AI and the future of finance

How is AI shaping the future of the finance function?

AI is reinventing finance by directly transforming forecasting, reporting, controls, the finance operating model, talent, and governance. By automating the manual execution layer that consumes most capacity today, intelligent finance automation elevates our perspective of what is possible on the future of finance.

AI is not only optimizing finance processes, but it is fundamentally redefining what finance teams spend their time on. AI is shaping finance by ultimately reducing people’s time spent to perform manual processes.

The core mandate remains the same—close the books, manage risk, allocate capital, and steer decisions—but the execution is elevated. Finance leaders who recognize this shift can seize a generational opportunity to move their teams beyond traditional transaction processing and into high-value, strategic roles shaping the future of the finance function.

According to Deloitte’s Q4 2025 CFO Signals Survey, 87% of CFOs believe AI will be extremely or very important to their department’s operations in 2026, yet only 63% have deployed AI solutions, and many without reaping the full benefits. Effective AI adoption requires vision and a close examination of critical success factors, sound governance and protocols, data, operating models, talent needs, and practical next steps.

Finance’s purpose is to steward enterprise value, steer strategic decisions, and earn the trust of investors and regulators. The finance transformation happens beneath that purpose, in the places finance professionals dread most: the Annual Operating Plan season spreadsheet marathon, the month-end reconciliation grind, the forecast that cannot start until the data pull is done.

Leaders who frame it this way can provide their teams with a clearer mandate and a faster path to value: same mission, elevated work, better tools. 

What are the five key forces redefining the finance function?

Finance leaders are navigating a convergence of forces evolving the expectations for their units. They will be expected to deliver the same or more output while holding head count flat. The forces are not slowing down, and the competitive gap grows rapidly between the organizations transforming their processes in response and those that are not. The five key forces redefining the finance function show:

1. Volatility in the markets is requiring real-time scenario analysis and resiliency.
2. Exponential technology means processes that once required a team of 10 can now run autonomously.
3. Evolving stakeholder expectations require finance to move from periodic reporting to continuous, on-demand insight.
4. Changing industry dynamics demand real-time responses to regulatory and competitive shifts.
5. A shifting workforce means adopting new roles as humans and machines work more closely together.

These forces intensify the shared challenges finance teams face to reinvent, rethink, and rework. They may need to adopt new roles in an AI-partnered future.

What are the emerging roles for an AI-powered finance function?

In the future, finance will evolve by playing three roles to optimize cost, accelerate growth, and create value.

Many of today’s outcomes will remain. Critical functional areas will not be replaced. Instead, human and machine collaboration will rewire them, unlocking new capabilities. Finance elevates from a necessary cost to a value-creating function. 

AI targets execution gaps, not the strategic intent of the finance function. Teams will do more with less, allowing them to focus on fully flexing their strategic capabilities. Finance moves to the driver’s seat, anticipating rather than reacting, embedding controls and risk management into processes, and turning experiments into returns on investment. 

These capabilities, coupled with the broader levers of talent, operating model, and culture, will enable finance to evolve from a cost center to a true value creator.

What are examples of AI use in the future of finance?

AI is reshaping the efficiency, accuracy, and intelligence of core processes, delivering the same outcomes with less friction and greater insight across every domain. Speed will be among the most noticeable and early factors of change. AI automates repetitive tasks, while Generative AI (GenAI) rapidly creates analyses and forecasts. Real-time, predictive insights and continuous reporting will likely lead to faster, more informed decision-making.

Across core finance process areas, AI won’t just optimize them—it will fundamentally redefine what finance teams spend their time on, delivering AI-enabled outcomes that are more efficient and effective. Examples include:

• In procure-to-pay (P2P), AI autonomously captures and validates vendor invoices against POs and GRs and auto-resolves mismatches, evolving P2P into a fast, compliant, and cost-efficient process that strengthens supplier relationships.
• Order-to-cash (O2C) automates cash application through AI-driven payment matching (“fuzzy logic”) and predicts payment behavior to trigger proactive collections, accelerating receivables, driving faster cash flows, reducing billing errors, and elevating the customer experience—the area expected to see the highest magnitude of change.
• Record-to-report (R2R) becomes an autonomous process that automates journal entry preparation with embedded policy checks and detects anomalies before period-end, delivering precise financials and empowering leaders with timely, forward-looking insights.
• Financial Planning & Analysis (FP&A) autonomously generates continuous forecasts and budgets and auto-generates scenario paths tied to live indicators, while Capital Allocation enables AI-assisted prioritization with risk-adjusted scoring, and Pricing implements AI-powered deal scoring and elasticity models to deliver smarter, faster price decisions and improved margins.
• Treasury auto-collects bank balances and activates machine learning (ML) for dynamic cash forecasts as a catalyst for optimized liquidity and real-time cash visibility, Tax continuously monitors new legislation and standardizes AI-driven classification and filing to deliver increased accuracy and streamlined return cycles, and Controls initiates ML to optimize control thresholds and autonomously flag high-risk transactions, driving real-time monitoring and audit-ready dashboards. Throughout, forward-looking insight replaces backward-looking reporting, with humans providing oversight for exceptions and process assurance.

