James: Hi, everyone. This is James from the CTRL+Listen Podcast, brought to you by Octopart. Today we have a special guest. It is John Hetherington, CEO of We Deliver Your Vision. Welcome to the show. Great to have you on.
John: Great to be here, James. Thanks for having me.
James: Anytime. To start, do you want to talk about what you do, the role of the company, and how you came to that point?
John: For sure. As you can probably tell from the accent, although I live in Canada now, I grew up in the UK. I came to Canada in 2005 and I’m really enjoying the North American lifestyle.
That journey actually kickstarted my business about six years ago. We started We Deliver Your Vision, and the goal there was, after working with some big companies like Nortel Communications and others in Canada, we saw some of the challenges finance teams were going through, especially CFOs.
I’ve always worked in financial services. I’m a bit of a nerd and a geek, so I love working with other nerds and geeks like accountants. I saw a real gap around how we help CFOs share their value and ultimately embrace new technology.
In the case of Nortel, it was a real shame to see that big communications company go downhill because of some of the challenges they were facing in finance. That really inspired me to launch We Deliver Your Vision.
Now we strive to close the books twice as fast, in less than two months, using automation and AI.
James: That’s where everyone is going right now in the industry. It’s great to see you operating in that space. One of the topics you and I talked about discussing today was the idea that it’s not “People, Process, Tech,” it’s “Process First.” Do you want to tell us a bit about what that statement means?
John: Yeah, for sure. This is a little bit controversial, but it’s truly my view based on what we’ve been through and what I’ve been through personally.
It goes back to one of the first AI projects I did in the year 2000, just coming out of the Y2K bug and the dotcom boom and bust. I was building out a software AI black box to create automated insurance quotes for a life insurance company.
That was a really big, hard lesson for me because, although we increased revenue for the life insurance company by about 15%—it was a really good revenue stream—we went to the board to present our results and I was expecting big things: promotion, pay rise, everything.
But they told me, “We’re canceling the project.” That was a real surprise, even though revenue had increased. What I found out was that, although revenue had increased, we’d lost connection with the customers. The company was known as The Friendly Life Insurance Society.
They were built on agents and reps having connection and conversations with clients, and my software was taking that away. It was speeding up and automating the life insurance quotes.
So, big lesson learned: although people are important, it’s really about making sure you align to the values of the company and understand that first. Then you know you can serve the people in the best way. That was a big lesson for me.
James: And how does that tie into what you’re doing now? How did that lesson translate to your current product?
John: Going back to Process, People, Technology, I always say yes, you absolutely have to have the right people, but everyone needs some sort of North Star—guiding principles, process, a way to do their best work.
The best team in the world will perform badly with a broken process.
My philosophy is, even if you have a two-person team or a hundred-thousand-person team, they need ways to work. They need processes, guidelines, steps to follow, and checklists.
I’m not talking about over-engineering or slowing things down. I’m talking about giving people the right direction so they can show their best work.
When we start with automation of any process, we always start with: what’s the goal? Then, how do we build out that process to give people the right guidelines, principles, and checklists to do their best work?
James: That’s something I’ve noticed people talking about online: you can have the best software imaginable, but if you’re not targeting it correctly or using it correctly, it’s basically useless.
Especially if you pull the human factor out and stop checking how things are going—you just run a process and assume it’ll do the job. If you haven’t established that in the correct way, it’s all for nothing.
John: Exactly. A bad process automated is still a bad process.
James: Right. It just keeps running.
John: Exactly. We were working with a client a couple of years ago. They called me in because they had done some automation on their finance processes around paying invoices.
The problem before automation was that some invoice payments were duplicated. There was human error; they were paying some invoices twice.
They brought in automation software, and because they didn’t fix the process, the automation was still paying the invoices twice. If anything, it was paying them twice more often.
We came in and the first thing we always do—and I can help your audience with this with a couple of free checklists—is an initial audit on the process.
Understand what we’re doing, why we want to do it, and what the bottlenecks are. Even that alone, before the technology and automation, gives quick wins and benefits to the team.
James: Obviously this sort of data analytics space is relatively new. We’ve had it for a while, but in recent years it’s really ramped up and become a hot topic and trend. What do you see people losing out on by not getting on board with this?
John: By not getting on board with the data analytics side of things?
James: Exactly. What are they losing out on by not taking this seriously and paying attention to what’s there?
John: The key thing about data is it enables metrics and measuring your success. It tells you if you’re achieving your goals. That’s why I love data.
We use a five-step framework with the acronym RAPID: R-A-P-I-D. The D stands for Data.
