A Transformation That Begins with Real Work<\/strong><\/h2>\n\nIn companies, initial discussions around GEN AI often take a technical form: which tools to use, which data to open up, which platform to choose, which risks to manage?<\/p>\n\n
These questions are necessary. But they must not obscure the essential: transformation begins with a thorough understanding of the real work of Finance teams. <\/p>\n\n
How do management controllers, accounting teams, consolidation managers, credit managers, tax specialists, or FP&A teams spend their days? Which tasks consume significant time for limited value? Which analyses arrive too late to truly influence decisions? Which manual adjustments still hinder the ability to take action? <\/p>\n\n
For one major retail player, the finding was immediate. Finance teams spent a significant portion of their time explaining performance variances between stores, regions, and product categories. Dashboards existed. Data was available. But each reporting cycle still heavily mobilized teams to produce commentary, connect variations to one another, and prepare summaries for operational stakeholders. <\/p>\n\n
GEN AI was then conceived as support for the initial variance analysis. Not to replace the management controller’s judgment, but to prepare an initial reading: which items explain most of the variance? Which areas appear atypical? Which recurring comments appear in previous months? Which points warrant human investigation? <\/p>\n\n![\"\"](\"https:\/\/althea-groupe.com\/wp-content\/uploads\/2026\/05\/image_1_tableau_bord_financier.jpg\")
<\/figure>\n\nThe gain is not limited to time saved. It lies primarily in the shift in approach. Controllers spend less time producing a first draft of commentary and more time challenging causes, engaging with operational teams, and identifying action levers. <\/p>\n\n
Automate, Augment, Transform<\/strong><\/h2>\n\nOne common mistake is to speak of GEN AI as a homogeneous block. For a Finance Department, its impacts are actually very different depending on the activities. <\/p>\n\n
Some tasks can be automated: preparation of recurring summaries, classification of requests, document analysis, production of initial reporting commentary, searches within internal repositories.<\/p>\n\n
Others are rather augmented. The decision remains human, but it is better prepared, faster, more documented. This is the case for budget management, performance analysis, risk management, cash forecasting, or negotiation support. <\/p>\n\n
Finally, some missions are fundamentally transformed. The management controller becomes more of a performance partner. The accountant focuses more heavily on anomalies and process quality. The FP&A manager shifts from a consolidation role to a modeling, anticipation, and decision-support role. <\/p>\n\n
For an international industrial group, the analysis was conducted based on a mapping of Finance activities. The monthly close quickly emerged as a priority area: variance justification, collection of explanations from subsidiaries, analysis of atypical entries, production of commentary for the group. <\/p>\n\n![\"\"](\"https:\/\/althea-groupe.com\/wp-content\/uploads\/2026\/05\/image_2_reunion_gen_ia_valeur.jpg\")
<\/figure>\n\nGEN AI was envisioned as a close assistant: preparing preliminary analyses, flagging inconsistencies, comparing commentary from previous periods, proposing points of attention. Teams remained responsible for validation, but they focused their energy on risk areas rather than comprehensive review of already structured data. <\/p>\n\n
Productivity Is Only Part of the Issue<\/strong><\/h2>\n\nGEN AI is often approached from the perspective of productivity gains. This is legitimate. Finance Departments must produce faster, with greater reliability, in a context of continuous pressure on resources. <\/p>\n\n
But limiting GEN AI to an efficiency topic would be reductive.<\/p>\n\n
One of the major challenges is Finance’s ability to better support growth. In many companies, certain commercial or operational opportunities remain underexploited due to insufficient analytical capacity. The data exists, but it is not always leveraged in time. Scenarios are possible, but too time-consuming to produce. Markets are identified, but business models are not sufficiently simulated. <\/p>\n\n
For a fast-growing B2B services company, the initial topic concerned profitability management by customer. The organization had a rich database: contracts, billing history, time spent, operational incidents, delivery costs, commercial terms. But the analysis remained fragmented, complex, and delayed. <\/p>\n\n![\"\"](\"https:\/\/althea-groupe.com\/wp-content\/uploads\/2026\/05\/image_3_roadmap_competences_finance.jpg\")
<\/figure>\n\nGEN AI made it possible to envision a system helping Finance and sales teams better understand pockets of profitability and underperformance: which customers have degraded profitability? What factors explain it? Which contracts deserve renegotiation? Which segments resemble the most profitable customers? <\/p>\n\n
In this case, GEN AI does not only serve to reduce costs. It opens a growth perspective: better target markets, refine offerings, secure margins, direct sales efforts. <\/p>\n\n
The CFO, Guarantor of Trust<\/strong><\/h2>\n\nThis transformation raises a central question: who guarantees the quality of what is produced?<\/p>\n\n
In a Finance Department, error does not have the same status as in other functions. An imprecise comment, a poorly formulated assumption, or misinterpreted data can lead to a poor decision. GEN AI, because it produces fluent and convincing responses, can create an illusion of reliability. <\/p>\n\n
The CFO’s role therefore becomes critical.<\/p>\n\n
It is not about slowing down usage, but about managing it. Define authorized use cases. Clarify accessible data. Identify necessary validation levels. Implement traceability rules. Train teams to challenge produced responses. Distinguish exploratory uses from binding uses. <\/p>\n\n
For a major telecommunications player, this question of trust was placed at the center of the approach. Finance teams immediately saw the value of GEN AI for accelerating variance analysis, preparing summary notes, or assisting budget production. But the risk was clear: allowing individual uses to multiply without a common framework. <\/p>\n\n
The first step therefore consisted of prioritizing use cases according to their level of criticality. A document summary assistant does not involve the same requirements as a tool contributing to financial forecasting. Assistance with drafting management commentary does not carry the same level of risk as a system used to arbitrate investments. <\/p>\n\n
This approach avoids two pitfalls: uncontrolled enthusiasm and blanket blocking.<\/p>\n\n
Skills Will Evolve Faster Than Organizations<\/strong><\/h2>\n\nThe most underestimated issue is probably that of skills.<\/p>\n\n
GEN AI does not only transform tasks. It modifies the qualities expected of Finance teams. Knowing how to formulate a good question becomes a professional skill. Knowing how to interpret a generated response, detect its limitations, compare it to data, and put it in perspective becomes essential. <\/p>\n\n
In Finance Departments, this evolution will create new gaps. Some employees will adopt these tools very quickly. Others will remain hesitant, not due to lack of talent, but for lack of support, framework, or time to learn. <\/p>\n\n
