AI in financial modeling is changing the pace of finance. Instead of spending weeks gathering data, teams can begin testing forecasts much sooner. By automating data collection, pattern recognition, and scenario building, artificial intelligence gives finance professionals more room to focus on judgment and strategy instead of spreadsheet mechanics. This overview breaks down what AI in financial modeling actually does, where it delivers the most value, and what to weigh befor

The best AI prompts for FP&A help finance teams accelerate reporting, variance analysis, scenario planning, and forecast preparation while keeping financial calculations in governed systems. Rather than replacing FP&A professionals, Generative AI (Gen AI) is most effective for drafting management commentary, organizing financial data, structuring analyses, and generating decision-ready narratives from approved information. When combined with strong data governance, human revi

The state of SaaS in this yearly review shows an industry becoming more efficient while facing slower growth, changing customer behavior, and growing pressure from AI. Recent SaaS benchmarks reveal improvements and declines as AI reshapes how software companies build, sell, and monetize their products. Understanding these SaaS performance metrics helps leaders benchmark their business, adapt their operating model, and make better decisions for sustainable growth in the year a

FP&A software and a finance operating system serve different but complementary roles in modern finance. FP&A software helps finance teams create budgets, forecasts, reports, and scenario plans. In contrast, a finance operating system (Finance OS) provides the governed data foundation that connects, consolidates, and manages financial information across the business. Understanding how these technologies work together helps organizations build an AI-ready finance function with

A finance engineer is an emerging role that combines finance expertise with AI, automation, data engineering, and workflow design to build and optimize the systems that power modern finance operations. Rather than focusing solely on budgeting, forecasting, or reporting, finance engineers create AI-driven workflows, connect financial data across platforms, and automate repetitive processes so finance teams can spend more time on strategic analysis and decision-making. As gener

OLAP vs ROLAP comes down to how financial data is stored, analyzed, and scaled. Online Analytical Processing (OLAP) uses prebuilt multidimensional cubes to deliver fast reporting and analysis, while Relational Online Analytical Processing (ROLAP) works directly with relational databases, offering greater flexibility, scalability, and real-time data access. Today, many organizations are moving toward ROLAP-based architectures because they can handle larger datasets, support AI

FP&A software companies are opening courses to teach AI in finance because finance teams increasingly need practical AI skills, not just AI-powered tools. Platforms like FinanceOS Academy, OneStream Finance AI Academy, and Planful Academy are now offering structured training on AI in Excel, forecasting, reporting, automation, and FP&A workflows to help finance professionals learn how to apply AI in real-world finance environments. Everyone is going all-in on AI right now, and

AI is making auditing cheaper in many areas by automating time-consuming tasks such as transaction testing, data sampling, audit trail documentation, and risk detection. AI-powered audits can reduce manual work, improve audit efficiency, and shorten audit timelines, allowing audit firms to complete engagements faster and more accurately. However, whether those savings translate into lower audit fees depends on how firms price their services and how actively CFOs negotiate for

How to present data persuasively as a CFO is less about showing numbers and more about shaping decisions . Most CFOs already have accurate data, solid models, and detailed analysis. The problem is not the data itself but with how it’s communicated. When presentations focus only on tables and outputs, audiences struggle to understand what matters and what actions to take next. A persuasive finance presentation bridges that gap. It connects numbers to meaning, and meaning to

“FP&A software is dead”, declared Datarails with the launch of FinanceOS , an AI-ready financial operating system built for the AI era. Instead of relying on traditional Financial Planning and Analysis (FP&A) tools, FinanceOS connects real-time, unified financial data directly to AI through a governed execution layer using Model Context Protocol (MCP). This enables finance teams to use platforms such as ChatGPT, Claude, and Microsoft Copilot to build models, automate workflo

Workforce modeling allows finance leaders to forecast hiring needs, estimate compensation costs, and evaluate how staffing decisions impact financial performance. When done well, it connects strategic planning with operational execution to help companies scale responsibly while maintaining financial discipline. As organizations grow, headcount becomes one of the largest and most dynamic drivers of cost. Salaries, benefits, hiring timelines, and productivity assumptions can

Understanding the average profit margin by industry is one of the fastest ways to benchmark your company’s financial health . While headlines often compare businesses broadly, profit margins vary dramatically depending on capital intensity, pricing power, competition, and cost structure. A 10% margin may be exceptional in retail but underwhelming in software. In this guide, we break down what profit margins are, what a good profit margin is, and how average profit margin be