From spend analysis to faster close cycles, AI is helping finance leaders find new efficiencies, reduce risk, and reallocate resources for strategic growth. Learn the major benefits that your business could gain from AI.
In this article we discuss:
Today’s finance teams face pressure to maintain low operating costs while also processing higher transaction volumes and acting with agility. But outdated legacy systems often get in the way, creating bottlenecks that hinder growth.
Teams need a solution that can scale with their operations while staying precise, accurate, and insightful. AI is bridging the gap, offering more sophisticated financial planning and insight while still driving efficiencies across the finance function.
For high-level leaders, a positive outlook on artificial intelligence (AI) is nearly unanimous—98% of CEOs say their business would immediately benefit from AI implementation. Finance in particular is leading the way on use cases, integration, and showing ROI—77% of finance AI pioneers have already embraced it in their day to day operations.
Still, there’s a definite gap between those who see AI’s potential value and those actually integrating it successfully. Knowing the strongest use casesAI in finance to drive cost savings and enhance team performance is key to keeping your business competitive in the future.
By anchoring AI implementation to tangible goals, finance teams can demonstrate ROI and secure support for broader adoption.
AI in finance refers to the use of artificial intelligence and machine learning (ML) to analyze data, automate processes, and generate insights for the finance function. Finance departments handle large amounts of data governed by strict rules—conditions where AI excels. Leveraging ML algorithms and advanced analytics, AI intelligently automates repetitive tasks, surfacing insights hidden in volumes of transactions.
As AI models refine their predictions over time, finance teams gain an adaptive partner that strengthens their compliance, accuracy, and decision speed without supplanting human oversight. For financial services companies, it also provides an important way to provide more personalized customer experiences. Key capabilities of AI in finance include:
Adaptive learning: Models continuously improve predictions by analyzing vast amounts of data and patterns.
Intelligent automation: Routine tasks like data extraction and reconciliation run with minimal oversight.
Predictive insights: Advanced forecasting tools anticipate cash flow needs and risk exposures before they arise.
Real-time monitoring: Continuous anomaly detection flags irregularities instantly, enabling prompt intervention and risk management.
Scalable integration: AI layers integrate with existing ERP and finance systems, preserving core workflows while boosting capability.
Leaders in finance are moving beyond small AI experiments to truly integrate AI across their entire operations. This shift is leading to real results. In fact, the Workday CFO AI Indicator Report found that finance leaders have high confidence in AI’s ability to deliver business benefits to their organizations, with increased revenue/profits and lower costs both in the top five benefits listed.
When implemented wisely, AI in finance turns tricky hidden inefficiencies into measurable savings. This means teams can operate effectively and seamlessly. Below are ten key areas where AI-powered tools provide the strongest utility and free up time to focus on more strategic, value-driving work.
Finance teams often spend hours manually entering invoice data. AI-driven OCR algorithms and natural language processing (NLP) models ingest unstructured documents, extract line-items, and auto-populate financial systems. By eliminating manual input and adapting to new document formats, companies reduce costly error-related rework.
Traditional automation can point out errors, but often misses the big picture. AI applications analyse transaction patterns to understand what’s normal and what’s a red flag, with humans stepping in to review anomalies. This smart approach means less time spent reviewing, faster-problem solving, and a significant cut in overall operational costs.
Accurate forecasting prevents costly overdrafts and idle cash. AI models ingest historical payments, seasonality, and market indicators to predict liquidity needs. By suggesting optimal payment schedules and collection priorities, finance functions lower borrowing costs and improve interest income.
Static rule-based systems can sometimes miss evolving threats. On the other hand, machine learning models continuously learn from transaction data to detect subtle fraud patterns in real time. Early alerts can prevent big financial losses from credit card fraud or money laundering, and save money on investigations by handling them quickly.
Month-end reconciliations can be a drag for financial institutions. AI-powered systems help by comparing ledgers, suggesting journal entries, and learning to spot and fix mistakes over time. This speeds up close cycles, reduces external audit fees, and accelerates stakeholder reporting.
Regulatory requirements change rapidly across different regions. Using NLP, AI-driven compliance tools parse policy updates and monitor transactions for adherence, generating audit-ready reports automatically. Organizations save on legal reviews and mitigate the risk of costly fines.
AI analytics platforms categorize expenditures and highlight maverick buying by learning typical vendor behaviors. Finance leaders gain visibility into overspend and contract deviations, enabling targeted negotiations and cost reductions.