For a transportation group, the analysis of Finance roles revealed a sensitive point. The most experienced profiles mastered the rules, exceptions, and subtleties of the organization. Younger profiles were often more comfortable with new tools. Value could only emerge from the meeting of the two. <\/p>\n\n
The approach was therefore designed as a transformation of practices: formation of pairs, identification of use cases, comparison of results to business realities, sharing of learnings. Experts provided judgment. More digital profiles tested uses and formulated queries. Together, they distinguished real gains from demonstration effects. <\/p>\n\n
Regaining Control of Time<\/strong><\/h2>\n\nIt would be naive to claim that GEN AI does not raise concerns. Some tasks will diminish. Some positions will evolve. Entire activities will be rethought. <\/p>\n\n
But an exclusively anxiety-driven narrative is often counterproductive. It reduces transformation to a threat, even though many Finance teams today suffer from chronic overload, an inflation of requests, increasing complexity of reporting, and difficulty focusing on substantive analysis. <\/p>\n\n
In several organizations, the most mobilizing message is not: “AI will replace part of the work.” It is rather: “AI can help us regain control of our time.” <\/p>\n\n
Regain control over excessively long closes. Over reports produced under pressure. Over manual adjustments. Over analyses requested too late. Over decisions made with incomplete information. <\/p>\n\n
For a healthcare company, Finance teams faced strong growth in data volume and management requirements. The work conducted identified simple use cases: automatic summary of budget files, preparation of variance analysis notes, assistance with supplier contract review, help formalizing commentary for business units. <\/p>\n\n
Taken separately, these cases might seem modest. Together, they outlined a trajectory: reduce operational pressure to restore analytical capacity, dialogue, and anticipation. <\/p>\n\n
Experiment Quickly, but Project Far<\/strong><\/h2>\n\n![\"\"](\"https:\/\/althea-groupe.com\/wp-content\/uploads\/2026\/05\/image_4_boussole_opportunites.jpg\")
<\/figure>\n\nFaced with the scale of the topic, some companies may be tempted to launch a major GEN AI program. This approach can sometimes be justified, but it carries a risk: delaying learning, distancing business units from the topic, and transforming GEN AI into a technology project before making it an operational lever. <\/p>\n\n
The most effective approaches often rely on a very concrete initial axis: a few well-chosen use cases. Simple enough to be tested quickly. Useful enough to produce value. Visible enough to engage teams. <\/p>\n\n
For a Finance Department, experimentation areas are numerous: reporting commentary, variance analysis, budget preparation, close support, document review, internal control, cash forecasting, profitability analysis, assistance producing Executive Committee notes, audit support.<\/p>\n\n
These use cases play an essential role. They make it possible to move beyond general discourse on AI, to concretely demonstrate what the technology can deliver, to reassure teams, but also to reveal the real conditions for success: data quality, expected level of control, evolution of practices, employees’ ability to adopt these new tools. <\/p>\n\n
But this first axis is not enough.<\/p>\n\n
For a CFO, the challenge is also to project 3-5 years ahead. Because GEN AI will not only transform a few isolated tasks. It will gradually modify the very structure of jobs, expected skills, career paths, and organizational balance within the Finance function. <\/p>\n\n
It therefore becomes essential to assess, for each job and each major skill family, the level of GEN AI impact: what will be automated, what will be augmented, what will remain highly human, what will require new skills, and what will give rise to new roles.<\/p>\n\n
This projection is critical. It makes it possible to anticipate the evolution of job descriptions, identify critical skills to develop, identify employees capable and willing to evolve toward these new roles, but also to clarify the hiring to undertake. <\/p>\n\n
A simple example illustrates this well: recruiting an accountant today without questioning what their role will become in three years constitutes a risk. Not because the role will disappear, but because it will be fundamentally modified. Tomorrow’s accountant will likely need to master anomaly analysis, data control, interpretation of results produced by augmented tools, understanding of automated processes, and the ability to interact with intelligent systems. <\/p>\n\n
The question is therefore no longer only: does this candidate master the accounting skills expected today?
This reflection applies to all Finance roles: management controllers, consolidators, FP&A managers, accountants, credit managers, tax specialists, treasury managers, internal control or audit teams. Not all will be impacted in the same way, but all must be viewed through this new lens. <\/p>\n\n
This is where part of the CFO’s responsibility lies. It is not only about choosing tools or launching experiments. It is about preparing tomorrow’s Finance organization: its roles, its skills, its career trajectories, its hiring needs, and its new balance between human expertise and technological power. <\/p>\n\n
The right question is therefore not only: “Which GEN AI use cases can we test quickly?”<\/p>\n\n
It is also: “What Finance Department do we want to build in 3-5 years, and how are we preparing today the women, men, and skills who will make it work?”<\/p>\n\n
A New Frontier for the Finance Function<\/strong><\/h2>\n\nGEN AI arrives at a particular moment for CFOs. The Finance function is already engaged in numerous transformations: ERP modernization, cloud migration, process automation, data visualization, evolution of management control, strengthening of ESG management, cost pressure, increased expectations from executive management. <\/p>\n\n
GEN AI does not replace these projects. It accelerates them, disrupts them, sometimes reconfigures them. <\/p>\n\n
It requires looking at existing processes differently. Distinguishing what relates to production, analysis, and decision. Questioning the value of roles. Reviewing skill paths. Bringing together Finance, business units, IT, HR, and transformation departments. <\/p>\n\n
It also gives Finance a rare opportunity: to position itself not only as guardian of the numbers, but as architect of a broader economic transformation.<\/p>\n\n
CFOs who take on this topic early will have an advantage. Not because they will have deployed the most visible tool, but because they will have known how to ask the right questions before others: which roles will be transformed? Which skills must we develop? Which risks must we manage? Which gains do we truly want to capture? Which growth opportunities can we make accessible? <\/p>\n\n
GEN AI will not eliminate the CFO’s role. On the contrary, it reinforces its importance. <\/p>\n\n
