Chris Johnston at Celonis on Modernising Data for CFOs

What can CFOs expect from data? 

The answer seems obvious. Actionable insights come in many forms. As raw numbers, data can be the answer that sometimes executives aren’t even looking for. In ESG strategy, numbers reflect an ongoing tale of crisis that panics consumers. Some companies have turned to greenwashing, although data can also be the reason businesses are less inclined to take this approach. 

Behind every decision, a set of numbers have been working in the background as the basis, the driving force and sometimes even the reason not to. “Knowing the business” has been a central theme to the success of several CFOs, and what better way to know the business than study the numbers? 

Knowing the numbers positions any leader to make an informed decision about the (hopefully incoming) success of the business. So exists a divide in attaining that knowledge, which comes in the form of reactive and predictive reporting. 

Reactive reporting is closing the books when all is said and done; using hard facts as the last line of data. 

Predictive reporting uses data from the past to predict the future; anything from statistical models to past results. AI can be used to help model prospective results. 

Modernisation has quickly become a synonym for the use of AI in financial operations. 

How are banking chiefs reacting, is the real question. Chris Johnston, Head of Global Banking at Celonis, speaks to Finance Chief about how the CFO is using reporting and digitalisation in the next era of modern finance.

Please could you give me a brief overview of yourself and your experience? 

I've actually been in enterprise software since the day I graduated from university. Early in my career, I joined a start-up that was later acquired by IBM. While a lot of my colleagues saw the acquisition as a reason to move on, I looked at it as an opportunity to learn. I was still in my twenties, and suddenly I had access to this huge global organisation, so I treated it as though it were business school. I spent around 14 years at IBM, progressing from an individual contributor into sales leadership and global roles. It gave me a fantastic grounding in enterprise technology and the opportunity to work with some of the world's largest organisations. 

From there, I spent six years at Pegasystems, where I led integrated financial services go-to-market teams, focused on how we could better serve large global banks. Celonis approached me last year to lead the Financial Services business, and I've now been here for over a year. We've grown the team and evolved our industry offering, and we're starting to see some really encouraging results. For me, my career has been helping large financial institutions use technology to transform how they operate and ultimately deliver better outcomes.

Do you feel as though CFOs have picked up in their use of real-time reporting in the last few years? 

Absolutely. We've seen a real shift in what CFOs expect from their data. CFOs need to understand what's happening across their business in real-time and respond much faster. 

The challenge, particularly for large financial institutions, is that data is often fragmented across multiple legacy systems. You can have plenty of reporting but still lack a clear picture of how your business is actually operating which makes it difficult to identify issues such as cash-flow pressures. We want to demonstrate to regulators that decisions are grounded in business rules and operational reality. 

That's why I believe the finance industry is moving beyond real-time reporting and towards real-time operational intelligence. It's no longer enough for CFOs to know what is happening, they need to understand why it's happening and what to do next.

What other finance trends have you noticed?  

One of the most interesting trends I’ve noticed is how much the distinction between traditional banks and challengers is starting to disappear. We're seeing digital-first banks mature into large, established financial institutions, while incumbent banks are investing heavily to deliver the speed and experience customers have come to expect from fintechs or cloud-native banks. 

That changes the competitive dynamic. AI is accelerating the pressure. Every bank is looking at how it can use AI to improve customer service, reduce cost and make better decisions. But established institutions also have incredibly complex technology environments, often spanning decades of systems and processes. 

This is why we're seeing greater emphasis on modernising the experience without necessarily replacing the core. Banks need to understand how their operations work across those existing systems, identify where friction exists and then apply AI where it can deliver measurable value. The goal is to combine the agility associated with fintechs with the scale, trust and resilience of established financial institutions.

Reactive vs predictive reporting: what are the pros and cons of each?  

The information you get from reactive reporting is quite important because it gives finance teams a clear, factual view of what happened. You need this historical record for financial accountability. The issue is that by the time you've identified an issue, the financial impact may already have occurred.   

Predictive reporting however gives CFOs the opportunity to identify emerging risks or spot operational issues before they become more expensive problems. The challenge with predictive reporting is trust. A prediction is only valuable if you understand the data and operational context behind it. If your information is fragmented across different systems, you risk making predictions based on an incomplete picture of the business. 

We need to use the two together: using real-time operational data to understand what's happening today, historical data to understand why it happened, and predictive capabilities to determine next steps. That's when finance can move from reporting on the business to actively helping shape better business outcomes.

How vital is data to the Office of the CFO?  

Data is absolutely fundamental to the Office of the CFO, but today the challenge is less about access to data and more about making sense of it. 

Most large financial institutions have enormous amounts of data, often scattered across different systems and business functions. For CFOs, this data becomes more valuable when you can connect it to the processes behind the numbers. If cash flow changes, for example, you don't just want to see the number you want to understand what's driving the change and where the issue originates. 

This is also important when adopting AI. AI needs accurate data, but it also needs the business context around that data to make reliable decisions. For the CFO, having that trusted operational foundation is critical to improving forecasting and ultimately making faster, better-informed decisions.

Is automating decisions the future, along with AI? 

I think we'll absolutely see more decisions automated, but I don't think the future is about removing people from decision-making altogether. It's about being much more deliberate about which decisions can be automated and where human judgement still adds value. 

Finance teams make thousands of repeatable decisions every day. If AI has the right operational context, many of those decisions can be made faster and more consistently, freeing people to focus on exceptions and complex cases. The goal isn’t autonomy for autonomy's sake. It’s using automation and AI where they can demonstrably improve outcomes, while keeping people involved where judgement matters most.

How best can finance leaders prepare human workers for automation while ensuring trust is still at the forefront of the company mission?  

I think the starting point is involving people in the transformation rather than presenting automation as something that's happening to them. The best implementations I've seen start with the problems employees experience every day. Employees need to understand what the AI system is doing and when a human needs to intervene. Particularly in financial services, you can't have a black box making business-critical decisions without clear accountability. 

There's also an important skills element. As more routine work becomes automated, organisations need to help people develop the skills to work alongside AI, challenge its recommendations and manage exceptions. Successful automation isn't about replacing expertise; it's about making that expertise more valuable.

Should all CFOs adopt a real-time reporting dashboard by the end of the year?  

I wouldn't say every CFO needs to rush out and buy a real-time dashboard by a particular deadline. A dashboard on its own doesn't solve the underlying problem. Most large organisations already have plenty of dashboards. The problem is whether the information behind them is connected and provides enough context for leaders to make a coherent decision. 

If CFOs are pulling information from fragmented systems into a faster dashboard, they may simply be seeing the same blind spots more quickly. The real opportunity is to understand how processes are actually running across the organisation and connect that operational reality to financial outcomes. 

CFOs should focus on whether they can see what’s happening across the business and act quickly, allowing finance leaders to identify issues earlier and make better-informed decisions.