Group CFO shares where AI is making humans even more relevant in finance organizations

Annabella Bassler is Group CFO at Ringier, a diversified international media and technology company with more than 120 entities.

We sat down with Annabella to learn how AI is changing finance organizations — and where humans are becoming increasingly relevant. Here's what she said.

Shaping the future with AI

I have been Group CFO of Ringier since 2012, and my journey has been a continuous transformation.

When I started, digital contributed virtually nothing to our EBITDA. Today, it contributes more than 80%. Along the way, we completed around 80 M&A transactions with a total volume of approximately CHF 2.5 billion.

So, transformation is not new to me. AI is another chapter — but potentially a fundamental one. For the first time, technology is not only changing how quickly finance processes information. It is changing how we think with information.

That is the real shift: from finance as a function that explains the past to finance as a function that helps navigate and shape the future.

A diversified media and tech company

Ringier is a highly diversified international media and technology company with more than 120 entities across 19 countries.

This matters when we talk about AI. We do not live in a beautiful world where everything runs on one SAP system — press F9 and suddenly the truth appears.

Our reality — like that of many companies built over decades and through acquisitions — is a patchwork of systems, data structures, business models, currencies, and markets.

That makes finance wonderfully complex.

Our job is not only to consolidate numbers. We create transparency across complexity and turn diverse data into information that enables decisions. We protect and propel the company. AI can help us do both better.

How AI shifts finance from finding information to interacting with it

One important change we've made with AI was a shift from searching for information to interacting with information.

Historically, answering a management question meant finding the right report, identifying relevant data across different systems, reconciling definitions, and then building and interpreting an analysis.

This is particularly relevant to us because our data landscape reflects our history: many companies, acquisitions, markets, and systems. Our data universe is not perfectly standardized.

Annabella Answers

But the biggest change is not speed. It is where finance spends its intellectual energy. Instead of spending most of our time asking, “Where are the numbers?”, we increasingly ask, “Why is this happening? What does it mean? And what should we do?”

Through our AI and data initiatives, including our work with Palantir, we are increasingly connecting these data worlds and making them easier to interrogate.

But the biggest change is not speed. It is where finance spends its intellectual energy. Instead of spending most of our time asking, “Where are the numbers?”, we increasingly ask, “Why is this happening? What does it mean? And what should we do?”

That is where I want my finance people to spend their brains.

How AI changes planning and forecasting

Another example is planning and forecasting.

Many companies still run planning processes designed for a world in which information was expensive and computing power limited. We spend months creating an incredibly detailed future we already know will not happen exactly that way.

AI gives us the opportunity to move from annual prediction to continuous navigation. That means dynamic scenarios, probabilities, leading indicators, and much faster feedback loops.

The question should no longer only be: “What will our EBITDA be in twelve months?”

It should increasingly be: “What could change our EBITDA? How early would we see it? What would it mean — and what would we do?”

That is a fundamentally different finance organization.

One important rule for AI in finance

I don’t like drawing a hard line between decisions for AI and decisions for humans. Both belong at the table.

We sometimes call AI our new colleague. I want that colleague in the room for forecasting, scenario analysis, variance analysis, and risk assessment — and also for capital allocation, investments, and M&A. Why wouldn’t I?

If AI can challenge an investment thesis, identify patterns we miss, test assumptions against much more information, create alternative scenarios, or spot risks humans overlook, I want that perspective before we invest millions.

But there is one important rule: We must challenge our new colleague like every other colleague.

AI can be extremely powerful, but it can also be confidently wrong.

A financially optimal decision is not necessarily the right decision. Strategy involves context. Leadership involves values. M&A involves people. Sometimes the most important variable is the one not sitting neatly in your dataset.

So, it's humans plus AI — with human accountability. AI gets a seat at the table. Accountability does not leave the table.

How AI transforms management performance analysis

Take management performance analysis. Traditionally, the process begins with collecting data: actuals, budget, forecast, prior year, and operational KPIs — often from different systems and different businesses. Finance reconciles the information, identifies variances, asks the businesses for explanations, and eventually produces management insight.

The AI-augmented finance model changes the sequence.

Once the relevant data is connected, AI can help identify unusual developments, patterns, and material deviations, generate hypotheses about potential drivers, and allow finance to interrogate the data much more dynamically.

The human can therefore engage earlier, precisely where human intelligence creates the greatest value: interpretation and judgment.

I see the future workflow as: Data → AI detection → Human challenge → Business dialogue → Decision → Learning.

