Generative AI in finance helps detect data inconsistencies, reduce human error, and streamline accounting workflows by analyzing large volumes of structured and unstructured data in real time.
The conversation around generative AI in finance is no longer about what’s coming. It’s about what’s already here. Finance teams are under increasing pressure to close faster, reduce manual workloads, and maintain accuracy across complex operations. Intelligent systems built specifically for this environment are emerging as trusted co-pilots.
They don’t just answer questions. They execute, adapt, and deliver results.
In a recent webinar, Nominal’s CEO and Co-Founder, Guy Leibovitz, and VP of Sales, Lee Greene, demonstrated how these technologies are transforming day-to-day workflows with solutions that are already in use. This post breaks down the key takeaways, real-world examples, and what finance leaders need to know to successfully adopt AI in 2025.
🎬 Click here to watch the full webinar 🎬
The Role of AI in Accounting
One of the most practical evolutions AI has introduced to accounting is the ability to move seamlessly between structured and unstructured data.
Traditionally, accounting systems have relied on structured inputs—fixed fields, predefined formats, and rigid templates. But much of the financial data organizations rely on today comes in unstructured formats: emails, PDFs, scanned contracts, spreadsheets, and even chat messages.
AI closes this gap. By using natural language processing, computer vision, and pattern recognition, AI systems can extract relevant data from unstructured sources and convert it into structured outputs ready for processing, like journal entries, financial reports, or audit logs.
This turns what was once a manual, error-prone process into a scalable and auditable one.
What Is Generative AI in Finance and How Is It Used Today?
In the finance world, this class of AI refers to systems capable of producing new outputs such as narrative insights, journal entries or visual reports, based on patterns learned from large datasets. What makes it especially relevant now is the growing operational pressure on finance teams. Traditional tools often cannot keep up with the speed and complexity of today’s business environment.
Rather than replacing professionals, these systems are designed to extend their capacity, helping teams make faster and more informed decisions. Unlike passive assistants that simply surface data, they interpret it, take action and deliver concrete outcomes, from ready-to-post journal entries to variance analysis and consolidated reporting.
How can generative AI be used in finance?
Generative AI is increasingly being used in finance to streamline day-to-day operations, reduce human error, and deliver real-time insights.
By transforming how data is processed, interpreted, and acted upon, it allows finance professionals to move from manual input to strategic output.
Teams can automate tasks like report generation, journal entries, and document classification, enabling them to shift focus toward review and decision-making.
In the webinar, several practical applications were demonstrated, showing how these capabilities work in action.
Financial analysis through natural language
Users can ask the system questions like “Show me the group P&L for Q4 and compare it to Q3” and receive real-time answers, without needing to touch a spreadsheet or write a single query.
Automated document processing
The AI converts unstructured documents, such as payroll files or contracts, into clean journal entries. This reduces errors and eliminates the time-consuming task of reformatting data from Excel.
Consolidation support across entities
Agents assist with intercompany eliminations by identifying matches across subsidiaries and suggesting appropriate entries. This speeds up consolidation and reduces reconciliation headaches.
Collaborative human-AI workflows
Every action taken by the AI is auditable and routed for human review. Accountants remain in control, validating the work and approving entries, which preserves compliance without slowing down operations.
You might also like: AI Agents in Finance and Accounting: From Manual Tasks to Strategic Insights
How GenAI Reduces Close Time and Increases Accuracy
In finance, generative AI is used to automate journal entries, analyze anomalies, and generate real-time insights, helping teams close faster and reduce spreadsheet dependency. By reducing time spent on manual work, teams can cut close cycles by up to 30% while improving data consistency.
Agents identify errors early, reduce spreadsheet dependence, and unlock time for more valuable analysis and forecasting.
This shift supports a broader transformation: moving finance professionals from “doers” to “reviewers.” Instead of building every report or entry manually, they now oversee and guide AI-driven processes.
Implementing Generative AI in Your Finance Stack
Implementing it in finance starts with identifying the most time-consuming and repetitive processes in your workflow.
Based on the insights shared in the webinar, a natural entry point is document-heavy tasks like payroll processing, lease abstraction, and spreadsheet reconciliation.
These activities are typically high in volume, follow predictable patterns, and benefit significantly from automation.
But successful adoption requires more than just picking the right starting point. Change management is key.
Teams should begin with a controlled rollout—selecting one workflow to automate, closely monitoring results, and training staff to work alongside AI systems. This staged approach helps build confidence and surface any integration challenges early.
Equally important is evaluating the AI tool’s ability to meet enterprise-grade standards. Look for vendors that provide robust audit trails, support human approval checkpoints, and are SOC 1 or SOC 2 certified.
In finance, where compliance and accountability are non-negotiable, these safeguards ensure that automation enhances control rather than introducing new risks.
The Role of the AI Co-Pilot in Modern Finance
“AI should be your co-pilot, not your autopilot. The final decision should still be yours—it’s about enhancing control, not replacing it”
AI should enhance finance teams rather than replace them. In the webinar, Guy shared a powerful analogy: finance doesn’t need a self-driving car, it needs a GPS. These tools should guide, suggest and assist, while professionals remain in control.
As more tasks are automated, the controller role begins to evolve. New skills come into play, such as configuring workflows, interpreting AI feedback and validating outputs. Within the next five to ten years, working effectively with this technology may become as fundamental as knowing Excel.
This co-pilot model helps finance stay accurate, strategic and responsive in a fast-paced business environment. The shift from manual processes to intelligent automation is already underway. As teams adopt these solutions, they gain quicker access to insights, reduce reconciliation errors and eliminate the burden of spreadsheets and document prep.
The result is a more agile and audit-ready finance function, capable of operating at scale.
Ready to bring these capabilities into your own workflows? Book a demo with Nominal and see how generative AI can help your finance team save time, improve accuracy, and focus on what matters most.
