Agentic AI is fundamentally reshaping the finance function – faster than most people expect. CFO Kyanush Kay and Jochen Heßler (bdg) discuss the reality of Excel, the AI hype, and what it all means for the CFO role.
TL;DR
- Agentic AI is not a revolution, but the next stage of a transformation that has long been underway.
- The technology isn’t the problem. The data is.
- The CFO won’t be replaced – but someone using AI will replace those who don’t.
- Starting beats waiting. Always.
You walk into the office in the morning, and the monthly report is already on your desk. Variance analysis complete, outliers commented on, an initial assessment prepared for the board. No copy-paste, no hours spent wrestling with data.
This isn’t utopia. It is the current state of play – at least for those making use of it.
In the bdg webinar From Excel to Agentic AI, CFO Kyanush Kay and Jochen Heßler, VP Growth at bdg, discussed exactly this: where finance teams truly stand today, what is currently changing, and what role the CFO plays in it – or may no longer play.
“AI won’t take my job”
Let’s start with the question many people ask themselves, but rarely out loud: Will Agentic AI make the CFO obsolete?
Kyanush Kay has a clear answer:
"AI won’t take my job. A person using AI will take my job."
Kay comes from a controlling background – Air Berlin, start-ups, EPM software – and understands both sides: the operational reality of finance and what technology is already capable of delivering today.
His assessment is pragmatic: any CFO primarily focused on preparing the past – reporting, closing, historical analysis – is in an increasingly vulnerable position. This work can be automated.
By contrast, the CFO who runs scenarios, prepares decisions and steers the business has little to fear. Quite the opposite.
A shift 15 years in the making
The evolution of finance – from number provider to business partner – is nothing new. It has been a core theme in controlling for at least 15 years. And yet, in many organisations, it has not truly materialised.
Why? Not due to a lack of intent, but because of day-to-day reality. Anyone spending their time merging data from multiple systems, consolidating files and manually building reports simply lacks the capacity to think strategically.
Agentic AI can break this bottleneck – but only if it is actually used.
The reality in finance departments
- 99 % of CFOs report significant challenges in their finance function
(BARC CFO Agenda 2025, n=194) - 50 % of companies aim to reduce their reliance on spreadsheets
(BARC CFO Agenda 2025) - 35 % still rely on manual data transfers between planning and consolidation tools
(BARC FPM Score 2025) - 71 % of CFOs face increasing pressure to provide up-to-date steering information
(Horváth CFO Study 2025)
Where do you really stand? The five stages
To understand what distinguishes Agentic AI from previous developments, it helps to look at the evolution of analytics – not as theory, but as an honest self-assessment.
1. Descriptive Analytics – What happened?
Classic reporting. Representing the past. Excel remains very much at home here. Most organisations are familiar with this stage – some with this stage alone.
2. Diagnostic Analytics – Why did it happen?
Analysing variances, breaking down drivers, going deeper. Modern EPM systems perform strongly here. Those who have reached this stage already hold a tangible advantage.
3. Predictive Analytics – What will happen?
Algorithms, machine learning, automated forecasts. Technologically feasible ffor some time – yet many organisations struggle to fully adopt it. We will come back to why.
4. Prescriptive Analytics – What should we do?
The system provides not only a forecast, but a recommendation. “Increase price in segment X by 3%.” This requires both a solid predictive foundation and trust in the outcome.
5. Agentic AI – The agent acts
No longer just output awaiting interpretation. The agent plans, decides and executes – within defined boundaries and under human oversight.
Kyanush Kay describes it as a digital junior controller: independently identifying patterns, comparing drivers, flagging outliers and suggesting initial solutions.
Why so many get stuck at predictive
The honest answer: because you are expected to stand behind numbers you do not fully understand.
An algorithm that produces a forecast without explaining how drivers were weighted, what role randomness played, or where uncertainty lies presents a real challenge in finance.
As a CFO, you stand in front of investors, the board and the executive team. A black box is not an option.
This leads many back to what feels safe: Excel. At least there, they understand what is happening. That is understandable – but ultimately misguided.
The solution is not to go backwards, but to adopt systems that explain their outputs.
What Agentic AI can already do today
Data preparation
Filling gaps, identifying inconsistencies and integrating data from multiple sources – faster and more reliably than any manual process. This is not future thinking. It is already reality.
