In a context where artificial intelligence is central to business transformations, finance departments are at a pivotal moment. Automation, predictive steering, skills evolution, transformation of operating models: AI no longer just questions the tools, but the very way the Finance function creates value.
On the occasion of Althéa’s participation in the Convergence summit on May 28, at the round table “AI & finance: evolution or revolution?”, David Bellaïche, Althéa’s CEO, shares his vision of a transformation that must be conceived, structured, and managed at the highest level.
For a long time, AI in finance was mainly about foresight or experimentation. Today, it is becoming part of concrete business practices. In your opinion, are we facing a technological evolution or a true revolution of the Finance function?
The Finance function has always been ahead in automation. For several years now, it has embraced process robotization, particularly with RPA, to optimize certain tasks and improve operational efficiency.
But artificial intelligence marks a different kind of step. It doesn’t just automate more: it forces finance departments to rethink their operating model, their skills, and their development trajectory.
My conviction is that the question is no longer whether AI will transform Finance. It is already transforming it. The real question is how finance departments will organize this transformation, and above all, whether they will anticipate it or suffer it.
To scale up, two major issues must be addressed. The first concerns data security, confidentiality, and sovereignty. These are essential issues, often driven by IT departments. The second, even more structuring for finance departments, concerns their target organization: what should their Finance Department look like in three years?
A significant part of finance professions will be transformed. Some jobs will be augmented, others automated, some will disappear, and others will be created. This requires forward-thinking today: what profiles will need to be recruited? What skills will need to be developed? What new professions will need to emerge?
This is where the real revolution lies. It is not only technological. It is organizational, human, and managerial.
There’s a lot of talk about AI in Finance. What use cases today bring real operational value to finance departments?
There are many use cases, but the challenge is not to pile them up. Real value emerges when these uses serve a clear vision for the transformation of the Finance function.
Among the first concrete cases is the relationship with finance department stakeholders: suppliers, internal clients, subsidiaries, business teams. Even today, many interactions go through emails, calls, or ticketing tools. Teams spend time qualifying requests, directing them, and identifying who should handle them. AI can automate part of this support, reduce low-value-added tasks, and improve the quality of service provided by Finance.
Another important application area concerns the invoice cycle and collection: reconciliation between invoice and purchase order, matching, customer reminders, risk alerts, account status analysis. These are processes where AI can generate quick and measurable gains.
There is also a major challenge around financial data. Finance departments have a considerable volume of data from subsidiaries, business units, and systems. AI can accelerate their consolidation, enrich their analysis, and above all, evolve Finance from a logic of observation to a logic of prediction.
With EPM tools, for example, finance departments can simulate different scenarios: inflation trends, geopolitical context changes, market tension, activity slowdown. AI must strengthen this ability to anticipate, reforecast more quickly, and inform decision-making.
But again, everything depends on the starting point. If we only consider use cases, we risk remaining in an opportunistic approach. If we start from the finance department’s target model, then use cases become levers for a structured transformation.
There are many announcements about AI. Which promises are truly being kept today, and which remain overestimated?
AI adoption has surprised everyone with its speed. We have never seen a technology adopted so quickly. For those who already use it daily, it is becoming almost indispensable.
However, we are still in a transition period. Many companies are in an experimentation phase: they launch POCs, test agents, identify use cases. This is a useful step because it helps acculturate teams and understand what the technology can offer.
But a POC does not make a transformation. The challenge for the coming years will be to move from an experimentation market to a scaling market.
This generalization will largely come through major publishers: ERP, EPM, HRIS, business solutions. AI will be progressively integrated into everyday tools. Users will be doing AI without necessarily realizing it, because the technological complexity will be absorbed by the solutions they already use.
This is probably where a very concrete promise lies: making AI accessible on a large scale by integrating it into existing processes and systems. The overestimated promise, however, would be to think that an isolated POC or a spectacular tool is enough to transform an organization.
Does AI only transform finance tools, or does it also redefine the role of finance teams and the CFO?
It profoundly redefines the role of finance teams. AI doesn’t just transform tools; it transforms the way of working, the expected skills, and Finance’s contribution to the company.
