies have,I think we would value our time more. And if we valued our time more, then we would be looking for things to offload like repetitive tasks that are pretty mindless.

Some things are truly that. It's like when you get in your car and you're not thinking and you just start driving, you go to where you can drive without even knowing how you got there to places that you go often. It's that same way in our desk jobs. Some things we just do, we've been doing it that way for a while, we know exactly how to do it without even thinking about it. Those are the very tasks that can be easily automated because it doesn't require your brain power.

Tanya Kohen: Valuing our time seems like it's a mindset. Getting into this mindset of just consciously being conscious about your time, about your tasks and about continuous improvement thereof, because to me it's just more interesting. If you are doing more meaningful strategic work it's just more fun and more interesting than just being stuck in the routine thing. So I think the motivation is there, but it's a mindset. It has to be done proactively. It doesn't just happen to us. Same with those small inventions that drive the value for the organization forward.

What practical steps do you think someone can take today to embrace this invention mode and this new mindset?

Tracey Knight: I don't think there's anything really new or groundbreaking here. It's the same things that you likely heard your whole career, which is to ask why, to stay curious, to continue to learn both in the company and outside the company. Often we think that our companies are solely responsible for our education and learning. They are not. We need to be proactive in continuing to learn, especially with change happening at the rate that it is now.

You've got to not be complacent. There is no such thing. If you're getting a little bit older, you can't think about coasting out your last few years. The rate of change is happening way too fast for that. You may not make it to your last few years. If you are doing a job that is too repetitive, too simple, too easily automated. You know, when people say “Is AI gonna replace our jobs?” Everybody likes to say “No, no, no, people are necessary.” Well, that's not totally true, let's be honest. Yes, AI can replace your job if your job is too mindless. So it's up to you to make sure that it's not.

Find ways to utilize technology to automate all those things that really are mindless so that your job is one that absolutely requires your thought, your expertise, your personal experience in the things that you do. That's what can't be replaced.

So make sure that's what you are continually helping your job to be of real strategic value, not just a body keeping a seat warm. Those are the jobs where the seat altogether is going to go away.

Tanya Kohen: That's a very important and powerful piece of advice, for sure. Thank you so much, Tracey, for this interview. I will wrap up with three final questions and you can give short answers or not so short answers, but I would like to ask every guest — what does an invention mindset mean to you?

Tracey Knight: An invention mindset is just the readiness to question and the readiness to change. It's not being reactive, it's being proactive. It's looking for change, not just waiting for change to happen to you.

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

Tracey Knight: Some big problems have already been solved. If you don't have global cash visibility, that's your fault. That's something that's already solved. But I think there are some other problems that still exist mostly because of the silos that we talked about earlier. I would say what's missing in the finance tech stack, it's mostly because of what I'm gonna call “the big picture views.” It's bringing together more data in a less siloed way. Even within treasury technology that already exists all those legacy platforms often have a hard time bringing together data that doesn't easily tie together. So, at different asset classes you have a hard time getting that holistic view on one report, a true exposure, let's say to a big bank that maybe you have FX with bank accounts and some debt and some investments and maybe some derivatives. Can you get that one? A real true view of your exposure to that bank — all in one report. Legacy systems have a hard time with that just because of the way they were developed in the first place. So, and then if you try to go beyond just treasury, then it gets even harder to get that complete cash view. Even when we talk about something that's pretty basic, the cash conversion cycle that might involve cash and AP and AR and maybe some inventory, how often do you get a really good view of that? It requires an outside look, an outside way of being able to pull some of these things together and a view that is usable, a view from which you can make decisions. And I think that's where we are in finance right now. It's time for a bigger view of thinking about data in a new way so that we can start to make some better, bigger decisions.

Tanya Kohen: One of the best takeaways from today's conversation, agreed. If you could improve one thing in a business, not necessarily tech related, what would it be?

Tracey Knight: This is maybe a little controversial, but I wish people could really understand the beauty of diversity. You know, DEI, even the terminology is now out of style, mostly because the meaning was misunderstood and co-opted. Diversity should mean designing a group, teams that have a diversity of thought. And that diversity of thought is what leads to bigger and better ideas. We're not talking about just gender and race. We're talking about experience, and generations, and approaches, and backgrounds. That's true diversity. That's what we should be seeking. Hiring people that are just like you, that you identify with and you feel an immediate bond with, that's not a good thing.

We don't need a team of clones. They can't think and develop on the fly. Look for people that are different than you, that think differently than you, that challenge you, that maybe you don't understand and don’t feel an immediate bond with. That's a good thing. We'll get better and better ideas. Better ideas lead to more invention, more innovation, and eventually more revenue. Isn't that what we're really about from the work perspective?

Tanya Kohen: Exactly. Thank you so much, Tracey. I really enjoyed talking to you today. Thank you for all the stories and examples and all the just important and interesting insights that you brought to the conversation. I'm encouraging our listeners to subscribe and to listen to all the following episodes. And please share your story of invention with me on LinkedIn. I'm always happy to hear from the audience.