In an environment where information production becomes faster, more abundant, and more automated, financial discernment becomes even more valuable. Tomorrow’s CFO will not only be the one who guarantees the reliability of the numbers. They will be the one who helps the company transform this new analytical power into sound decisions, sustainable performance, and conquest of new growth areas. <\/p>\n\n
<\/figure>\n\nThe gain is not limited to time saved. It lies primarily in the shift in approach. Controllers spend less time producing a first draft of commentary and more time challenging causes, engaging with operational teams, and identifying action levers. <\/p>\n\n
Automate, Augment, Transform<\/strong><\/h2>\n\nOne common mistake is to speak of GEN AI as a homogeneous block. For a Finance Department, its impacts are actually very different depending on the activities. <\/p>\n\n
Some tasks can be automated: preparation of recurring summaries, classification of requests, document analysis, production of initial reporting commentary, searches within internal repositories.<\/p>\n\n
Others are rather augmented. The decision remains human, but it is better prepared, faster, more documented. This is the case for budget management, performance analysis, risk management, cash forecasting, or negotiation support. <\/p>\n\n
Finally, some missions are fundamentally transformed. The management controller becomes more of a performance partner. The accountant focuses more heavily on anomalies and process quality. The FP&A manager shifts from a consolidation role to a modeling, anticipation, and decision-support role. <\/p>\n\n
For an international industrial group, the analysis was conducted based on a mapping of Finance activities. The monthly close quickly emerged as a priority area: variance justification, collection of explanations from subsidiaries, analysis of atypical entries, production of commentary for the group. <\/p>\n\n<\/figure>\n\nGEN AI was envisioned as a close assistant: preparing preliminary analyses, flagging inconsistencies, comparing commentary from previous periods, proposing points of attention. Teams remained responsible for validation, but they focused their energy on risk areas rather than comprehensive review of already structured data. <\/p>\n\n
Productivity Is Only Part of the Issue<\/strong><\/h2>\n\nGEN AI is often approached from the perspective of productivity gains. This is legitimate. Finance Departments must produce faster, with greater reliability, in a context of continuous pressure on resources. <\/p>\n\n
But limiting GEN AI to an efficiency topic would be reductive.<\/p>\n\n
One of the major challenges is Finance’s ability to better support growth. In many companies, certain commercial or operational opportunities remain underexploited due to insufficient analytical capacity. The data exists, but it is not always leveraged in time. Scenarios are possible, but too time-consuming to produce. Markets are identified, but business models are not sufficiently simulated. <\/p>\n\n
For a fast-growing B2B services company, the initial topic concerned profitability management by customer. The organization had a rich database: contracts, billing history, time spent, operational incidents, delivery costs, commercial terms. But the analysis remained fragmented, complex, and delayed. <\/p>\n\n<\/figure>\n\nGEN AI made it possible to envision a system helping Finance and sales teams better understand pockets of profitability and underperformance: which customers have degraded profitability? What factors explain it? Which contracts deserve renegotiation? Which segments resemble the most profitable customers? <\/p>\n\n
In this case, GEN AI does not only serve to reduce costs. It opens a growth perspective: better target markets, refine offerings, secure margins, direct sales efforts. <\/p>\n\n
The CFO, Guarantor of Trust<\/strong><\/h2>\n\nThis transformation raises a central question: who guarantees the quality of what is produced?<\/p>\n\n
In a Finance Department, error does not have the same status as in other functions. An imprecise comment, a poorly formulated assumption, or misinterpreted data can lead to a poor decision. GEN AI, because it produces fluent and convincing responses, can create an illusion of reliability. <\/p>\n\n
The CFO’s role therefore becomes critical.<\/p>\n\n
It is not about slowing down usage, but about managing it. Define authorized use cases. Clarify accessible data. Identify necessary validation levels. Implement traceability rules. Train teams to challenge produced responses. Distinguish exploratory uses from binding uses. <\/p>\n\n
For a major telecommunications player, this question of trust was placed at the center of the approach. Finance teams immediately saw the value of GEN AI for accelerating variance analysis, preparing summary notes, or assisting budget production. But the risk was clear: allowing individual uses to multiply without a common framework. <\/p>\n\n
The first step therefore consisted of prioritizing use cases according to their level of criticality. A document summary assistant does not involve the same requirements as a tool contributing to financial forecasting. Assistance with drafting management commentary does not carry the same level of risk as a system used to arbitrate investments. <\/p>\n\n
This approach avoids two pitfalls: uncontrolled enthusiasm and blanket blocking.<\/p>\n\n
Skills Will Evolve Faster Than Organizations<\/strong><\/h2>\n\nThe most underestimated issue is probably that of skills.<\/p>\n\n
GEN AI does not only transform tasks. It modifies the qualities expected of Finance teams. Knowing how to formulate a good question becomes a professional skill. Knowing how to interpret a generated response, detect its limitations, compare it to data, and put it in perspective becomes essential. <\/p>\n\n
In Finance Departments, this evolution will create new gaps. Some employees will adopt these tools very quickly. Others will remain hesitant, not due to lack of talent, but for lack of support, framework, or time to learn. <\/p>\n\n
For a transportation group, the analysis of Finance roles revealed a sensitive point. The most experienced profiles mastered the rules, exceptions, and subtleties of the organization. Younger profiles were often more comfortable with new tools. Value could only emerge from the meeting of the two. <\/p>\n\n
The approach was therefore designed as a transformation of practices: formation of pairs, identification of use cases, comparison of results to business realities, sharing of learnings. Experts provided judgment. More digital profiles tested uses and formulated queries. Together, they distinguished real gains from demonstration effects. <\/p>\n\n
Regaining Control of Time<\/strong><\/h2>\n\nIt would be naive to claim that GEN AI does not raise concerns. Some tasks will diminish. Some positions will evolve. Entire activities will be rethought. <\/p>\n\n
But an exclusively anxiety-driven narrative is often counterproductive. It reduces transformation to a threat, even though many Finance teams today suffer from chronic overload, an inflation of requests, increasing complexity of reporting, and difficulty focusing on substantive analysis. <\/p>\n\n
In several organizations, the most mobilizing message is not: “AI will replace part of the work.” It is rather: “AI can help us regain control of our time.” <\/p>\n\n
Regain control over excessively long closes. Over reports produced under pressure. Over manual adjustments. Over analyses requested too late. Over decisions made with incomplete information. <\/p>\n\n
