4 ways AI can empower Finance

Unlocking data

AI thrives on quality data. In the past, this has required mapping data into clearly defined data schemas, typically in the cloud. But AI can now do much of that work, creating on-the-fly virtual databases using tools like GenAI, ML, and NLP. For example, GenAI can pull essential data directly from business applications, process both structured data (defined fields) and unstructured data (PDFs, emails, audio, etc.), and then extract and format the data. The result: An instant database that bypasses legacy data bottlenecks.

Streamlining transactions

AI automates many essential—but previously time-consuming—business transactions. Here are some examples of AI modules within the quote to cash function, which use GenAI, NLP, RPA, ML, and PA:

• Contract engine that automatically generates contracts and prioritizes reviews based on deviations from standard terms.
• Quote generator to optimize quotes based on customer profiles, contract terms, and inventory data.
• Order engine to capture orders, track fulfillment, generate invoices, expedite collections, and reduce errors related to inventory or contract discrepancies.

Taming the close

AI-powered modules help finance teams dramatically reduce the time and pain of closing cycles and improve productivity and accuracy. These two examples use GenAI, NLP, RPA, ML, and PA:

• Automated accounting rules engine that compares data to defined rules, automates tasks like journal entries and adjustments, scans for anomalies and errors, and expedites compliance and audit reporting.
• Disclosures narrative creator that automates disclosure drafts and reviews, integrates with real-time data and previous reports to ensure consistency, and feeds into reporting.

Elevating FP&A

AI-enabled financial planning and analysis (FP&A) delivers tangible bottom-line benefits with enhanced insights that enable operational agility and create new value faster. These two examples use PA, ML, NLP, and GenAI.

• Intelligent forecasting solutions that use continuously trained predictive models, advanced scenario analyses, and both internal and external data to improve the speed, accuracy, and value of forecasts.
• Capital expenditure optimizer to better prioritize investments (projects, technology, equipment), enable scenario modeling, optimize allocations, and support real-time monitoring for rapid adjustments.