How to Transform Financial Analysis With AI

There are two ways to solve a problem in Finance and FP&A Analogy: "We’ve always done it this way, let’s just tweak the template." First Principles: "What is undeniably true about this data, and how can we build it from scratch?" Method #1 keeps you stuck in manual reporting loops. Method #2 opens the door to Python, AI Agents, and full automation. Use this guide to start: https://lnkd.in/eB9Rvwbw I’m re-reading Thinking Like a Rocket Scientist by Ozan Varol, and I thought how to apply First Principles Thinking to AI in Finance: Step 1: Identify & Challenge Assumptions We carry invisible baggage that limits us. You need to identify these myths: ❌ "AI is too complex—only data scientists can use it." ❌ "Automation means buying expensive software." ❌ "Python is for coders, not finance people." Step 2: Break Down to First Principles Ask: What is undeniably true here? ✅ Data is Data. Whether it's a P&L or a CSV, it's just structured information. ✅ Python is Logic. It’s not "code"; it’s a language for expressing logic. ✅ LLMs are Engines. They can read your logic and write the code for you. Once you accept these truths, the barrier to entry disappears. Step 3: Rebuild from the Ground Up Here is your new roadmap to build an AI-powered Finance function: 1. Define the Workflow Don't automate "everything." Pick one high-value task: Variance Analysis, Cash Flow Projections, or Management Reporting. 2. Translate to Logic Map it out: Input (Excel) → Process (Calculate Variance) → Output (Email Summary). 3. Use LLMs as your Co-Developer You don't need to know the syntax. Just prompt: "Write a Python script to load this CSV, calculate the variance between Col A and Col B, and summarize the top 3 drivers." Detailed Guide here: https://lnkd.in/eF6ZxY4t 4. Build in Google Colab You don't need to download any software. Use cloud-based notebooks to test your ideas instantly. https://lnkd.in/eVjchqAS 5. Build Analytics Superpowers Move beyond dashboards. Use AI to generate "what-if" scenarios and predictive insights that Excel simply can't handle. A few final tips for the journey: Test small: Automate one report first. Question everything: "Why are we doing this manually?" Scale responsibly: Always validate the output. I think the future of Finance belongs to those who think like Rocket Scientists, not those who just copy last year's template. Which of those "Assumptions" in Step 1 is holding your team back the most? Let me know in the comments. 

I just watched 80 AI agents work simultaneously on a single spreadsheet. Each pulling different data points. Revenue figures from SEC filings.  Credit ratings from Moody's.  Current ratios from balance sheets. All happening in parallel while I grabbed coffee. Normally, this would mean opening endless browser tabs, hunting through investor relations pages, copying numbers into spreadsheets. Instead, I used AI agents to automate this entire research. Then, used Gemini in Sheets to analyze the data. Here's the real insight: Working with spreadsheets is still complete slop. We've had ChatGPT for 3 years, yet most financial analysis still happens the old way.  You ask an AI a question, get a text response, then manually structure it yourself. That doesn't make sense for research like this. Some workflows need spreadsheet agents, not chat interfaces. So, I used this agentic spreadsheet tool, Ottogrid. Here's what I did: Created a table with 10 companies. Added columns for the financial metrics I needed. Instead of researching each cell manually, I selected the entire range and hit "Run cells." Ottogrid turned every empty cell into an AI agent: ↳ Agent 1: Find Apple's FY2024 revenue ↳ Agent 2: Get Apple's credit rating ↳ Agent 3: Calculate Apple's current ratio ↳ Agent 80: Find Intel's total debt All running simultaneously. All finding exactly what I specified. 2 minutes later: Complete financial analysis ready. Then I moved everything to Google Sheets and used Gemini to create Financial Health Scores and identify red flags across all companies. All without writing or even trying to remember a single spreadsheet formula. This isn't for massive datasets.  But if you can automate one routine research task that eats 2-3 hours of your day, the ROI is obvious. The professionals using AI agents for research definitely have an unfair advantage over those still doing everything manually. If you find this useful, Repost 🔁 to share it with your friends. I share practical AI implementations for finance professionals. To get started: 📩 Subscribe to Unwind AI for AI news, tools, and tutorials: https://lnkd.in/dunsQXDS ⭐️ Star the repo for opensource AI finance agents: https://lnkd.in/db2UynaZ ✅ Follow me for more such AI tools, news, workflows, and insights.