For a healthcare company, Finance teams faced strong growth in data volume and management requirements. The work conducted identified simple use cases: automatic summary of budget files, preparation of variance analysis notes, assistance with supplier contract review, help formalizing commentary for business units. <\/p>\n\n
Taken separately, these cases might seem modest. Together, they outlined a trajectory: reduce operational pressure to restore analytical capacity, dialogue, and anticipation. <\/p>\n\n
Experiment Quickly, but Project Far<\/strong><\/h2>\n\n<\/figure>\n\nFaced with the scale of the topic, some companies may be tempted to launch a major GEN AI program. This approach can sometimes be justified, but it carries a risk: delaying learning, distancing business units from the topic, and transforming GEN AI into a technology project before making it an operational lever. <\/p>\n\n
The most effective approaches often rely on a very concrete initial axis: a few well-chosen use cases. Simple enough to be tested quickly. Useful enough to produce value. Visible enough to engage teams. <\/p>\n\n
For a Finance Department, experimentation areas are numerous: reporting commentary, variance analysis, budget preparation, close support, document review, internal control, cash forecasting, profitability analysis, assistance producing Executive Committee notes, audit support.<\/p>\n\n
These use cases play an essential role. They make it possible to move beyond general discourse on AI, to concretely demonstrate what the technology can deliver, to reassure teams, but also to reveal the real conditions for success: data quality, expected level of control, evolution of practices, employees’ ability to adopt these new tools. <\/p>\n\n
But this first axis is not enough.<\/p>\n\n
For a CFO, the challenge is also to project 3-5 years ahead. Because GEN AI will not only transform a few isolated tasks. It will gradually modify the very structure of jobs, expected skills, career paths, and organizational balance within the Finance function. <\/p>\n\n
It therefore becomes essential to assess, for each job and each major skill family, the level of GEN AI impact: what will be automated, what will be augmented, what will remain highly human, what will require new skills, and what will give rise to new roles.<\/p>\n\n
This projection is critical. It makes it possible to anticipate the evolution of job descriptions, identify critical skills to develop, identify employees capable and willing to evolve toward these new roles, but also to clarify the hiring to undertake. <\/p>\n\n
A simple example illustrates this well: recruiting an accountant today without questioning what their role will become in three years constitutes a risk. Not because the role will disappear, but because it will be fundamentally modified. Tomorrow’s accountant will likely need to master anomaly analysis, data control, interpretation of results produced by augmented tools, understanding of automated processes, and the ability to interact with intelligent systems. <\/p>\n\n
The question is therefore no longer only: does this candidate master the accounting skills expected today?
This reflection applies to all Finance roles: management controllers, consolidators, FP&A managers, accountants, credit managers, tax specialists, treasury managers, internal control or audit teams. Not all will be impacted in the same way, but all must be viewed through this new lens. <\/p>\n\n
This is where part of the CFO’s responsibility lies. It is not only about choosing tools or launching experiments. It is about preparing tomorrow’s Finance organization: its roles, its skills, its career trajectories, its hiring needs, and its new balance between human expertise and technological power. <\/p>\n\n
The right question is therefore not only: “Which GEN AI use cases can we test quickly?”<\/p>\n\n
It is also: “What Finance Department do we want to build in 3-5 years, and how are we preparing today the women, men, and skills who will make it work?”<\/p>\n\n
A New Frontier for the Finance Function<\/strong><\/h2>\n\nGEN AI arrives at a particular moment for CFOs. The Finance function is already engaged in numerous transformations: ERP modernization, cloud migration, process automation, data visualization, evolution of management control, strengthening of ESG management, cost pressure, increased expectations from executive management. <\/p>\n\n
GEN AI does not replace these projects. It accelerates them, disrupts them, sometimes reconfigures them. <\/p>\n\n
It requires looking at existing processes differently. Distinguishing what relates to production, analysis, and decision. Questioning the value of roles. Reviewing skill paths. Bringing together Finance, business units, IT, HR, and transformation departments. <\/p>\n\n
It also gives Finance a rare opportunity: to position itself not only as guardian of the numbers, but as architect of a broader economic transformation.<\/p>\n\n
CFOs who take on this topic early will have an advantage. Not because they will have deployed the most visible tool, but because they will have known how to ask the right questions before others: which roles will be transformed? Which skills must we develop? Which risks must we manage? Which gains do we truly want to capture? Which growth opportunities can we make accessible? <\/p>\n\n
GEN AI will not eliminate the CFO’s role. On the contrary, it reinforces its importance. <\/p>\n\n
In an environment where information production becomes faster, more abundant, and more automated, financial discernment becomes even more valuable. Tomorrow’s CFO will not only be the one who guarantees the reliability of the numbers. They will be the one who helps the company transform this new analytical power into sound decisions, sustainable performance, and conquest of new growth areas. <\/p>\n\n
<\/figure>\n\nGEN AI was envisioned as a close assistant: preparing preliminary analyses, flagging inconsistencies, comparing commentary from previous periods, proposing points of attention. Teams remained responsible for validation, but they focused their energy on risk areas rather than comprehensive review of already structured data. <\/p>\n\n
Productivity Is Only Part of the Issue<\/strong><\/h2>\n\nGEN AI is often approached from the perspective of productivity gains. This is legitimate. Finance Departments must produce faster, with greater reliability, in a context of continuous pressure on resources. <\/p>\n\n
But limiting GEN AI to an efficiency topic would be reductive.<\/p>\n\n
One of the major challenges is Finance’s ability to better support growth. In many companies, certain commercial or operational opportunities remain underexploited due to insufficient analytical capacity. The data exists, but it is not always leveraged in time. Scenarios are possible, but too time-consuming to produce. Markets are identified, but business models are not sufficiently simulated. <\/p>\n\n
For a fast-growing B2B services company, the initial topic concerned profitability management by customer. The organization had a rich database: contracts, billing history, time spent, operational incidents, delivery costs, commercial terms. But the analysis remained fragmented, complex, and delayed. <\/p>\n\n<\/figure>\n\nGEN AI made it possible to envision a system helping Finance and sales teams better understand pockets of profitability and underperformance: which customers have degraded profitability? What factors explain it? Which contracts deserve renegotiation? Which segments resemble the most profitable customers? <\/p>\n\n
