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Insights for Finance Leaders
Practical guides on using AI for financial analysis, avoiding hallucinations, and making faster decisions. Written by the team building DataWyse.
AI for Excel and Google Sheets: What Works and What Breaks
AI spreadsheet assistants write formulas well and reason about one workbook. Where that ceiling sits, why it arrives faster than expected in finance, and what to use past it.
9 min read →What an "AI CFO" Actually Means for a Mid-Market Team
The phrase is doing a lot of work in vendor marketing. What is genuinely automatable in a CFO's week, what is not, and how to tell the two apart before you buy.
10 min read →Financial Analytics Software: How to Choose Without Buying Twice
Four categories get sold under one label, and buying the wrong one is the most expensive mistake in this market. How to tell which problem you actually have first.
11 min read →How to Automate Financial Reporting Without Breaking Trust in the Numbers
Automating the monthly pack is a solved problem. Automating it in a way finance still trusts is not. The sequence that works, and the two shortcuts that quietly destroy confidence.
11 min read →The Real Cost of Ad-Hoc Financial Analysis
Ad-hoc analysis never appears as a budget line, which is exactly why it costs so much. A full decomposition of what unplanned financial questions actually cost in analyst hours, fully-loaded salary, and delayed decisions.
11 min read →Why ChatGPT Fails at Financial Analysis, and What Actually Works
CFOs have tried ChatGPT for variance analysis, cash forecasts, and board prep. Most gave up within a week. The failure is architectural, not a prompting problem: here is the mechanism, and the fix.
12 min read →Variance Analysis: A Practical Guide for Mid-Market Finance Teams
A step-by-step guide to variance analysis that does not assume a team of analysts. Covers budget versus actual, price-volume-mix decomposition, materiality thresholds, and how to present findings to a board.
13 min read →Cash Flow Forecasting Without a Data Team
You do not need a data warehouse to forecast cash accurately. How to build a rolling 13-week forecast from the systems you already have, what makes it drift, and when automating it starts to pay.
12 min read →Seven Board Questions Your FP&A Tool Cannot Answer
Your FP&A platform produces the report. When the board asks why margin dropped, where the cash went, or what happens if you delay the hire, you are back in Excel. The seven questions that expose the gap.
11 min read →FP&A Tools for Mid-Market Finance Teams: An Honest Comparison
Datarails, Mosaic, Cube, Drivetrain, Fathom, and the spreadsheet you actually use. What each category is genuinely good at, where every one of them stops, and how to tell which gap is yours.
14 min read →Cohort Retention Analysis: How to Build It and What It Reveals
Blended churn averages good cohorts with bad ones and hides the trend that matters. How to build a cohort retention triangle, where the curve flattens, and why revenue cohorts can exceed 100%.
11 min read →Why Month-End Reconciliation Breaks, and How to Make It Stop
Most failed reconciliations are not accounting problems. They are formatting problems, timing problems, and identifier problems, and each has a specific fix that stops it recurring every month.
11 min read →How to Write Finance Prompts That Do Not Hallucinate
Most AI hallucinations in finance come from prompts that leave the model room to guess. The six instructions that close those gaps, why each one matters, and where careful prompting still is not enough.
10 min read →Most Financial Analysis Cannot Be Templated. Here Is What That Costs
Your FP&A tool handles the repeatable reporting. The larger share (board questions, variance deep-dives, scenario models) still lives in Excel. Why that work resists templating, and what it costs to keep doing it by hand.
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