Artificial intelligence (AI) has become a major topic of interest for business leaders, surpassing its initial status as a subject confined to IT.
According to a McKinsey study from August 2023, approximately 25% of senior executives have already integrated generative AI tools into their daily activities. Moreover, 25% of respondents in companies adopting AI report that this technology now features prominently on their board of directors’ agenda. Additionally, 40% of respondents are considering increasing their overall investment in AI, spurred by advancements in generative AI.
These statistics illustrate the growing adoption of generative AI in businesses, suggesting a significant impact on the Finance Department.
The rapid rise of generative AI has the power to transform this Department. It has the potential to alter not only existing processes and systems but also its talent and organization.
AI is no longer a mere possibility; it has become a certain eventuality, raising questions about the ‘when’ and ‘how’ of this transformation.
However, this technology does not come without responsibilities. While generative AI presents major opportunities for improving, for example, operational performance, it also poses ethical and organizational challenges.
Thus, the adoption of generative AI is not limited to a technological issue but also has a human dimension.
The purpose of this article is to provide Finance Department with a compass to understand and navigate this new reality.
PART I – Talents and skills: The new face of Finance department
When we think of the finance function, it is traditionally associated with profiles trained and focused on the proper application of standards. It is also linked to adherence to procedures as well as the reliability of figures and the ability to analyze them.
What impact could generative AI have on the roles within the Finance Department? What new skills might emerge, and how might the profile of the financial professional evolve in this new context ?
Generative AI: Catalyst for transforming the roles in the Finance department
Generative AI will transform the professions within the finance function. It will reduce their transactional tasks in favor of more analytical and strategic missions.
Financial Control & Analysis (FP&A) : More control, less production
Beyond the reliability and uniformity of data, from the production of figures and their analysis, the financial controller’s role could expand towards the test/control of the performance of algorithms. Moreover, it seems likely that their role will eventually be less centered on the production of figures (largely shifted towards AI) and more on their control.
Internal Audit: A new combination of expertise for a new field of investigation
The auditor will no longer just verify compliance with procedures and management practices defined by the company. Now, their role could extend to understanding and validating the algorithms at the heart of the company’s processes. Furthermore, risk assessment might also include analyzing the reliability of AI models and their compliance with current regulations.
Accounting: More analysis and less transactional tasks
Generative AI will not only automate numerous transactional tasks but also offer decision-making assistance. Generative AI will assist accountants in identifying which receivables should be prioritized for collection, help them reconciling intercompany accounts, and bank reconciliation. It will suggest making certain accounting and analytical adjustments. The accountant will gradually move away from his role as an executor to become more of an ‘Accounting Analyst’.
Treasury: Foresight and flexibility, the Treasurer’s new key assets
Generative AI will enable treasurers to be more agile in the face of market fluctuations.
By exploiting AI capabilities, treasurers will now be able to have more intelligent financial simulations.
This technology could, for instance, generate cash flow scenarios based on a rich palette of data, far beyond simple historical data, including hundreds of economic and financial variables.
This would allow for the anticipation of market fluctuations that were generally elusive in analysis.
From a professional standpoint, this paradigm shift will move the treasurer’s role from reactive liquidity management to proactive and insightful management.
In short, they will be able to better anticipate exceptional market situations, relying on a multitude of automatically generated scenarios.
Consolidation and reporting: Strategy and analysis are the new watchwords
Generative AI could revolutionize the way consolidation is carried out. Instead of aggregating and reprocessing financial data from different entities, AI could generate consolidation reports by proposing predictive models. These models could anticipate, for example, the financial impacts of regulatory or tax variations in different countries.
Teams in charge of consolidation will no longer just ‘compile’ and adjust financial data; they will anticipate economic and regulatory fluctuations. The traditional ‘consolidator’ will become a consolidation analyst, responsible for verifying and interpreting the predictions generated by AI.
In terms of reporting, generative AI could create reports tailored to each stakeholder (shareholders, regulators, senior management). This customization will be possible thanks to real-time analysis of each actor’s information needs.
The ‘Financial Controller’, rather than a report producer, will coordinate and validate the results generated by AI for each recipient.
CFO: Evolving from Chief Financial Officer to Chief Future Officer
The role of the finance director has always been to ensure the financial health of the company, provide analyses to guide operational decisions, and ensure regulatory compliance. However, with the rise of AI, especially in its generative form, the finance director’s role is evolving, offering new opportunities.
Opportunities and challenges
In the current period of strong economic and political tensions, companies must be even more agile and proactive. Finance directors can use generative AI to their advantage, particularly to better anticipate market trends, optimize investments, and increase revenues. However, they must also face significant challenges. These include not only effectively integrating this technology but also mastering its technical, regulatory, and ethical aspects.
