AI is not only changing how professionals perform in finance functions — it is changing how those departments are structured altogether. Markus Hofbauer and Alissa Lugo of Gartner write that compliance concerns will persist if companies don’t update their operating model.
The quickly evolving use of AI in finance functions can feel like a loss of control, especially through compliance. This sense of loss of control is showing up in a few consistent ways. Data flows are becoming invisible, often described as “shadow AI.” Teams are moving faster with AI, but that pace makes it harder to see where sensitive financial data is flowing. AI outputs are probabilistic and model-driven, breaking our expectation that systems give consistent, repeatable and fully traceable results from the same inputs. And finally, decisions are starting to outpace controls because risk identification, recommendations and automation are scaling faster than approvals, auditability and governance.
What is driving the loss of control is not the technology; it’s an operating model gap. AI is already reshaping role composition in finance, shifting how work gets done and where accountability sits. Control, governance and accountability need to be redesigned accordingly.
The new shape of finance: From pyramid to diamond
Traditional finance organizations have long been structured as pyramids, with a broad base of entry-level roles and multiple layers of management. Today, these organizations are evolving into “diamond-shaped” models, reflecting significant changes in how work is structured and delivered.
Entry-level roles are shrinking as automation increasingly handles routine knowledge work. As a result, new hires are expected to bring more specialized skills, and the traditional pathway of learning through repetitive tasks is becoming less common. Mid-level roles are expanding and transforming. Professionals in these positions are now responsible for overseeing both human and machine workflows, ensuring that automated processes run smoothly while applying their expertise to more complex issues. Their role is becoming less about task execution and more about managing exceptions, interpreting data-driven outputs and making judgment calls where human insight is essential.
Leadership responsibilities are also evolving. Senior leaders must now focus on architecting how humans and AI collaborate effectively. This includes setting a clear vision for AI-first operating models, designing the structure for integrated teams and ensuring robust governance practices are in place. Leaders are expected to guide their organizations through this transformation, balancing innovation with the need for accountability and control.
The rise of shared human-AI jobs
By 2030, Gartner expects that 70% of finance roles will be “shared jobs,” where humans and AI collaborate closely. These roles will fall into two categories, depending on how human judgment shows up.
Firstly, managerial shared jobs will involve AI executing repeatable, high-volume tasks, such as reconciliations and transaction processing, while humans supervise outcomes, set parameters and intervene when confidence in AI outputs is low. Secondly, collaborative shared jobs will require humans and AI to work iteratively, especially in areas like financial planning and analysis, where scenario modeling and strategic decision-making demand ongoing human judgment.
An accounting manager, for example, will oversee an AI-driven financial close process. AI acts as the execution engine that will continuously reconcile transactions and detect exceptions, generating audit-ready outputs. The manager’s role will be to configure judgment frameworks, tune model performance and adjudicate low-confidence or outlier scenarios. This shift means compliance controls must be redesigned to ensure the manager’s oversight is documented and auditable, even as the AI automates much of the process.
Another example is a finance business partner: They will collaborate with AI to co-create insights for business decision-making. AI will analyze vast datasets and surface potential risks or opportunities, while the human partner will interpret these signals, apply business context and challenge machine outputs. Here, compliance implications arise from the need to ensure that sensitive financial data used by the AI is protected and that decisions made are traceable and defensible. In both examples, the implications of transparency in terms of how and where AI is being used, also serves as a critical component.
Implications & actions for compliance leaders
As AI becomes embedded in finance, compliance risks evolve. Data flows become less visible, making it harder to ensure compliance with privacy and security regulations. AI-generated results may not be fully traceable, challenging traditional audit and approval processes. AI can automate actions faster than governance frameworks can adapt, increasing the risk of unauthorized or noncompliant activities.
To address these challenges, compliance leaders must collaborate with finance leaders to help rethink their operating models, as they adopt AI. Human judgment becomes a critical control point, with humans treating AI outputs as hypotheses, not answers, to ensure robust decision-making and risk management.
To ensure compliance and maintain control in an AI-driven finance environment, compliance leaders should:
- Work with finance leaders to establish clear decision rights between humans and machines and ensure auditability of AI-driven processes.
- Partner with HR to develop leadership capabilities in systems architecture and AI oversight, institutionalizing new role models.
- Continuously assess the effectiveness of controls and update compliance frameworks as AI capabilities evolve.
- Ensure there is human oversight to review and approve AI-derived statements or information.
The integration of AI into finance is not just a technological shift; it’s an operating model transformation. Compliance risks will persist unless organizations proactively redesign their workforce structures, role definitions and governance frameworks. By embracing shared human-AI jobs, updating talent expectations and strengthening controls, compliance teams can help finance teams remain compliant and competitive in an AI-driven future.