Your champions CFO just got AI superpowers. And you’re still showing up with last quarter’s playbook. I just read a post from a CFO explaining how AI is transforming finance. CFOs now have tools detecting anomalies in real-time, pulling cross-system data in seconds, and surfacing risks before they hit the board deck. That ROI calculator you’re perfecting? They already ran those numbers. Here’s what’s costing you deals: I spoke to a rep who lost a $400K deal last quarter. Solid discovery. Clean business case. 18% ROI. Lost to a competitor who opened with: “Your renewal risk is concentrated in 4 accounts representing 35% of ARR, and your current systems can’t surface this until it’s too late to rectify.” Same product. Different conversation. The losing rep sold efficiency. The winning rep sold visibility. The shift: CFOs don’t fund solutions that prove what they already know. They fund solutions that expose what they can’t see. Instead of: “We reduce month-end close by 20%.” Try: “Your EMEA discounting is running 8%, deeper than other regions, and it’s not flagging until the quarter’s over.” Instead of: “We save your analysts 15 hours a week.” Try: “Your team spends 60% of their time on data prep instead of analysis. What strategic work isn’t happening because of that? And how does that impact the business?” What’s working right now: • Lead with the insight gap they can’t see with their current technology • Propose 60-day pilots with 4 clear KPIs (not 18-month deployments) • Frame ROI as capacity creation: “Redeploys 20 hours toward margin analysis” • Show integration maps early - be connective tissue, not another silo Try this in your next finance call: Replace your deck with one question: “When your CEO asks an unexpected question in the board meeting, how long does it take your team to pull that answer? And what stops while they’re hunting for it?” Then listen. Eliran Glazer shared this: “Start small. Build trust. Measure impact. Scale what works.” Show up with the insight they don’t have yet, not validation of what they already know. What’s one thing you’re changing about how you engage finance buyers?

Stop using your FP&A team to "download csv - copy - paste". A $75M-revenue CFO shared: “Every month we burn hours updating the same reports, and have no time left to think strategically.” That’s not a workflow; it's a tax on decision speed. The shift is underway: finance is moving from report production to decision production. What it looks like in practice: - GL line items and pipeline data are automatically pulled from source systems. - AI runs the first pass: flags variances, explains drivers, spots trend breaks, suggests where to dig. - Your team spends its calories on scenarios, actions, and trade-offs. Not on formatting. The payoff: - 70–80% of time goes to forward-looking analysis, not backward-looking compilation. - Faster board and exec cycles with clearer “do this next” recommendations. - A finance function measured by decisions made and dollars avoided/captured; not decks shipped. This isn’t sci-fi. Teams are already doing it with AI-native platforms (yes, including Payflow) that automate the grunt work and surface the “why” behind the numbers. If your smartest people are still stitching CSVs at month-end, you’re leaving strategic alpha on the table. Flip the calendar: let machines build the package and let finance drive the plan. #FPandA #CFO #StrategicFinance #FinanceTransformation #AIinFinance

This week I watched AI do something in 10 minutes that used to take days — and multiple people. I had: • One AI agent running inside an Excel workbook with historical financials • Another AI agent inside a separate workbook with the Quality of Earnings report • A third AI agent running in PowerPoint Here was the workflow: First, I asked the historical financials agent to “talk” to the QofE agent and reconcile the EBITDA adjustments being presented. It pulled the adjustment detail directly from the QofE workbook and created a line-item reconciliation to validate the numbers. Then I asked the Excel agent to build a 3-year EBITDA bridge. Once complete, that agent handed the analysis off to the PowerPoint agent, which per my direction: • Built the slide • Created a lead-in summary • Added callout boxes explaining key variances • Generated a takeaway section at the bottom of the page All in about 10 minutes. No analyst team. No back-and-forth versions. No manually rebuilding charts in PowerPoint. Just raw information → validated analysis → presentation-ready output. This is where AI becomes transformational in private equity. Not because it replaces investors — but because it compresses workflows that historically required multiple people, multiple programs, and multiple days. As we build Palingenix, we’re putting AI into PE in everything we do. #PrivateEquity #AI #DealFlow #DueDiligence #Palingenix #PAiE

A lot of finance teams are waiting for a magic box. A plug-and-play AI solution that solves all their modeling and forecasting challenges... out of the box. But here's the truth: 𝘛𝘩𝘦 𝘳𝘦𝘢𝘭 𝘰𝘱𝘱𝘰𝘳𝘵𝘶𝘯𝘪𝘵𝘺 𝘪𝘴𝘯’𝘵 𝘪𝘯 𝘰𝘶𝘵-𝘰𝘧-𝘵𝘩𝘦-𝘣𝘰𝘹 𝘢𝘯𝘺𝘵𝘩𝘪𝘯𝘨... It’s in evolving your finance processes and team with automation and AI together. Because AI won’t replace your FP&A team. But it can help: • Automate recurring models • Enhance variance and scenario analysis • Assist decision-making with smarter insights • Help the team see beyond their current sight, creating more capable professionals For AI automation to work, companies need to stop thinking like tech consumers... And start thinking like process designers. Here are key things to consider for a successful AI + automation project: ➤ Start with clarity Know which processes are repetitive, time-consuming, and rules-based. Automate them. ➤ Identify the biggest bottlenecks for a successful automation. They might be good use cases for AI ➤ Don’t skip the human layer AI can assist with insight, but you still need finance judgment to interpret and act. ➤ Data quality is everything Bad inputs = bad outputs. Garbage in, garbage out. Clean, consistent, structured data is key. ➤ Integrate, don’t isolate AI should sit within your tools and workflows, not float in a separate app. It should part of an existing process and not a process created apart. ➤ Implement measures to keep data safe. Governance, policies and compliance. Create guardrails in the processes. ➤ Measure impact, not hype Track real ROI: time saved, accuracy improved, insights gained. The future of FP&A isn't a robot doing your budget. It's smarter tools helping humans do finance better.