Proactive monitoring
Instead of spending hours searching for anomalies, the agent does it for you. It detects patterns, flags outliers and prepares initial commentary. Teams can focus on thinking, not searching.
Forecasting with statistical confidence
Forecasts provide not just a number, but a range. If a planner enters a value far outside historical norms, the system flags it: “Are you sure?”
This improves quality without overriding human judgement.
Scenario analysis with external factors
Inflation, commodity prices, exchange rates – external influences can be directly integrated into planning. Scenarios can be created in minutes rather than overnight.
Self-service for business units
With a solid data foundation, business units can conduct simple analyses themselves. This relieves finance – and strengthens its role as a partner rather than a service provider.
The prerequisite no one likes to talk about
None of this works without reliable data.
Kyanush Kay puts it bluntly: “Prescriptive or predictive analytics only works when the data is ready.” If data is still being copied from multiple systems, versions shared via email and Excel files manually consolidated, Agentic AI will have limited impact – regardless of the tool.
That is not a reason to wait. It is a reason to start right there.
The most expensive mistake: waiting for the right moment
A familiar pattern: management discusses Agentic AI, while the reality in departments remains Excel, with a seemingly vast gap in between. The result? Inertia.
“Where do I even start?” is a valid question. It should not become an excuse.
Jochen Heßler sees this regularly in practice. His advice is clear: start, do not wait. A well-defined initial use case can demonstrate tangible results within days. This creates clarity and builds trust – neither of which emerges in strategy meetings.
💡 PRACTICAL INSIGHT
A mid-sized company started with a single module for monthly P&L planning. Three months later, they added cash flow forecasting. Reporting that previously took three days was reduced to two hours.
What did the team do with the time gained? They analysed for the first time – rather than simply preparing data.
Source: bdg project experience
The hamster wheel will not stop on its own
Another reason for hesitation: lack of time. Day-to-day operations leave little room for change. That is true – but it is also a trap.
If time is consumed by constant data preparation, it will remain scarce until that work is automated. The solution is not more time. It is a clearly defined first step.
What really changes for the CFO
Less preparation, more judgement
When reporting and monitoring are automated, time is freed for what CFOs should be doing: evaluating outcomes, considering options and advising the business.
Faster responses when it matters
Geopolitical events, commodity prices, interest rate shifts – those who can model scenarios in minutes rather than weeks make better decisions.
Forecasts you can trust
Not because AI is infallible, but because it identifies patterns humans miss and makes uncertainty transparent rather than concealing it.
A different role in the room
A CFO who can tell sales in a budget discussion, “Pushing this segment will not pay off – the margins will not support it,” and back this up with current data rather than a three-week-old report is a very different counterpart. That is the difference.
CONCLUSION
Kyanush Kay’s message is clear: Agentic AI is not a passing trend. It is a stage in an ongoing evolution – much like Big Data and predictive analytics before it. And further stages will follow. Those who do not start today will be at a disadvantage tomorrow.
So, back to the original question – will the CFO still be needed? Yes. But not as the person producing reports. As the one making decisions based on them.
Everything else comes down to getting started.
Kyanush Kay is a CFO with a background in controlling, including roles at Air Berlin and in the start-up environment. Jochen Heßler is VP Growth at better decisions group (bdg), an international EPM and BI consultancy.
Frequently asked questions (FAQ)
What differentiates Agentic AI from the AI features my EPM tool already has?
Traditional AI features deliver output: forecasts, insights, dashboards. An agent goes further – it acts. It executes steps without the requirement for manual triggers. It’s the difference between an assistant that suggests and one that implements.
Do I need to clean up my data before starting?
Not completely – but partly, yes. A pragmatic approach: start with the use case where data is already relatively clean. That delivers quick results and shows where to go next.
How do I explain AI-generated forecasts to the board?
By using systems that explain their results: which drivers were weighted how, where uncertainty lies. This isn’t optional – it’s a requirement. Any system that can’t do this doesn’t belong in finance.
How long does it take to get started?
A prototype for a clearly defined use case can be built in a few days. Not a finished system – but a realistic understanding of what’s possible. And a far better basis for decisions than theory.
What about data security?
A question that must be answered before implementation. Where is data processed? Is it used to train models? What about GDPR and industry-specific compliance? Serious providers have clear answers.