Role evolution
With generative AI, the finance director becomes the main player in the company’s data management and analysis. He is no longer just the guardian of finances but also of data, driving the organization’s decisions.
The future Finance Director
In this new context, the CFO’s role goes far beyond mere financial management. The CFO of tomorrow will be a strategist, an innovator, a technologist, and a financier. A true visionary of the future and perhaps even the next CEO!
Generative AI: The necessary workforce transformation to stay competitive
The increasing integration of generative AI in financial function processes will also reshape the key skills expected from the workforce of a Finance Department. This transition to a finance function augmented by AI requires an update of both technical skills and ‘soft skills’ for the Finance Department’s staff.
New technical skills for Finance augmented by generative AI
Generative AI will lead to a redesign of skills traditionally associated with the financial function. It will require a broader and more technical range of skills, covering areas as diverse as cybersecurity and AI model management. Only by investing in these new skills will Finance Departments be able to truly leverage the potentials offered by generative AI.
Understanding the basics of AI
All members of the Finance Department, whether they are internal auditors, accountants, or financial controllers, will need to have a basic understanding of the underlying principles of AI. For example, understanding how an AI model is trained and how it operates is essential to assess the reliability of the results it produces.
Proficiency in AI tools
The rise of generative AI will require a part of the workforce to develop advanced skills in new AI tools and their configurations. This expertise will be critical for them to understand and interpret the results generated by AI.
Understanding and managing AI models
Other staff members will need to master the models used by the Finance Department. This involves the ability to understand, test, validate, and adjust models according to needs and results. This skill will enable the identification of when a model is outdated or requires updates, thus ensuring the accuracy and efficiency of AI solutions.
Cybersecurity and compliance
The adoption of cutting-edge technologies, particularly generative AI, underscores the importance of ensuring security and adherence to regulations. This assurance is crucial for Finance Departments due to the sensitivity and value of the information they manage. Some members of the financial function’s staff will need to have expertise covering, among others: data privacy and security, regulatory knowledge, AI model integrity, and data ethics.
Soft Skills,’ the finance professional’s profile for thriving with generative AI
The adoption of generative AI by the Finance Department will not only redefine the landscape of technical skills. It will also establish a new standard for ‘soft skills,’ essential for navigating this new reality. These human skills, often underestimated, are the foundation upon which technical skills can be built.
Adaptability
In a world where technology rapidly evolves, the ability to learn and adapt to new tools and methodologies will be paramount.
Critical Thinking
Even with automated analyses, the skill to question, validate, and interpret results is essential to avoid the potential pitfalls of algorithmic biases.
Collaborative Attitude
Financial staff will have to collaborate even more closely with IT teams, data specialists, and other departments to ensure a smooth integration of AI.
AI Training and career evolution in the Finance department
The integration of generative AI is not just about adding a simple tool to the arsenal of Finance Departments. Rather, it represents a profound transformation in how finance professionals are trained and progress in their careers.
Training will be essential to bridge the skills gap created by the integration of AI in the Finance Department’s activities. Training will be less of a “nice-to-have” and more of a necessity to remain competitive.
In-House training
Companies will need to offer in-house training to master the concepts and specific tools adopted. Human Resources will need to create specific training courses that must be frequently updated.
The training curricula will cover both technical, ethical, and regulatory aspects.
Another option will be to enlist external providers (schools, universities, consulting companies) to offer this service.
Specialized training
Universities, business schools, and other training organizations will launch ‘Executive’ type courses dedicated to AI in finance.
Likewise, it is very likely that organizations will establish AI certifications for finance professionals.
New AI career paths
The adoption of AI within the Finance Department will necessitate the creation of new roles and services within the department. This transformation will offer the opportunity for new career trajectories for finance professionals:
- Hybrid Roles: Positions like “AI Financial Analyst” or “AI Auditor” might emerge, requiring expertise in finance and expertise of AI models.
- AI Centers of Excellence: departments might be created to focus solely on AI-related tasks, such as predictive analysis, management of algorithmic risks, etc.
- AI Management Roles: Specific AI leadership roles could be created, such as ‘Director of AI Operations in Finance’.”
PART II – Reinventing the organization of Finance departments in the era of generative AI
With the growing adoption of generative AI, Finance Departments are set to experience changes that go beyond mere surface-level adjustments. These changes will deeply influence how teams collaborate, make decisions, and generate value.
Impacts on the organization and workforce
Generative AI raises questions about the restructuring of existing services. The increased efficiency of processes through AI could lead to significant repercussions on the internal organization and the size of teams in its various services.
Breaking down silos and fostering integrated processes
The boundaries between different services of the Finance Department could blur. Generative AI could promote closer collaborative work.