AI is not only changing how professionals perform in finance functions — it is changing how those departments are structured altogether. Markus Hofbauer and Alissa Lugo of Gartner write that compliance concerns will persist if companies don’t update their operating model.
The quickly evolving use of AI in finance functions can feel like a loss of control, especially through compliance. This sense of loss of control is showing up in a few consistent ways. Data flows are becoming invisible, often described as “shadow AI.” Teams are moving faster with AI, but that pace makes it harder to see where sensitive financial data is flowing. AI outputs are probabilistic and model-driven, breaking our expectation that systems give consistent, repeatable and fully traceable results from the same inputs. And finally, decisions are starting to outpace controls because risk identification, recommendations and automation are scaling faster than approvals, auditability and governance.
What is driving the loss of control is not the technology; it’s an operating model gap. AI is already reshaping role composition in finance, shifting how work gets done and where accountability sits. Control, governance and accountability need to be redesigned accordingly.
The new shape of finance: From pyramid to diamond
Traditional finance organizations have long been structured as pyramids, with a broad base of entry-level roles and multiple layers of management. Today, these organizations are evolving into “diamond-shaped” models, reflecting significant changes in how work is structured and delivered.
Entry-level roles are shrinking as automation increasingly handles routine knowledge work. As a result, new hires are expected to bring more specialized skills, and the traditional pathway of learning through repetitive tasks is becoming less common. Mid-level roles are expanding and transforming. Professionals in these positions are now responsible for overseeing both human and machine workflows, ensuring that automated processes run smoothly while applying their expertise to more complex issues. Their role is becoming less about task execution and more about managing exceptions, interpreting data-driven outputs and making judgment calls where human insight is essential.
Leadership responsibilities are also evolving. Senior leaders must now focus on architecting how humans and AI collaborate effectively. This includes setting a clear vision for AI-first operating models, designing the structure for integrated teams and ensuring robust governance practices are in place. Leaders are expected to guide their organizations through this transformation, balancing innovation with the need for accountability and control.
The rise of shared human-AI jobs
By 2030, Gartner expects that 70% of finance roles will be “shared jobs,” where humans and AI collaborate closely. These roles will fall into two categories, depending on how human judgment shows up.
Firstly, managerial shared jobs will involve AI executing repeatable, high-volume tasks, such as reconciliations and transaction processing, while humans supervise outcomes, set parameters and intervene when confidence in AI outputs is low. Secondly, collaborative shared jobs will require humans and AI to work iteratively, especially in areas like financial planning and analysis, where scenario modeling and strategic decision-making demand ongoing human judgment.
An accounting manager, for example, will oversee an AI-driven financial close process. AI acts as the execution engine that will continuously reconcile transactions and detect exceptions, generating audit-ready outputs. The manager’s role will be to configure judgment frameworks, tune model performance and adjudicate low-confidence or outlier scenarios. This shift means compliance controls must be redesigned to ensure the manager’s oversight is documented and auditable, even as the AI automates much of the process.
Another example is a finance business partner: They will collaborate with AI to co-create insights for business decision-making. AI will analyze vast datasets and surface potential risks or opportunities, while the human partner will interpret these signals, apply business context and challenge machine outputs. Here, compliance implications arise from the need to ensure that sensitive financial data used by the AI is protected and that decisions made are traceable and defensible. In both examples, the implications of transparency in terms of how and where AI is being used, also serves as a critical component.
Implications & actions for compliance leaders
As AI becomes embedded in finance, compliance risks evolve. Data flows become less visible, making it harder to ensure compliance with privacy and security regulations. AI-generated results may not be fully traceable, challenging traditional audit and approval processes. AI can automate actions faster than governance frameworks can adapt, increasing the risk of unauthorized or noncompliant activities.
To address these challenges, compliance leaders must collaborate with finance leaders to help rethink their operating models, as they adopt AI. Human judgment becomes a critical control point, with humans treating AI outputs as hypotheses, not answers, to ensure robust decision-making and risk management.
To ensure compliance and maintain control in an AI-driven finance environment, compliance leaders should:
- Work with finance leaders to establish clear decision rights between humans and machines and ensure auditability of AI-driven processes.
- Partner with HR to develop leadership capabilities in systems architecture and AI oversight, institutionalizing new role models.
- Continuously assess the effectiveness of controls and update compliance frameworks as AI capabilities evolve.
- Ensure there is human oversight to review and approve AI-derived statements or information.
The integration of AI into finance is not just a technological shift; it’s an operating model transformation. Compliance risks will persist unless organizations proactively redesign their workforce structures, role definitions and governance frameworks. By embracing shared human-AI jobs, updating talent expectations and strengthening controls, compliance teams can help finance teams remain compliant and competitive in an AI-driven future.