Once you’ve established your process, automated it, and have the right people, you need to make sure you have data to capture results and measure progress so you can improve.
When you’re automating a financial close process, you might be looking at “days to close”—maybe it’s coming down from 15 to 13. More importantly, you measure things like:
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How many reconciliations are we doing after the books close?
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How many manual entries are we doing in addition to the automation?
We don’t want to measure too many metrics—that’s the number one pitfall. People try to measure 10 or 15 different KPIs and that’s a mistake because it gets overwhelming.
My advice with data is: start with three to five—ideally three—metrics that really impact the process or business you’re trying to improve.
James: That makes total sense to me. Let’s talk about scaling. This is something you can do with a really large company or a small company. How did you find the process of scaling your offering?
John: I call it the 3Ms. This was a big learning for me: the mindset for how I was going to do it, the metrics for achieving it, and managing the results and people as well.
The first thing around scaling my business, going back a few years, is the theory of constraints, which my business coach taught me. As you start to grow, you want to maintain momentum.
A lot of businesses, including my own at the beginning, would get busy. We’d get busy on delivery and stall out on marketing because we were too busy delivering for clients.
As a result, we’d finish projects and then suddenly have to start marketing again to get more leads and sales.
We realized the constraint was that we didn’t continue marketing because we got busy on delivery. To maintain momentum, we had to solve that.
Eating our own breakfast, we looked at the process:
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How do we onboard a client?
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How do we manage a client?
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How do we deliver?
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And how do we maintain marketing so it keeps going while we’re busy delivering?
As soon as we started tackling those constraints and maintaining momentum—which was uncomfortable—things really started to grow.
We asked: what’s our momentum? Back to metrics, we measured progress and asked how we keep going.
For example, we ended up hiring someone to specifically look after marketing so we could internalize that function. Our marketing continued while we got busy on delivery.
James: That makes sense. You’ve got to focus on the right areas to really find success. A term people use a lot—maybe not everyone knows what it means, but they’ve heard it—is “data silos.” These have been a big thing for a long time: collections of massive amounts of data that, for a long time, weren’t really able to be broken down into useful information. How has that changed with the adoption of AI in this space?
John: Interesting. I would say, if anything, AI is increasing the problem of data silos because AI is a tool that loves data. It’s trained on data. Generative AI, like ChatGPT, is built on data sources and patterns.
It sees patterns in the data to make predictions, and that’s how it responds when you give it a prompt.
For example, when you ask it, “Happy birthday, what comes next?” the pattern is “Happy birthday to you.” That data has to be accurate and available.
When you’re applying AI to business, the first thing we do is understand which data sources we want to apply the AI to, with the right controls. You don’t want someone searching a CEO’s salary or personal data, but you do need the right information available to do the job.
Again, it comes back to the first R in RAPID: Reason. What are we using AI for?
Typically, I start with productivity: documentation, summarizing long emails, creating communications—just getting prompt engineering laid out and done well.
To do that, it has to have access to the right data:
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Where documents are stored
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Where it stores the output from AI
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Which information you’re allowing access to, to create a better result
It’s the pipeline. I call them data pipelines. You want to connect your data. Start with the process: where does our data flow?
Create pipelines to those data sources. Then, within those pipelines, you’re turning taps on and off to allow the right data to flow to the right people.
That’s independent of AI—that’s just good data governance.
James: You mentioned before the call that there was one key skill that every business needs to have. What would you say that is?
John: I did a survey with about 400 CFOs when I was speaking in Palm Springs recently, and it was the same answer I’ll give you now.
You might think about technical skills or AI, but what’s coming up over and over again is that the one thing every company is looking for right now is critical thinking.
They really want their teams to think about the why—how things are done. AI is a great tool for enabling teams to do much more, much faster: more research, more analysis.
It doesn’t replace all the technical skills, especially in legal and accounting, but it does make a lot of the administrative and information work easier.
So what does that mean for humans? It means we need to apply our judgment, empathy, engagement, and critical thinking to solve problems using AI in the right way. That’s where critical thinking frameworks come in.
James: I didn’t think of that perspective, but you’re definitely correct. That’s where the human aspect is so valuable. You might go to AI with something and it comes back with 15 options for you, but only one or two are really practical for what you’re trying to do. You then have to determine which of these you actually use. AI isn’t necessarily going to do that for you.
John: Exactly. I list that out in my CFO Digital Playbook, which I’m happy to offer free to your listeners. If you drop “playbook” into the comments, I’ll make sure we send you a copy—no signup, no emails. I’ll just send it through, because I’m at a place in my career where the more I can help people work through this age of AI, the better.
James: I love that. We’ll have a link in the video description to that, so make sure you check there if you’re interested.