In this case, GEN AI does not only serve to reduce costs. It opens a growth perspective: better target markets, refine offerings, secure margins, direct sales efforts. <\/p>\n\n
The CFO, Guarantor of Trust<\/strong><\/h2>\n\nThis transformation raises a central question: who guarantees the quality of what is produced?<\/p>\n\n
In a Finance Department, error does not have the same status as in other functions. An imprecise comment, a poorly formulated assumption, or misinterpreted data can lead to a poor decision. GEN AI, because it produces fluent and convincing responses, can create an illusion of reliability. <\/p>\n\n
The CFO’s role therefore becomes critical.<\/p>\n\n
It is not about slowing down usage, but about managing it. Define authorized use cases. Clarify accessible data. Identify necessary validation levels. Implement traceability rules. Train teams to challenge produced responses. Distinguish exploratory uses from binding uses. <\/p>\n\n
For a major telecommunications player, this question of trust was placed at the center of the approach. Finance teams immediately saw the value of GEN AI for accelerating variance analysis, preparing summary notes, or assisting budget production. But the risk was clear: allowing individual uses to multiply without a common framework. <\/p>\n\n
The first step therefore consisted of prioritizing use cases according to their level of criticality. A document summary assistant does not involve the same requirements as a tool contributing to financial forecasting. Assistance with drafting management commentary does not carry the same level of risk as a system used to arbitrate investments. <\/p>\n\n
This approach avoids two pitfalls: uncontrolled enthusiasm and blanket blocking.<\/p>\n\n
Skills Will Evolve Faster Than Organizations<\/strong><\/h2>\n\nThe most underestimated issue is probably that of skills.<\/p>\n\n
GEN AI does not only transform tasks. It modifies the qualities expected of Finance teams. Knowing how to formulate a good question becomes a professional skill. Knowing how to interpret a generated response, detect its limitations, compare it to data, and put it in perspective becomes essential. <\/p>\n\n
In Finance Departments, this evolution will create new gaps. Some employees will adopt these tools very quickly. Others will remain hesitant, not due to lack of talent, but for lack of support, framework, or time to learn. <\/p>\n\n
For a transportation group, the analysis of Finance roles revealed a sensitive point. The most experienced profiles mastered the rules, exceptions, and subtleties of the organization. Younger profiles were often more comfortable with new tools. Value could only emerge from the meeting of the two. <\/p>\n\n
The approach was therefore designed as a transformation of practices: formation of pairs, identification of use cases, comparison of results to business realities, sharing of learnings. Experts provided judgment. More digital profiles tested uses and formulated queries. Together, they distinguished real gains from demonstration effects. <\/p>\n\n
Regaining Control of Time<\/strong><\/h2>\n\nIt would be naive to claim that GEN AI does not raise concerns. Some tasks will diminish. Some positions will evolve. Entire activities will be rethought. <\/p>\n\n
But an exclusively anxiety-driven narrative is often counterproductive. It reduces transformation to a threat, even though many Finance teams today suffer from chronic overload, an inflation of requests, increasing complexity of reporting, and difficulty focusing on substantive analysis. <\/p>\n\n
In several organizations, the most mobilizing message is not: “AI will replace part of the work.” It is rather: “AI can help us regain control of our time.” <\/p>\n\n
Regain control over excessively long closes. Over reports produced under pressure. Over manual adjustments. Over analyses requested too late. Over decisions made with incomplete information. <\/p>\n\n
For a healthcare company, Finance teams faced strong growth in data volume and management requirements. The work conducted identified simple use cases: automatic summary of budget files, preparation of variance analysis notes, assistance with supplier contract review, help formalizing commentary for business units. <\/p>\n\n
Taken separately, these cases might seem modest. Together, they outlined a trajectory: reduce operational pressure to restore analytical capacity, dialogue, and anticipation. <\/p>\n\n
Experiment Quickly, but Project Far<\/strong><\/h2>\n\n<\/figure>\n\nFaced with the scale of the topic, some companies may be tempted to launch a major GEN AI program. This approach can sometimes be justified, but it carries a risk: delaying learning, distancing business units from the topic, and transforming GEN AI into a technology project before making it an operational lever. <\/p>\n\n
The most effective approaches often rely on a very concrete initial axis: a few well-chosen use cases. Simple enough to be tested quickly. Useful enough to produce value. Visible enough to engage teams. <\/p>\n\n
For a Finance Department, experimentation areas are numerous: reporting commentary, variance analysis, budget preparation, close support, document review, internal control, cash forecasting, profitability analysis, assistance producing Executive Committee notes, audit support.<\/p>\n\n
These use cases play an essential role. They make it possible to move beyond general discourse on AI, to concretely demonstrate what the technology can deliver, to reassure teams, but also to reveal the real conditions for success: data quality, expected level of control, evolution of practices, employees’ ability to adopt these new tools. <\/p>\n\n
But this first axis is not enough.<\/p>\n\n
For a CFO, the challenge is also to project 3-5 years ahead. Because GEN AI will not only transform a few isolated tasks. It will gradually modify the very structure of jobs, expected skills, career paths, and organizational balance within the Finance function. <\/p>\n\n
It therefore becomes essential to assess, for each job and each major skill family, the level of GEN AI impact: what will be automated, what will be augmented, what will remain highly human, what will require new skills, and what will give rise to new roles.<\/p>\n\n
This projection is critical. It makes it possible to anticipate the evolution of job descriptions, identify critical skills to develop, identify employees capable and willing to evolve toward these new roles, but also to clarify the hiring to undertake. <\/p>\n\n
A simple example illustrates this well: recruiting an accountant today without questioning what their role will become in three years constitutes a risk. Not because the role will disappear, but because it will be fundamentally modified. Tomorrow’s accountant will likely need to master anomaly analysis, data control, interpretation of results produced by augmented tools, understanding of automated processes, and the ability to interact with intelligent systems. <\/p>\n\n
The question is therefore no longer only: does this candidate master the accounting skills expected today?