Restructuring of services and a merging of roles
- The first consequence could be the creation of hybrid services. For example, mixed teams combining experts in accounting, management control, and AI.
- Positions may become more versatile, requiring expertise in multiple areas. Thus, the role of employees would be redefined.
Redefining reporting lines and decision-making processes
- With With cross-functional teams, reporting lines could become less clear. Who reports to whom? This question will become more complex.
- Decision-making might shift to committees with members from various departments, changing the dynamic of authority and responsibility allocation.
Reduction of hierarchical levels
With the introduction of generative AI into Finance Departments, it’s plausible to envision a reduction in hierarchical levels, transforming pyramid structures into flatter organizations. This would allow the Finance function to be more responsive and capable of rapid adaptation, a vital need in an increasingly volatile economic and commercial environment.
Democratization of data analysis
Generative AI allows for real-time and accurate analysis of accounting, financial, and commercial data. This empowers a broader range of staff with decision-making tools, thereby diminishing the necessity for hierarchical oversight.
Streamlining communication pathways
AI tools provide fast, accurate, and direct access to information, potentially reducing the need for intermediary layers.
Staff downsizing or talent redeployment?
It is difficult to predict at this stage whether generative AI will lead to a net reduction in staff within Finance Departments.
This will depend on several factors (organizational strategy, degree of AI adoption, etc.).
However, what is clear is that roles within these Departments will be transformed. The real question may not be whether AI will reduce the number of positions, but rather how existing talents can be redeployed to create more value in an AI-augmented environment. Finance Departments must carefully plan this transition.
New services and departments dedicated to AI
The adoption of generative AI could lead to the emergence of specialized new services designed to maximize the benefits of this technology.
Predictive analysis service
Generative AI can transform financial and operational data into highly accurate predictions and optimization recommendations.
A new service could be established, dedicated to predictive analysis and optimization, utilizing AI to simulate various financial scenarios. This service would be responsible for providing predictive analyses to other departments, such as Treasury for cash flow management, Credit Management for customer risk analysis, FP&A for optimizing various forecasts, and Internal Audit for compliance checks in certain subsidiaries.
AI Center of Excellence (COE): Pooling the Finance department’s needs
Another development could be the creation of AI COE’s within the Finance Department. These cross-functional teams would bring together experts in finance, data analysis, AI, and ethics.
For instance, instead of having data analysts scattered across various services, these experts could be grouped together. They would work on predictive models for cash management or advanced consolidation simulations, using generative AI.
Data department: Responsible for data quality within the Finance department
Professionals in this department would guarantee the quality of data used in AI models, serving as the intermediary between technology and finance experts. Their role would be to verify that generative models accurately represent the company’s financial reality.
The Chief Data Officer in this department would oversee the comprehensive data strategy and would work in synergy with the CFO.
Department of automation and operational intelligence: Maximizing the benefits of AI
Process automation and decision-making facilitated by AI will be key to operational effectiveness. A new division dedicated to these areas might be established. It would work to identify automation opportunities across services and be responsible for implementing and monitoring automation initiatives.
Algorithm compliance service: Controlling the power of AI
To ensure that AI algorithms comply with regulations and company policies, a dedicated service for algorithm compliance, reporting to the Chief Compliance Officer, will be necessary. This service would work closely with the legal department and Data scientist teams to ensure that algorithms are compliant and ethical. Whether this service falls under the responsibility of the Finance Director or Legal Director will likely depend on the culture of the organization.
Organizational agility
The growing adoption of generative AI is a powerful catalyst for organizational agility in Finance Departments. It’s no longer a question of ‘if’ but ‘how’ to integrate this agility, as generative AI is here to stay.
Transitioning to an agile organizational model: In the context of generative AI
The integration of generative AI into Finance Departments is not just a technological evolution; it is an organizational revolution. The capabilities of generative AI — from prediction to generating complex financial information — call for increased organizational agility.
Real-Time adaptive systems
Generative AI is fundamentally adaptive. It can continually refine its models based on new data, meaning the Finance Department must also be adaptive to fully harness this potential.
Accelerated decision cycle
With AI-assisted decision-making, long decision cycles are outdated. Financial decisions can be made quickly, and this fundamental change requires a more agile Finance Department.
Integrated ecosystems instead of organizational silos
An integrated ecosystem model would be an organizational response to the imperatives of generative AI. In this structure:
- Each unit is autonomous and can operate independently, with its own mini-hierarchy and cross-functional team.
- While autonomous, each unit remains interdependent, sharing information and resources as needed to achieve common goals.
The goal is to create ‘agile teams’ within the Finance Department, which can quickly respond to the information and opportunities generated by AI.
Agile Methodology
The agile methodology is not new in the manufacturing or software development fields, and it could become the standard in the Finance Department as generative AI gains prominence.