Tomorrow, some repetitive or transactional tasks will be automated. This does not mean that the Finance function will lose importance; on the contrary. It will need to reposition itself more towards analysis, anticipation, steering, internal consulting, and decision support.
The CFO’s role will therefore evolve. They will need to be able to lead this transformation with a global vision: which processes to rethink, which skills to strengthen, which profiles to recruit, which risks to control, what governance to put in place.
Finance will need to become more predictive, more agile, and more value-creation oriented. And that requires strong leadership.
We often talk about the opportunities related to AI. What are the main limitations or areas of vigilance for companies today?
The primary areas of vigilance concern data security, confidentiality, and sovereignty. For finance departments, which handle sensitive data, these are absolutely critical issues.
Today, the answers are not yet fully stabilized, particularly on the question of sovereignty. This can slow down some companies, or even lead some organizations to severely restrict the use of AI.
The second point of vigilance is scaling. Making a technology work for a single use case is one thing. Industrializing it at the company level, with governance, a run model, and an evolution capability, is another.
Finally, there is a major human factor. Not all employees adopt AI at the same pace. Some will quickly integrate these tools into their daily lives, while others will need support. A successful transformation will therefore require a significant effort in training, acculturation, and skills development.
AI will not succeed simply because it is powerful. It will succeed if organizations know how to create the conditions for its adoption.
Why do some AI projects quickly produce value while others struggle to get past the pilot stage?
The experience of RPA projects is very instructive. Many robotization projects failed even though the technology was mature. Why? Because some companies pursued technology for technology’s sake.
Before automating, processes must be rethought. A faulty process, even if automated, remains a faulty process. Technology alone does not correct organizational problems.
The second failure factor is the lack of anticipation of the run phase. Many projects are launched without considering how they will be maintained, adapted, and governed over time. Yet an environment evolves: rules change, systems change, uses change. If no one manages these evolutions, the solution eventually stops working.
With AI, we must avoid repeating the same mistakes. Success will not only depend on the quality of the use case but on the ability to integrate it into a sustainable trajectory: processes, organization, skills, governance, and maintenance.
How does Althéa currently support finance departments in this AI-driven transformation?
Our role is to help finance departments anticipate rather than suffer.
At Althéa, we start with a conviction: finance departments must first project themselves into their target operational model for three years. Before choosing a solution or multiplying use cases, it is essential to understand how the organization will evolve.
We help CFOs identify the impacts of AI on their jobs, skills, processes, and organization. Which professions will be transformed? Which tasks will be automated? Which employees will need to be trained? Which new profiles will need to be recruited? Which roles do not yet exist today but will become necessary tomorrow?
Based on this projection, we build a three-year transformation roadmap. It allows for progressive, structured, and coherent progress, aligning technological, organizational, and human challenges.
AI is therefore not approached as a mere tool. It becomes a lever for transforming the operational model of the Finance function.
In your opinion, what will the finance department look like in the coming years under the influence of AI?
Tomorrow’s finance department will be profoundly different. It will be more automated for repetitive tasks, more augmented in its analytical capabilities, and more strategic in its contribution to decision-making.
Certain departments, particularly accounting, will be significantly reshaped. Transactional tasks will be largely automated, while value will shift towards control, analysis, anticipation, and steering.
We will also see the emergence of more hybrid profiles, with dual Finance and data/IT skills. These profiles will be able to understand business challenges, retrieve data, structure it, build models, and derive useful analyses for the company.
The finance department will no longer just be the function that produces numbers. It will increasingly be the one that clarifies scenarios, anticipates risks, and helps the company make better decisions.
If you had to give one piece of advice to financial leaders who are still questioning AI today, what would it be?
I would tell them to start by looking at their organization in three years.
The first step is awareness. Before launching an AI project, one must ask what this technology will change in professions, skills, processes, and the way the Finance function is managed.
The finance departments that succeed will be those that have anticipated this transformation. Those that have built a vision, a trajectory, and an adaptability capacity.
AI is not a subject that can be treated separately from the organization. It will transform the very core of the Finance function. Now is the time to prepare for it.