Ultimately, the future of finance looks faster, more accurate, and highly predictive, with AI autonomously generating real-time information. As these core processes become intelligent, the finance function will be accountable for a larger number of outcomes. Finance leaders who anchor their AI investments to this framing will find it easier to build the business case, communicate value, and hold the organization accountable for results.

Finance can establish a new standard for enterprise value creation. For decades, finance has served as the trusted steward. Through AI, finance can capitalize on unlimited opportunities to leverage data for real-time insights, customize investor reports for specific audiences, deliver forward-thinking insights, and quickly detect threats in the market to realize an expanded role as strategic adviser to the enterprise.

Five steps to get your team ready for using AI in finance

The finance function is at a strategic crossroads, requiring leaders to explicitly define their future operating model. Leaders must begin looking to the horizon, charting a new path, and aligning resources and strategies to create a finance organization of tomorrow. This will require setting guide points for harnessing AI and driving true transformation.

1. Set a clear business strategy for using AI in finance
   A holistic finance strategy, inclusive of a vision for AI, sets the stage. It defines the organization’s AI goals, ethical principles, and reasons for adoption. It guides where and how to apply AI in the finance function to achieve results. Key components for success include aligning business goals, collaborating with technology partners, developing an end-to-end AI governance and risk model, and considering all enablers.
   
   
2. Identify use case opportunities for AI in finance
   Organizations might begin with targeted, high-impact AI use cases in high-volume, rule-based processes such as invoice matching, cash application, journal entry preparation, and variance analysis. Grounding use cases in end-to-end processes, rather than focusing on point solutions, can lead to more transformative value. Leaders should pay attention to out-of-the-box vendor solutions but not wait for them. Constant internal experimentation to identify immediate wins could build in-house AI expertise, creating a competitive advantage.
   
   
3. Align on an AI investment strategy for the finance function
   Any approach will have a crawl, walk, run element to it. And as solutions progress, the organizational value of those efforts increases. A stepwise investment framework serves as a critical building block that enables the scaling of higher-value AI solutions. Integrated agentic solutions sit upon a foundation of strategy, data, and technology. Taking time to get these elements right allows finance teams to iterate and grow, as opposed to throwing darts in the dark.
   
   
4. Define finance’s key performance indicators (KPIs) to measure AI value
   Any investment relies on KPIs that connect process improvement to quality outcomes. These might include dimensions like financial value, operational value, strategic value, and employee value.
   
   
5. Align your finance operating model and talent for AI
   To successfully adopt AI, finance organizations must evolve across six fundamental pillars: technology, data, operating model, talent, adoption, and governance. Process changes only create value when the people, structures, and governance surrounding them are ready. Finance leaders must understand their strategy across each pillar, as well as the key elements that will disrupt the way that finance operates.

A deep dive into the six pillars of AI-ready finance

Three types of people who make up the best AI-driven finance teams

So where to begin? Once finance leaders set their AI strategy across technology, data, operating model, talent, adoption, and governance the real question is the first move. There is no single right answer, and that is good news.

The three profiles below come straight from client transformations we have led, and each one took a sharply different route to get there. Few organizations match just one. The best teams borrow from all three.

The Collaborator

Embraces business process outsourcing to drive out inefficiencies and deliver immediate savings by streamlining transactional areas.

• Benefits: Rapid cost savings and freed-up internal capacity. 
• Risks: Loss of internal expertise and reduced control over ongoing process improvement.

The Technologist

Positions finance as a digital-first organization by investing in best-in-class technologies and harmonizing data to maximize efficiency and agility.

• Benefits: A strong, scalable foundation for AI-enabled process improvement and finance automation.
• Risks: Significant up-front investment and long implementation timelines.

The Innovator

Champions a bold, end-to-end transformation that reimagines finance one process at a time, focusing on high-value opportunities to enhance the finance operating model.

• Benefits: Flexible and modular finance transformation.
• Risks: Lack of enterprise-wide consistency and ROI that depend heavily on execution discipline.

Regardless of the approach, the measure of success is the same: Did finance’s outcomes improve? The ultimate accountability is whether finance delivers more reliable financials, more credible forecasts, more timely insights, and more effective risk management—sustained and improved over time.

Conclusion

Finance’s outcomes have not changed. The obligation to manage risk, report accurately, allocate capital wisely, and inform better decisions is as enduring as the finance function itself. 

What AI is changing is the process by which finance meets those obligations. AI offers the potential to reduce manual labor, synthesize and organize data in real time, and create timely, insight-rich reports. AI might empower the next generation of finance organizations to deliver outcomes with greater speed, higher accuracy, and far more capacity for the human judgment that drives strategic value. 

Transformation is not about reinventing finance’s purpose. It is about removing obstacles that have stood between finance and its ability to fully realize its potential as a strategic adviser to the enterprise.