This reflection applies to all Finance roles: management controllers, consolidators, FP&A managers, accountants, credit managers, tax specialists, treasury managers, internal control or audit teams. Not all will be impacted in the same way, but all must be viewed through this new lens. <\/p>\n\n
This is where part of the CFO’s responsibility lies. It is not only about choosing tools or launching experiments. It is about preparing tomorrow’s Finance organization: its roles, its skills, its career trajectories, its hiring needs, and its new balance between human expertise and technological power. <\/p>\n\n
The right question is therefore not only: “Which GEN AI use cases can we test quickly?”<\/p>\n\n
It is also: “What Finance Department do we want to build in 3-5 years, and how are we preparing today the women, men, and skills who will make it work?”<\/p>\n\n
A New Frontier for the Finance Function<\/strong><\/h2>\n\nGEN AI arrives at a particular moment for CFOs. The Finance function is already engaged in numerous transformations: ERP modernization, cloud migration, process automation, data visualization, evolution of management control, strengthening of ESG management, cost pressure, increased expectations from executive management. <\/p>\n\n
GEN AI does not replace these projects. It accelerates them, disrupts them, sometimes reconfigures them. <\/p>\n\n
It requires looking at existing processes differently. Distinguishing what relates to production, analysis, and decision. Questioning the value of roles. Reviewing skill paths. Bringing together Finance, business units, IT, HR, and transformation departments. <\/p>\n\n
It also gives Finance a rare opportunity: to position itself not only as guardian of the numbers, but as architect of a broader economic transformation.<\/p>\n\n
CFOs who take on this topic early will have an advantage. Not because they will have deployed the most visible tool, but because they will have known how to ask the right questions before others: which roles will be transformed? Which skills must we develop? Which risks must we manage? Which gains do we truly want to capture? Which growth opportunities can we make accessible? <\/p>\n\n
GEN AI will not eliminate the CFO’s role. On the contrary, it reinforces its importance. <\/p>\n\n
In an environment where information production becomes faster, more abundant, and more automated, financial discernment becomes even more valuable. Tomorrow’s CFO will not only be the one who guarantees the reliability of the numbers. They will be the one who helps the company transform this new analytical power into sound decisions, sustainable performance, and conquest of new growth areas. <\/p>\n\n
<\/figure>\n\nGEN AI made it possible to envision a system helping Finance and sales teams better understand pockets of profitability and underperformance: which customers have degraded profitability? What factors explain it? Which contracts deserve renegotiation? Which segments resemble the most profitable customers? <\/p>\n\n
In this case, GEN AI does not only serve to reduce costs. It opens a growth perspective: better target markets, refine offerings, secure margins, direct sales efforts. <\/p>\n\n
The CFO, Guarantor of Trust<\/strong><\/h2>\n\nThis transformation raises a central question: who guarantees the quality of what is produced?<\/p>\n\n
In a Finance Department, error does not have the same status as in other functions. An imprecise comment, a poorly formulated assumption, or misinterpreted data can lead to a poor decision. GEN AI, because it produces fluent and convincing responses, can create an illusion of reliability. <\/p>\n\n
The CFO’s role therefore becomes critical.<\/p>\n\n
It is not about slowing down usage, but about managing it. Define authorized use cases. Clarify accessible data. Identify necessary validation levels. Implement traceability rules. Train teams to challenge produced responses. Distinguish exploratory uses from binding uses. <\/p>\n\n
For a major telecommunications player, this question of trust was placed at the center of the approach. Finance teams immediately saw the value of GEN AI for accelerating variance analysis, preparing summary notes, or assisting budget production. But the risk was clear: allowing individual uses to multiply without a common framework. <\/p>\n\n
The first step therefore consisted of prioritizing use cases according to their level of criticality. A document summary assistant does not involve the same requirements as a tool contributing to financial forecasting. Assistance with drafting management commentary does not carry the same level of risk as a system used to arbitrate investments. <\/p>\n\n
This approach avoids two pitfalls: uncontrolled enthusiasm and blanket blocking.<\/p>\n\n
Skills Will Evolve Faster Than Organizations<\/strong><\/h2>\n\nThe most underestimated issue is probably that of skills.<\/p>\n\n
GEN AI does not only transform tasks. It modifies the qualities expected of Finance teams. Knowing how to formulate a good question becomes a professional skill. Knowing how to interpret a generated response, detect its limitations, compare it to data, and put it in perspective becomes essential. <\/p>\n\n
In Finance Departments, this evolution will create new gaps. Some employees will adopt these tools very quickly. Others will remain hesitant, not due to lack of talent, but for lack of support, framework, or time to learn. <\/p>\n\n
For a transportation group, the analysis of Finance roles revealed a sensitive point. The most experienced profiles mastered the rules, exceptions, and subtleties of the organization. Younger profiles were often more comfortable with new tools. Value could only emerge from the meeting of the two. <\/p>\n\n
The approach was therefore designed as a transformation of practices: formation of pairs, identification of use cases, comparison of results to business realities, sharing of learnings. Experts provided judgment. More digital profiles tested uses and formulated queries. Together, they distinguished real gains from demonstration effects. <\/p>\n\n
Regaining Control of Time<\/strong><\/h2>\n\nIt would be naive to claim that GEN AI does not raise concerns. Some tasks will diminish. Some positions will evolve. Entire activities will be rethought. <\/p>\n\n
But an exclusively anxiety-driven narrative is often counterproductive. It reduces transformation to a threat, even though many Finance teams today suffer from chronic overload, an inflation of requests, increasing complexity of reporting, and difficulty focusing on substantive analysis. <\/p>\n\n
In several organizations, the most mobilizing message is not: “AI will replace part of the work.” It is rather: “AI can help us regain control of our time.” <\/p>\n\n
Regain control over excessively long closes. Over reports produced under pressure. Over manual adjustments. Over analyses requested too late. Over decisions made with incomplete information. <\/p>\n\n
For a healthcare company, Finance teams faced strong growth in data volume and management requirements. The work conducted identified simple use cases: automatic summary of budget files, preparation of variance analysis notes, assistance with supplier contract review, help formalizing commentary for business units. <\/p>\n\n
Taken separately, these cases might seem modest. Together, they outlined a trajectory: reduce operational pressure to restore analytical capacity, dialogue, and anticipation. <\/p>\n\n
Experiment Quickly, but Project Far<\/strong><\/h2>\n\n<\/figure>\n\nFaced with the scale of the topic, some companies may be tempted to launch a major GEN AI program. This approach can sometimes be justified, but it carries a risk: delaying learning, distancing business units from the topic, and transforming GEN AI into a technology project before making it an operational lever. <\/p>\n\n
The most effective approaches often rely on a very concrete initial axis: a few well-chosen use cases. Simple enough to be tested quickly. Useful enough to produce value. Visible enough to engage teams. <\/p>\n\n
For a Finance Department, experimentation areas are numerous: reporting commentary, variance analysis, budget preparation, close support, document review, internal control, cash forecasting, profitability analysis, assistance producing Executive Committee notes, audit support.<\/p>\n\n
These use cases play an essential role. They make it possible to move beyond general discourse on AI, to concretely demonstrate what the technology can deliver, to reassure teams, but also to reveal the real conditions for success: data quality, expected level of control, evolution of practices, employees’ ability to adopt these new tools. <\/p>\n\n
But this first axis is not enough.<\/p>\n\n
For a CFO, the challenge is also to project 3-5 years ahead. Because GEN AI will not only transform a few isolated tasks. It will gradually modify the very structure of jobs, expected skills, career paths, and organizational balance within the Finance function. <\/p>\n\n
It therefore becomes essential to assess, for each job and each major skill family, the level of GEN AI impact: what will be automated, what will be augmented, what will remain highly human, what will require new skills, and what will give rise to new roles.<\/p>\n\n
This projection is critical. It makes it possible to anticipate the evolution of job descriptions, identify critical skills to develop, identify employees capable and willing to evolve toward these new roles, but also to clarify the hiring to undertake. <\/p>\n\n
A simple example illustrates this well: recruiting an accountant today without questioning what their role will become in three years constitutes a risk. Not because the role will disappear, but because it will be fundamentally modified. Tomorrow’s accountant will likely need to master anomaly analysis, data control, interpretation of results produced by augmented tools, understanding of automated processes, and the ability to interact with intelligent systems. <\/p>\n\n
The question is therefore no longer only: does this candidate master the accounting skills expected today?