And I particularly like the last step. Because once we systematically learn from the decisions we made and what happened afterward, finance stops being only a reporting system. It becomes a learning system.

Why the new challenge with AI is focus

The biggest positive result of AI is not just cost reduction; it is the compression of time between question and insight.

Tasks that previously required several iterations — collecting, reconciling, analyzing, and preparing information for management — now happen much faster. That creates something I find much more valuable than efficiency: management bandwidth.

But AI has also created a new problem: abundance.

Suddenly, we can produce another analysis, another scenario, another presentation, another ten-page memo — with almost no marginal effort. You can even see it in everyday communication. Emails become longer because AI makes it so easy to write more. But more is not necessarily better.

I think one of the most valuable skills for CFOs in the AI age will be condensation.

What really matters? What are the two or three insights? What decision do we need to make? Keeping it short and simple suddenly has a whole new meaning.

The classic questions remain remarkably powerful: Why? What? How?

AI can expand possibilities almost infinitely. Leadership must create focus.

Why AI cannot be implemented in isolation

I have always believed that transformation rests on four interconnected dimensions:

• Processes
• Systems
• Organization
• Culture

Change one, and it inevitably affects the others. AI makes that even more visible.

You cannot redesign a process with AI without ensuring your systems support it. You cannot change the system without identifying process ownership. You cannot change responsibilities without considering capabilities, incentives, and culture.

Therefore, today, I would always think big before going small.

Start with the end state: How should finance work in an AI-augmented organization? What decisions should be better? Where should humans spend their time?

Then, work backward to processes, systems, organization, and culture — and embed AI across that picture rather than adding it as a fifth box on the side. Because AI is not another layer of the operating model. It increasingly runs through the operating model.

Why processes and data need to be optimized before adding AI

AI has not magically solved fragmented processes or poor data. That was an important lesson for me.

In a complex environment like ours, you quickly discover that intelligence on top of complexity does not automatically remove it.

If processes or ownership are unclear, or data definitions differ between businesses, AI can sometimes expose the problem faster. That is valuable — but it is not transformation yet.

My conclusion is: Don’t throw AI on top of a bad process. First, understand what the process should achieve. Then simplify it. Then decide where technology and AI genuinely add value. Otherwise, you may create a faster version of something you shouldn’t be doing in the first place.

Why experienced humans are still crucial to finance

Explainability remains critical in finance. A beautiful answer is useless if I cannot understand where it came from.

In finance, “the model says so” can never be sufficient evidence. We need clear lineage from source data through AI-generated insight to the ultimate decision.

And we need people who retain enough expertise to challenge the machine.

Annabella Answers

Explainability remains critical in finance. A beautiful answer is useless if I cannot understand where it came from.

How finance teams are changing

The cards are being reshuffled. And I find that exciting.

Traditional finance expertise remains essential, but suddenly, other capabilities become much more valuable: curiosity, data literacy, creativity, the ability to formulate great questions, and the confidence to experiment.

Through initiatives like our AI hackathons, we see people revealing talents that traditional finance structures might never uncover.

So, I don’t necessarily think first about fewer people in finance. I think about different superpowers becoming visible.

Why CFOs must understand psychology to be effective with AI

CFOs need to understand psychology. Seriously.

The technology will probably develop faster than your organization can absorb it. People will be excited, skeptical, frightened, curious, defensive — sometimes all before lunch.

So, if you want AI adoption, understand people.

Technology may power AI transformation. But it runs on human psychology.

How to change AI from a threat to an invitation

I'll offer three pieces of advice:

1. Start with the business question, not the technology. The AI universe is becoming a jungle of possibilities. As CFOs, our job is not to demonstrate all theoretical possibilities. Our job is to prioritize what matters. I would rather have five AI use cases that change the business than fifty impressive demos.
2. Get involved personally. Don’t delegate AI to IT, your digital team, or the youngest person in the room. Play with it. Challenge it. Use it. You cannot lead a transformation you have only read about in a PowerPoint presentation.
3. Probably most importantly, embrace the change and make it fun. I love our AI hackathons for exactly that reason. New combinations of financial expertise, curiosity, creativity, and technology appear. That is incredibly energizing.

So, don’t make AI a threat hanging over your organization. Turn it into an invitation. Give people permission to experiment. Let them surprise you. Celebrate curiosity.

Because I believe the biggest opportunity is not replacing talent with AI. It is discovering talent through AI that we didn’t even know we had.

Follow along

You can follow along with Annabella Bassler's work on LinkedIn. You can also check out her initiative, EqualVoice.