Inventory misalignment ties up capital. Predictive AI models analyze demand trends and supplier performance to recommend reorder points and preferred vendors. This helps reduce carrying costs and emergency replenishment fees.
Process-mining applications help visualize every step, identifying bottlenecks and redundant steps. AI algorithms offer suggestions for automating workflows, making everything faster and cutting down expenses. Increasingly, finance AI agents are even handling workflows autonomously so teams can stay focused on higher-level work.
Automating repetitive tasks by leveraging AI allows finance professionals to redirect more time, energy, and expertise to areas like financial analysis and strategic planning. Redirecting talent toward high-value activities enhances team productivity and maximizes ROI.
Seventy-seven percent of finance AI pioneers have already embraced AI in their day-to-day operations.
Rolling out AI for your finance team demands a targeted and metrics-driven approach. Finance leaders should prioritize high-impact processes, establish clear KPIs around expense reduction, and build a framework that balances quick wins with sustainable scalability.
By anchoring each phase of implementation to tangible goals, teams can demonstrate ROI early and secure executive support for broader adoption.These steps can help you adopt AI into your finance function smoothly.
Start by looking at your finance tasks to find where repetitive work and fixing mistakes eat up costs and time. Talk with AP clerks, accountants, and controllers to understand daily bottlenecks and common exception triggers. Document how long tasks take and how often people have to step in. This will help you select one or two processes, like approving invoices or reconciling accounts, where machine learning algorithms can quickly make an impact.
Work with IT to consolidate AP, AR, general ledger, and other relevant feeds into a centralized data repository. Standardize data formats and implement simple validation checks—such as mandatory fields and range validations—to catch anomalies early. Set up a basic dashboard tracking key metrics (e.g., processing time, exception rates) to ensure data quality remains high and supports reliable AI training and ongoing monitoring.
Choose an AI solution that clearly matches what you want to achieve. This could be AI that helps pull information from documents or generative AI that creates financial reports from that data. Collaborate with your vendor or internal data science team to feed the model historical transaction data to help it learn about specific document styles and rules. Be sure to connect the AI to existing finance systems to ensure it pulls data and sends alerts seamlessly.
Before full rollout, measure baseline metrics: average processing time, volume of exceptions, and manual reconciliation hours. Run the AI solution in parallel with current workflows (known as shadow mode) for a short pilot period. Compare AI-driven results to your baseline to quantify time saved and reduction in manual errors. Use any discrepancies to refine model parameters and exception-handling rules before switching over.
Establish a quarterly review cadence with stakeholders from finance, IT, and operations to assess AI performance and process metrics. Retrain models on fresh data, adjust automation thresholds as transaction patterns evolve, and update validation rules. Apply lessons learned to new finance processes—such as tax compliance or treasury operations—extending cost savings step by step while maintaining governance and transparency.
AI is proving to be a catalyst for real transformation. When AI is built into your core finance processes, it constantly reveals new ways to improve efficiency. Leaders gain precise knowledge to anticipate what’s next, handle risks, and use resources wisely. This in turn empowers teams to go beyond routine tasks and focus instead on strategic analysis that fuels informed decisions.
You don’t have to overhaul every aspect of your financial processes at once. The journey to AI in finance begins with a targeted project where AI's ability to reduce costs can be clearly seen and measured right away. These early successes create confidence and inspire wider use, turning AI from a simple experiment into a vital part of your strategic planning. Over time, as each new area of finance is thoughtfully integrated, AI becomes the foundation for a competitive edge that drives lasting value.
CFOs across industries are facing increased expectations from internal and external stakeholders. Uncover the top five reasons CFOs are moving to Workday in this report.
More Reading
Workday has been recognized as a Customers’ Choice in the 2025 Gartner® Voice of the Customer for Financial Planning Software report. Based entirely on verified end-user feedback, this recognition reflects the strength of our partnerships and our continued focus on delivering modern, intuitive financial planning tools that help organizations thrive.
Excel’s flexibility and powerful calculation engine make it a natural choice for building and refining financial forecasts alongside core FP&A platforms. Its familiar interface, shared workbook capabilities, and built-in audit trails boost usability, drive collaboration, and ensure the team works from a single, reliable source of truth.
AI is profoundly transforming work—influencing careers, talent strategies, and organizational structures. AI leaders Athena Karp and Danielle Li explore how AI is reshaping the workplace, from accelerating learning to fostering human-AI collaboration.
