Key takeaways:
AI has quickly become part of the day-to-day work of finance teams. They’re using it to support financial planning, budgeting, forecasting, analysis, reporting, and other processes that influence business decisions.
And adoption is already widespread. According to Deloitte’s Q2 2026 CFO Signals survey, 93% say their organizations use AI extensively or modestly across multiple key functions and operations. Within finance specifically, 44% use AI for financial planning and budgeting, while 41% use it to analyze financial data.
As AI becomes more embedded in finance, governance becomes essential to maintaining confidence in the data, processes, and decisions it supports. Finance leaders need visibility into how AI is used, clear accountability for its outputs, appropriate controls, and human judgment at critical points.
AI governance is becoming a finance responsibility
AI governance requires collaboration across the organization. IT, security, legal, risk, internal audit, and business functions each bring important expertise, and finance has its own responsibilities to consider.
Finance teams work with sensitive data and processes where accuracy, traceability, and accountability carry significant weight. Chief financial officers (CFOs), chief accounting officers (CAOs), controllers, and FP&A leaders need to understand where AI is being used, how it affects existing workflows and controls, and who remains accountable for the resulting decisions.
In fact, Deloitte’s research shows that finance leaders are already taking a larger role. Nineteen percent of CFOs surveyed say they have the greatest responsibility for AI governance at their company, behind chief information security officers and chief information officers but ahead of CEOs, boards and board audit committees, and chief risk officers.
PwC’s guidance on responsible AI in finance also identifies CFOs, CAOs, and controllers as important participants in AI governance. PwC recommends that finance leaders evaluate AI’s effect on internal control over financial reporting, understand the data supporting AI use cases, establish controls to validate outputs, and regularly assess third-party AI systems used within finance.
These responsibilities will continue to evolve as finance expands AI into more consequential work.
Trust matters as AI moves deeper into finance
The governance requirements for AI become more significant when outputs influence forecasts, budgets, financial analysis, reporting, and strategic recommendations.
Finance teams need clear answers to practical questions. What data supports an output? Which assumptions influence the result? Who can access the underlying information? Where does a person review or approve an action?
These questions become especially important with agentic AI. Role-based agents can monitor information, identify changes, recommend actions, and support workflows across finance. Greater autonomy increases the importance of defining permissions, establishing accountability, and determining where human review is required.
The pressure to move quickly makes those practices even more relevant. Deloitte found that 59% of CFOs identify balancing pressure to deploy AI quickly while managing risks as their biggest challenge in developing an effective enterprise-wide AI governance framework. Another 43% cite insufficient visibility into AI tools or their use as a challenge.
Governance provides finance leaders with a framework for addressing those concerns as adoption grows.
What effective AI governance looks like in finance
Strong governance starts with the data AI relies on. Finance leaders need visibility into data sources, ownership, quality, definitions, and access. Controls should help teams validate the completeness and accuracy of information that feeds AI-supported processes, particularly when those outputs influence financial reporting or material business decisions.
Transparency is equally important. Finance professionals should be able to understand how an output was generated and trace the data, assumptions, calculations, changes, and approvals that contributed to it. Audit trails help establish accountability and give finance teams a record of activity when questions arise.
Access also needs to reflect each user’s responsibilities. Role-based permissions can help ensure people and AI systems interact only with the information and workflows appropriate to their roles. Sensitive financial information should remain protected as AI becomes more deeply embedded in existing processes.
Human oversight completes that framework. Finance teams should define where AI can analyze, recommend, or take predefined actions and where human validation or approval is required. People remain responsible for applying business judgment, evaluating context, and making consequential decisions.
Clear ownership ties these practices together. Organizations should know who owns an AI use case, who evaluates its performance, who reviews exceptions, and who is accountable for its outcomes.
Governance supports greater confidence in expanding AI
Evidence increasingly links strong governance and auditability with better outcomes from AI in finance.
KPMG’s 2026 Global AI in Finance research surveyed 1,013 senior finance leaders across 20 countries and 13 sectors. KPMG found that organizations able to efficiently produce and explain AI audit evidence reported significantly stronger results than organizations without that level of assurance readiness. Thirty-three percent reported significant improvements in error reduction compared with 6% among their peers. Forty-two percent reported greater confidence in scaling AI compared with 14%.
KPMG found that only 42% of organizations are fully assurance-ready for AI-enabled finance processes, showing that many finance functions still have work to do as their use of AI matures.
Governance clearly plays a practical role in AI adoption. When finance leaders can understand how AI reaches an output, verify its underlying information, establish appropriate controls, and retain human accountability, they have a stronger foundation for introducing AI into additional workflows.
What finance leaders should expect from their technology
Governance should be part of the technology foundation supporting AI in finance.
As finance leaders evaluate platforms and applications, they should consider whether the technology can:
- Ground AI in trusted enterprise data and established business context
- Combine probabilistic AI with deterministic calculations where financial precision matters
- Provide transparency into outputs, assumptions, and changes
- Maintain audit trails that support accountability
- Apply role-based permissions and enterprise security controls
- Keep people involved in consequential decisions and approvals
- Support AI within established finance workflows
- Provide governance across multiple finance processes from a common platform
These capabilities give finance teams a more consistent way to manage AI as its role expands across the function.
Build a trusted foundation for AI in finance with Anaplan
The Anaplan platform brings predictive, generative, and agentic AI together with enterprise data, workflows, and a powerful deterministic calculation engine. This combination helps finance teams use probabilistic AI for analysis and reasoning while applying explicit business logic and precise calculations to decision-making. Anaplan delivers all of this with the centralized governance, security, administration, and auditability enterprises require. That extends to how outside systems reach your data: our AI Gateway securely connects external AI assistants and agents to Anaplan through a governed MCP interface, enforcing existing permissions and full auditability on every interaction. This means you can deploy AI-driven solutions and applications with confidence across FP&A, consolidation, and reporting.
Anaplan’s impact across finance and accounting is also reflected in independent analyst recognition.
Anaplan was recognized as a 9x Leader in the 2025 Gartner® Magic Quadrant™ for Financial Planning Software and an Exemplary Leader in the 2025 ISG Buyers Guide™ for Financial Consolidation. Together, these recognitions provide independent perspectives on Anaplan’s position across financial planning and financial consolidation as organizations modernize their finance technology foundation.
The path forward for AI in finance
AI will continue to take on a greater role in how finance teams analyze information, plan, forecast, report, and advise the business. Trusted data, transparent logic, appropriate controls, clear accountability, and human judgment give finance leaders the confidence to put those capabilities to work across increasingly important decisions.
With governance built into the technology foundation, finance can expand its use of AI while maintaining the precision and trust the business expects.