Financial Sprints
Long financial processes (Budgeting, investment, annual closing) could be broken down into sprints of a few weeks, with clearly defined goals and measurable deliverables.
Regular Reviews and Iterations
Each sprint would be followed by a review and planning for the next one, allowing for continuous improvement and adaptation.
PART III – Governance and ethics: Challenges and perspectives for the Finance department
Redefining Governance: Implications and Considerations in the Era of Generative AI
The emergence of generative AI marks a decisive step in the transformation of businesses. For Finance departments in particular, this technology offers not just opportunities for optimization: it also raises fundamental questions about governance.
In this context, it is essential to consider the implications and challenges that generative AI might introduce into the structures and decision-making processes of the CFO’s office.
Transparency and explainability of decisions
Due to its complexity, generative AI can make decision-making processes opaque. As financial systems increasingly integrate this technology, understanding how certain decisions are made can be challenging. Ensuring that AI-based processes and decisions are transparent and explainable to all stakeholders will be one of the challenges to overcome.
Example: Consider an algorithm used to determine the risk level of an investment or a customer. How can stakeholders trust this assessment if they do not understand the model which was used to assess the risk?
Responsibilities
With generative AI making decisions or performing tasks, the question of responsibility in the event of errors or problems arises. Clearly defining responsibilities in terms of AI will quickly become a question that the company must address.
Example: If an AI algorithm makes an error in a financial forecast, who is held responsible? The team that designed the algorithm? The Finance department that implemented it?
Data integrity and security
Generative AI requires vast amounts of data to function. The management, storage, and security of this data become crucial. Ensuring that the data used by AI are managed and protected adequately is a major challenge.
Updating and maintaining models
The generative AI models evolve rapidly. Establishing a robust process for updating and maintaining AI models to ensure their continued relevance becomes crucial.
Navigating the grey zone: Ethical questions of generative AI for Finance executives
The adoption of generative AI by companies, and by Finance departments, raises significant concerns regarding labor, data authenticity, and impartiality in decision-making.
This technology brings significant ethical challenges, which we are only beginning to understand, but which Finance executives will need to address quickly.
Work ethics
One of the first challenges concerns the transformation of the finance function’s workforce. Indeed, generative AI has the potential not only to enhance the operational performance of finance professionals but also to automate a number of their tasks.
What should be done with employees whose positions become redundant due to this automation? How can their transition or training in new skills be facilitated?
Authenticity and reliability of data
Generative AI has the potential to create content that appears authentic. In the financial field, this could translate into the creation of fake documents (invoices, bank statements, expense reports), false transactions, or even fake balance sheets.
How can we ensure the authenticity and reliability of data in a world where AI can produce seemingly genuine financial content?
Impartiality of AI-based decisions
AI models, often trained on existing data, can inherit biases if these data are flawed or skewed. In finance, this might result in unfair or discriminatory decisions.
How do we ensure the fairness of AI-driven decisions? How can we prevent AI from reinforcing pre-existing biases?
Example: If an AI is trained on historical data to assess customer solvency and this data is biased (for example, discriminatory against certain groups), the AI could replicate these biases in its decisions.
Confidentiality and data protection
The Finance department handles sensitive data. The use of AI, especially generative AI, to process this data raises concerns about their confidentiality and protection.
How do we safeguard the privacy and security of financial data when using generative AI?
Consider the case of generative AI used to optimize budget forecasts, with access to employee data. In this context, there is a risk of data exposure or misuse of this information.
Finance and generative AI: Towards strategic adoption”
Generative AI is no longer a futuristic concept for financial departments; its potential is now tangible.
The time has passed for organizations to be mere spectators; a proactive approach is required, structured around several axes:
Contextual Analysis
A meticulous evaluation of the needs and opportunities specific to each Finance organization is fundamental. This includes diagnosing current processes in accounting, financial control, treasury, etc.
Skill Development
Investing in training programs focused on Generative AI and data science is crucial to prepare teams for this transformation.
Implementation of Pilot Projects
Launching pilot projects will serve as a testing ground to evaluate the relevance, effectiveness, and integrity of Generative AI solutions.
However, its adoption presents inherent complexities that must be anticipated:
Ethics and Security
Interacting with sensitive data requires a strict governance framework to ensure confidentiality and regulatory compliance.
Resistance to Change
The human challenge remains significant. Change management initiatives are essential to facilitate the acceptance of new technologies.
In conclusion, the choice is no longer between adopting or not adopting Generative AI, but in how to integrate it thoughtfully and effectively.
Organizations that anticipate, plan, and invest wisely in this technology will be the ones positioning themselves as leaders tomorrow. The status quo has become the riskiest choice.