Back to AI—it’s the hot-button topic right now. Would you view it as something that is more of a tool people should learn to use to progress their careers, or do you buy into the mindset that it’s something that’s going to replace a large number of jobs in different industries?
John: Very interesting. My philosophy is that AI won’t replace you, but someone using AI will.
At the end of the day, AI is a tool. But if you look back over the last 20–25 years, we’ve moved from an era of information—the internet brought information.
Once we had access to information in the late ’90s and 2000s, suddenly “knowledge is power” became the mantra. I can get knowledge quickly.
What AI has done is enable everyone to get any knowledge very quickly. So the whole “knowledge is power” thing is breaking down.
I can upload a legal document and get good legal questions from a contract very easily with AI. It’s not necessarily 100% correct and I would still rely on a lawyer for very sensitive or important contracts, but the “knowledge is power” idea is starting to break down.
Now it becomes: how do we use that knowledge in the right way? That’s where critical thinking comes in.
Using AI to do a better job is why you’ll maintain a job going forward—you’re improving how you work.
James: Right. Looking back, we’ve had milestones in the last few decades where something becomes a base skill—just sort of expected to function in the current climate. I think this is another one of those milestones. You kind of have to have a basic grasp of this to be competitive in the workplace.
John: Yeah, it is very much a tidal wave coming at us. I’ll self-admit, I was a resistor of AI when it first came out. When ChatGPT launched in late 2022, even though I love technology and I’m a nerd and a geek—I’ve always embraced coding and IT—my first reaction was, “This is cheating. This is not real.”
It was my ego talking: “I can do better than this.” It took me a good few months, even as a technology advocate, to say, “Okay, let’s get into this.”
Now I love it. I use it multiple times a day. I love the power of it.
It gets addictive because now I’m using it to see how far I can push it: create new applications, take my data and present it in different ways, come up with recommendations for things I haven’t thought of.
I’m always the final check. I’m always there making sure it’s the right product for delivery—you can’t blame AI for the result. But it’s definitely taking us in different directions and helping us grow as a business and ultimately give better service to our clients.
James: I think the other thing is the speed at which it’s advancing. If you look at generative AI two years ago versus where it is now, it’s infinitely more effective.
John: Yes, and all the things around it as well.
We’re at what I’d call the sixth wave of economic explosion and impact on the economy. Previous waves go back to when we first invented metals and agriculture, then the automobile, electricity, and the internet.
This sixth wave—with AI, robotics, and clean, storable energy like batteries—is the first wave that’s truly autonomous.
AI can start to program robots, make predictions, and see patterns. We’re not even at general AI yet—it’s getting close. But coupled with access to more power and electricity and batteries, it’s starting to take over passive technologies.
Cars can’t do anything without us; AI can start to do things without us.
James: Right. It’s a new frontier for us. It’s a big learning curve.
John: Yes, which is why I love going back to critical thinking. Just ask “why.” Be curious.
The best thing we can do is that. I was watching an interview with Tom Cruise about his last Mission Impossible film. I admire the determination and commitment he puts into his movies.
He made a really good comment along the lines of: it’s not about not feeling afraid, it’s about knowing you can get through it. Don’t be afraid of feeling afraid.
James: Right, okay. Makes sense.
John: With the right people and the right conversations, you know you can probably get through it.
James: Definitely. It’s human to be afraid of what you don’t know or understand. It’s how you react to that, like you said, that determines your outcome. I have two broader questions for you before we come to a close.
The first is: during COVID we saw a complete reshaping of supply chains. It shifted from “just in time” to “just in case.” Did we see any sort of shift or change in the way we view and operate in data in that same period?
John: Yes, definitely.
COVID was a terrible time for a lot of people, but on the flip side, from a technology adoption perspective—even just enabling remote working quickly for many companies—it thrust technology front and center for pretty much everyone on the planet.
We talk about uncertainty and disruption these days—economy, politics, world events. But if we think back to what we’ve been through, we never want to go through it again, yet we got through it.
Most of us survived, adapted, and dealt with these challenges. When clients say, “There’s so much going on, too much change, we can’t even keep the lights on, let alone deal with AI,” my response is, “That’s cool. It is a problem, yes. Accept that a lot’s going on, but it doesn’t mean you can’t deal with it.”
Back to the Tom Cruise statement: embrace the feeling of being afraid. It’s normal. That’s okay.
We as humans have an amazing capacity to deal with change, some more than others, but we all move on.
Working through this disruption, the first thing you can do is look back at what we did before. What does this disruption look like compared to similar events? Then do scenario planning with your teams. Get your teams involved in: what could go wrong, what could we do differently? Don’t feel you have to solve it on your own.