This reflection applies to all Finance roles: management controllers, consolidators, FP&A managers, accountants, credit managers, tax specialists, treasury managers, internal control or audit teams. Not all will be impacted in the same way, but all must be viewed through this new lens. <\/p>\n\n
This is where part of the CFO’s responsibility lies. It is not only about choosing tools or launching experiments. It is about preparing tomorrow’s Finance organization: its roles, its skills, its career trajectories, its hiring needs, and its new balance between human expertise and technological power. <\/p>\n\n
The right question is therefore not only: “Which GEN AI use cases can we test quickly?”<\/p>\n\n
It is also: “What Finance Department do we want to build in 3-5 years, and how are we preparing today the women, men, and skills who will make it work?”<\/p>\n\n
A New Frontier for the Finance Function<\/strong><\/h2>\n\nGEN AI arrives at a particular moment for CFOs. The Finance function is already engaged in numerous transformations: ERP modernization, cloud migration, process automation, data visualization, evolution of management control, strengthening of ESG management, cost pressure, increased expectations from executive management. <\/p>\n\n
GEN AI does not replace these projects. It accelerates them, disrupts them, sometimes reconfigures them. <\/p>\n\n
It requires looking at existing processes differently. Distinguishing what relates to production, analysis, and decision. Questioning the value of roles. Reviewing skill paths. Bringing together Finance, business units, IT, HR, and transformation departments. <\/p>\n\n
It also gives Finance a rare opportunity: to position itself not only as guardian of the numbers, but as architect of a broader economic transformation.<\/p>\n\n
CFOs who take on this topic early will have an advantage. Not because they will have deployed the most visible tool, but because they will have known how to ask the right questions before others: which roles will be transformed? Which skills must we develop? Which risks must we manage? Which gains do we truly want to capture? Which growth opportunities can we make accessible? <\/p>\n\n
GEN AI will not eliminate the CFO’s role. On the contrary, it reinforces its importance. <\/p>\n\n
In an environment where information production becomes faster, more abundant, and more automated, financial discernment becomes even more valuable. Tomorrow’s CFO will not only be the one who guarantees the reliability of the numbers. They will be the one who helps the company transform this new analytical power into sound decisions, sustainable performance, and conquest of new growth areas. <\/p>\n\n
The most underestimated issue is probably that of skills.<\/p>\n\n
GEN AI does not only transform tasks. It modifies the qualities expected of Finance teams. Knowing how to formulate a good question becomes a professional skill. Knowing how to interpret a generated response, detect its limitations, compare it to data, and put it in perspective becomes essential. <\/p>\n\n
In Finance Departments, this evolution will create new gaps. Some employees will adopt these tools very quickly. Others will remain hesitant, not due to lack of talent, but for lack of support, framework, or time to learn. <\/p>\n\n
For a transportation group, the analysis of Finance roles revealed a sensitive point. The most experienced profiles mastered the rules, exceptions, and subtleties of the organization. Younger profiles were often more comfortable with new tools. Value could only emerge from the meeting of the two. <\/p>\n\n
The approach was therefore designed as a transformation of practices: formation of pairs, identification of use cases, comparison of results to business realities, sharing of learnings. Experts provided judgment. More digital profiles tested uses and formulated queries. Together, they distinguished real gains from demonstration effects. <\/p>\n\n
Regaining Control of Time<\/strong><\/h2>\n\nIt would be naive to claim that GEN AI does not raise concerns. Some tasks will diminish. Some positions will evolve. Entire activities will be rethought. <\/p>\n\n
But an exclusively anxiety-driven narrative is often counterproductive. It reduces transformation to a threat, even though many Finance teams today suffer from chronic overload, an inflation of requests, increasing complexity of reporting, and difficulty focusing on substantive analysis. <\/p>\n\n
In several organizations, the most mobilizing message is not: “AI will replace part of the work.” It is rather: “AI can help us regain control of our time.” <\/p>\n\n
Regain control over excessively long closes. Over reports produced under pressure. Over manual adjustments. Over analyses requested too late. Over decisions made with incomplete information. <\/p>\n\n
For a healthcare company, Finance teams faced strong growth in data volume and management requirements. The work conducted identified simple use cases: automatic summary of budget files, preparation of variance analysis notes, assistance with supplier contract review, help formalizing commentary for business units. <\/p>\n\n
Taken separately, these cases might seem modest. Together, they outlined a trajectory: reduce operational pressure to restore analytical capacity, dialogue, and anticipation. <\/p>\n\n
Experiment Quickly, but Project Far<\/strong><\/h2>\n\n<\/figure>\n\nFaced with the scale of the topic, some companies may be tempted to launch a major GEN AI program. This approach can sometimes be justified, but it carries a risk: delaying learning, distancing business units from the topic, and transforming GEN AI into a technology project before making it an operational lever. <\/p>\n\n
The most effective approaches often rely on a very concrete initial axis: a few well-chosen use cases. Simple enough to be tested quickly. Useful enough to produce value. Visible enough to engage teams. <\/p>\n\n
For a Finance Department, experimentation areas are numerous: reporting commentary, variance analysis, budget preparation, close support, document review, internal control, cash forecasting, profitability analysis, assistance producing Executive Committee notes, audit support.<\/p>\n\n
These use cases play an essential role. They make it possible to move beyond general discourse on AI, to concretely demonstrate what the technology can deliver, to reassure teams, but also to reveal the real conditions for success: data quality, expected level of control, evolution of practices, employees’ ability to adopt these new tools. <\/p>\n\n
But this first axis is not enough.<\/p>\n\n
For a CFO, the challenge is also to project 3-5 years ahead. Because GEN AI will not only transform a few isolated tasks. It will gradually modify the very structure of jobs, expected skills, career paths, and organizational balance within the Finance function. <\/p>\n\n
It therefore becomes essential to assess, for each job and each major skill family, the level of GEN AI impact: what will be automated, what will be augmented, what will remain highly human, what will require new skills, and what will give rise to new roles.<\/p>\n\n
This projection is critical. It makes it possible to anticipate the evolution of job descriptions, identify critical skills to develop, identify employees capable and willing to evolve toward these new roles, but also to clarify the hiring to undertake. <\/p>\n\n
A simple example illustrates this well: recruiting an accountant today without questioning what their role will become in three years constitutes a risk. Not because the role will disappear, but because it will be fundamentally modified. Tomorrow’s accountant will likely need to master anomaly analysis, data control, interpretation of results produced by augmented tools, understanding of automated processes, and the ability to interact with intelligent systems. <\/p>\n\n
The question is therefore no longer only: does this candidate master the accounting skills expected today?
