Knowledge Base

A growing collection of practitioner insights, organized by topic. Updated weekly.

You can now prototype and iterate financial models faster by using Claude natively in Excel rather than copying formulas back and forth between an AI chat interface and your spreadsheet.

Switch to Power Query for financial consolidation to turn a 3-hour manual monthly task into a 10-second refresh, reducing errors and freeing time for actual analysis.

Claude's ability to trace cell references and generate forecasts with formulas intact can cut your financial modeling time roughly in half, freeing you for higher-level analysis.

You can automate complex data cleaning and transformation tasks in Excel by prompting ChatGPT to generate Power Query M code, eliminating manual transformation work.

Learn how to structure Power Query workflows with a raw data layer and documented M code comments so your budgets-vs-actuals can scale without breaking audit controls.

FP&A teams should track infrastructure cost efficiency improvements like Rubin's 10x inference cost reduction, as lower compute costs directly impact AI project ROI and capital efficiency metrics.

Organizations evaluating AI infrastructure should monitor custom chip offerings from hyperscalers as viable alternatives to traditional GPUs, particularly for inference-heavy workloads where cost optimization matters most.

Before automating anything, map your workflows, kill the garbage work, and build a TRACE-compliant implementation strategy that your CFO can understand in 60 seconds.

Learn to use AI agents like Claude Artifacts to automate financial modeling end-to-end, from research and assumptions to sensitivity analysis and executive dashboards, rather than treating AI as a basic chat tool.

If your team spends significant time on repetitive revenue tasks, outsourced AI agent teams may deliver faster results than building internal AI capabilities, though vendor lock-in and integration complexity should be evaluated.

Structure ChatGPT requests as an FP&A analyst taking on specific jobs (variance analysis, consolidation, narrative), and feed it your actual Excel data to automate repetitive reporting in minutes rather than hours.

For teams using AI coding assistants, auto mode offers a way to reduce permission friction without sacrificing security by letting models evaluate whether actions are genuinely user-intended.

Structure board forecast presentations around a clear decision or outcome rather than information dump, and pre-wire key stakeholders to surface objections before the meeting.

Start your board forecast presentation with the financial implications and ask, then support with assumptions, scenarios, and risks—respecting the board's time and preference for impact-driven storytelling.

Structure board forecasts around forward-looking KPIs, variance drivers, and specific decisions needed—not raw statements—to keep executive attention on what matters.

Before a tough conversation with a business partner about budget or spending, identify the root financial problem, define what 'win' looks like for both sides, and hold it live to read tone and build collaborative momentum.

Ground your budget pushback in specific data and benchmarks, ask clarifying questions to understand your partner's real objective, and propose a win-win alternative rather than simply rejecting their request.

Start tough budget conversations by establishing shared strategic goals, not numbers—this reduces defensiveness and creates a foundation for prioritization.

Understanding your users' real, ground-level concerns about AI tools—not just abstract risks—is essential for product development and stakeholder communication.

Structure your headcount planning in four steps: assess current state, forecast demand by business goal, analyze gaps, and build scenarios—allowing you to align staffing with strategic objectives and present options to leadership.

Frame headcount planning as a productivity lever tied to specific business outcomes, and use scenario-based org charts to give executives visibility into talent gaps and succession risks.

FP&A leaders planning infrastructure strategies should model for sustained capex intensity at hyperscalers and anticipate that debt financing will increasingly bridge the gap between AI investment needs and traditional cash generation—reshaping capital structures across the sector.

FP&A leaders should consider whether their finance teams should shift from building internal AI tools to outsourcing intelligence-heavy work (like close procedures or forecasting) to AI-powered service providers, freeing internal staff for high-judgment activities like strategic planning.