James: Great answer. I love that. The last one is a bit broader. What are some of the trends in the data space you’ve noticed in recent years or even recent months that you find exciting or interesting?
John: Data quality is always number one.
Whenever I speak to clients, the first thing we do is enable a result very quickly—that’s our job, to create a result. When we’re shortening a month-end close process or creating a new dashboard, it very quickly surfaces problems in data accuracy.
Unfortunately, it’s just human nature: the way data is entered into a system is prone to mistakes.
In terms of trends, data quality and accuracy—especially with AI—is much more important. Making sure data is accurate and up to date is critical.
We can use AI to find data errors for you and help correct them.
The other trend: there used to be a huge push around structured data and classification, metadata, and so on. What we’re finding now is that AI is much better at finding the right data in the right silos.
So unstructured data, as long as it’s good quality and accurate, is much easier to use. Having more unstructured data—images, emails, documents, records, databases—can be fine because AI is much better at pulling that data out.
Now the focus is shifting from the structure of the data to how we’re using that data to make better decisions.
James: Fascinating. That was going to be the last question I asked, but you made me think of something. Data has been a commodity for a long time. Do you see AI shifting how that value is placed, as opposed to raw data versus refined data?
John: Good question. I still think there’s inherent value in data.
There was a discussion a few years back about whether we should put the value of data on balance sheets and how to capture that value. It was always difficult. I never saw companies reliably and consistently reflect the value of data on their balance sheets.
On the flip side, it comes back to how we’re using the data. In this age of AI, my view is that how we create AI tools—AI agents, bots, how we’re programming AI—that’s going to be the new value-add.
Once we start creating AI bots and agents and products, we have the potential to resell those, depending on the company. Some companies can lease them out to others.
Or you keep them within your company but give them to other departments. A finance automation bot might be used in a supply chain automation project. You start to share those bots and products between teams.
You’re already seeing it with companies like Replit, where you can crowdsource AI bots and effectively resell your bot to a public marketplace. These companies become platforms for you to resell the AI bot you’ve created. That’s going to be an interesting market to watch.
James: That’s very interesting. I never thought of it in that way. That’s a whole new industry.
John: Yeah, it’s fascinating.
James: I love this topic. It’s like, where can this go?
John: Exactly. My mind starts whirring: how do we use this for our own business? Then you see how other businesses are using it, and it’s fascinating.
I’d encourage everyone to start learning about it, even if it’s just 15 minutes a week. Go on YouTube and watch some robots falling over. Understand how AI is not being used well, because even that shows you its capabilities.
No doubt that’ll take you down rabbit holes to see how bigger AI is being used in the right way.
James: That comes back to the whole point of “Is it a tool that’s going to replace people?” There may be roles that are no longer as necessary, but, as you said, completely new industries are being born out of this, so new roles are coming too. It kind of balances out with jobs lost versus jobs created—it’s just a pivot.
John: There’s an interesting statistic. One of my followers on LinkedIn posted recently that, currently, the number of jobs with AI has actually increased overall. The negative effect of AI hasn’t really landed yet.
There will be changes in roles for sure—some jobs will change and some will go away—but at the moment, the number of jobs created by AI is increasing. We need AI developers, people to help fix data, people to use these tools.
If anything, it’s increasing productivity and economic output, which ultimately needs more people.
James: Makes total sense. It’s a new frontier. Very exciting—very scary in some senses, but very exciting in others.
John: It is. The uncertainty is there, and that creates fear, but again, to quote Tom Cruise: don’t fear the feeling of being afraid.
James: Exactly. To bring it all together, is there anything coming up with your company that people should be aware of or looking out for? Any new products or offerings?
John: We’re working on some exciting AI projects right now around creating a finance dashboard to not only find ways to automate the financial close process—the month-end close—but also to find other ways to improve the business.
We want to position finance as the strategic advisor to the company, looking holistically at the company with a company-wide dashboard: finding bottlenecks, scaling issues, even people issues, and making recommendations that still need humans to implement. That’s an exciting product we’re working on now.
James: That’s very exciting. If people are interested in getting in touch or taking advantage of what you’re offering, what are the best ways to do that?
John: Reach out to me on LinkedIn or on Instagram - @thejohnhetherington. And of course, that offer of the CFO Digital Playbook is always there. If they put “playbook” in the comments, I’ll make sure they get a copy.
James: And is that on your company website as well?
John: It’s not. If they email me or DM me on social, I’ll send a copy straight away.
James: Fantastic. John, thank you so much. This has been absolutely fascinating—great conversation topics.