This reflection applies to all Finance roles: management controllers, consolidators, FP&A managers, accountants, credit managers, tax specialists, treasury managers, internal control or audit teams. Not all will be impacted in the same way, but all must be viewed through this new lens. <\/p>\n\n
This is where part of the CFO’s responsibility lies. It is not only about choosing tools or launching experiments. It is about preparing tomorrow’s Finance organization: its roles, its skills, its career trajectories, its hiring needs, and its new balance between human expertise and technological power. <\/p>\n\n
The right question is therefore not only: “Which GEN AI use cases can we test quickly?”<\/p>\n\n
It is also: “What Finance Department do we want to build in 3-5 years, and how are we preparing today the women, men, and skills who will make it work?”<\/p>\n\n
A New Frontier for the Finance Function<\/strong><\/h2>\n\nGEN AI arrives at a particular moment for CFOs. The Finance function is already engaged in numerous transformations: ERP modernization, cloud migration, process automation, data visualization, evolution of management control, strengthening of ESG management, cost pressure, increased expectations from executive management. <\/p>\n\n
GEN AI does not replace these projects. It accelerates them, disrupts them, sometimes reconfigures them. <\/p>\n\n
It requires looking at existing processes differently. Distinguishing what relates to production, analysis, and decision. Questioning the value of roles. Reviewing skill paths. Bringing together Finance, business units, IT, HR, and transformation departments. <\/p>\n\n
It also gives Finance a rare opportunity: to position itself not only as guardian of the numbers, but as architect of a broader economic transformation.<\/p>\n\n
CFOs who take on this topic early will have an advantage. Not because they will have deployed the most visible tool, but because they will have known how to ask the right questions before others: which roles will be transformed? Which skills must we develop? Which risks must we manage? Which gains do we truly want to capture? Which growth opportunities can we make accessible? <\/p>\n\n
GEN AI will not eliminate the CFO’s role. On the contrary, it reinforces its importance. <\/p>\n\n
In an environment where information production becomes faster, more abundant, and more automated, financial discernment becomes even more valuable. Tomorrow’s CFO will not only be the one who guarantees the reliability of the numbers. They will be the one who helps the company transform this new analytical power into sound decisions, sustainable performance, and conquest of new growth areas. <\/p>\n\n
<\/figure>\n\nFaced with the scale of the topic, some companies may be tempted to launch a major GEN AI program. This approach can sometimes be justified, but it carries a risk: delaying learning, distancing business units from the topic, and transforming GEN AI into a technology project before making it an operational lever. <\/p>\n\n
The most effective approaches often rely on a very concrete initial axis: a few well-chosen use cases. Simple enough to be tested quickly. Useful enough to produce value. Visible enough to engage teams. <\/p>\n\n
For a Finance Department, experimentation areas are numerous: reporting commentary, variance analysis, budget preparation, close support, document review, internal control, cash forecasting, profitability analysis, assistance producing Executive Committee notes, audit support.<\/p>\n\n
These use cases play an essential role. They make it possible to move beyond general discourse on AI, to concretely demonstrate what the technology can deliver, to reassure teams, but also to reveal the real conditions for success: data quality, expected level of control, evolution of practices, employees’ ability to adopt these new tools. <\/p>\n\n
But this first axis is not enough.<\/p>\n\n
For a CFO, the challenge is also to project 3-5 years ahead. Because GEN AI will not only transform a few isolated tasks. It will gradually modify the very structure of jobs, expected skills, career paths, and organizational balance within the Finance function. <\/p>\n\n
It therefore becomes essential to assess, for each job and each major skill family, the level of GEN AI impact: what will be automated, what will be augmented, what will remain highly human, what will require new skills, and what will give rise to new roles.<\/p>\n\n
This projection is critical. It makes it possible to anticipate the evolution of job descriptions, identify critical skills to develop, identify employees capable and willing to evolve toward these new roles, but also to clarify the hiring to undertake. <\/p>\n\n
A simple example illustrates this well: recruiting an accountant today without questioning what their role will become in three years constitutes a risk. Not because the role will disappear, but because it will be fundamentally modified. Tomorrow’s accountant will likely need to master anomaly analysis, data control, interpretation of results produced by augmented tools, understanding of automated processes, and the ability to interact with intelligent systems. <\/p>\n\n
The question is therefore no longer only: does this candidate master the accounting skills expected today?
This reflection applies to all Finance roles: management controllers, consolidators, FP&A managers, accountants, credit managers, tax specialists, treasury managers, internal control or audit teams. Not all will be impacted in the same way, but all must be viewed through this new lens. <\/p>\n\n
This is where part of the CFO’s responsibility lies. It is not only about choosing tools or launching experiments. It is about preparing tomorrow’s Finance organization: its roles, its skills, its career trajectories, its hiring needs, and its new balance between human expertise and technological power. <\/p>\n\n
The right question is therefore not only: “Which GEN AI use cases can we test quickly?”<\/p>\n\n
It is also: “What Finance Department do we want to build in 3-5 years, and how are we preparing today the women, men, and skills who will make it work?”<\/p>\n\n
![\"\"](\"https:\/\/althea-groupe.com\/wp-content\/uploads\/2026\/05\/M2P8179-1024x1024.jpg\")
<\/figure>\n<\/div><\/div>\n\n\n\n