AI is transforming finance — and CFOs need to be ready. In a recent interview with Adam Zaki of CFO.com, I shared some key insights from my book "AI Mastery for Finance Professionals," and how finance leaders can navigate the rapidly evolving AI landscape. Here are the highlights: 1️⃣ Data Readiness is Critical Generative AI offers incredible potential, but without mature, clean, and well-governed data, it’s not a technology that can be fully leveraged. CFOs must prioritize their data infrastructure first. 2️⃣ Start Small, Think Big Success with AI isn’t about automating everything overnight. Focus on incremental wins—projects that demonstrate impact, gain buy-in, and build momentum for broader adoption. 3️⃣ Understand the Tool, Not Just the Output AI isn’t a magic box. CFOs don’t need to be developers, but understanding how AI works is crucial to asking the right questions and trusting its insights effectively. 4️⃣ Bias Awareness Matters AI models are only as good as the data they’re trained on. Proactively test for fairness and ensure your datasets are free from bias. 5️⃣ CFOs as Strategic Leaders Today’s CFOs are more than financial stewards—they’re strategists and innovators. AI enhances this role, providing tools to forecast, predict, and guide with creativity and precision. 💡 Final Thought: AI adoption isn’t about replacing people — it’s about empowering teams and creating new efficiencies that drive long-term value. The future is here, and it’s time for finance leaders to embrace it. https://lnkd.in/emBQtfHR

I often hear leaders ask where to start with AI. My answer is usually the same: start with one thing where you can remove friction fast. Quick, internal wins show teams what progress looks like and build momentum.   At Campbell Scientific, they chose to start with financial reporting because it was a pain point everyone felt. The process was manual and relied on fragile spreadsheets. Reporting cycles took up to two weeks, and teams were spending time fixing numbers instead of acting on them.   By working with MCA Connect and using Microsoft Fabric, they automated reporting. Hours of manual reconciliation disappeared, freeing the finance team to focus on analysis and supporting the business. Leaders gained faster access to trusted data, allowing them to make smarter decisions sooner.   This is how AI earns credibility. One operational win that removes friction, then momentum builds. If you’re thinking about where to start, or how to scale what’s already working, AI Skills Navigator has free, practical modules that can help. This one on building end‑to‑end analytics with Microsoft Fabric is a good place to begin: https://lnkd.in/dVU_ya9t Here’s more about Campbell Scientifics AI transformation:

Last quarter, I spoke with a finance team buried under spreadsheets, hopping between platforms, and creating reports that felt outdated the moment they were done. Sound familiar? AI isn’t just a buzzword - it’s taking over the repetitive work, letting CFOs focus on strategy, insights, and real business growth. Here are 6 AI tools every CFO should know: → Coefficient AI – Turn Google Sheets into a command center. Connect 70+ systems, automate dashboards, pivots, and reports, and eliminate manual data entry. → Pilot – Full-service AI-powered bookkeeping with human oversight. Perfect for startups and SMBs needing accurate financials and dedicated support. → Bookkeep – E-commerce accounting on autopilot. Daily syncs, automated categorization, and seamless Shopify integration. → Fathom – Privacy-first website analytics. Real-time insights, GDPR-compliant, and no cookies required. → LivePlan – AI-powered business planning. Scenario modeling, guided financial forecasting, and interactive dashboards. → Float – Resource planning meets financial modeling. Visual project timelines, capacity tracking, and scenario modeling. These tools aren’t just time-savers - they help finance teams see the bigger picture, reduce errors, and make smarter, faster decisions. Start with tools that integrate with your current systems, automate repetitive work, and give you clear, actionable reporting. Let technology handle the grunt work so you can focus on the strategy that drives growth. Which AI tool is helping you transform finance in 2025? Share your experience below! 👇 #CFO #FinanceLeadership #GenerativeAI #DigitalTransformation #FinTech #BusinessGrowth #AIinFinance

Private equity (+ credit) is drowning in AI noise. Every PE partner I talk to has the same problem: 100+ cold emails about AI tools, occasional ChatGPT use, but 𝗻𝗼 𝗰𝗹𝗲𝗮𝗿 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 for where AI actually moves the needle for their fund. Our niche is data & AI transformation for PE. I've found it comes down to focusing on 4 critical areas (in no particular order): 𝟭. 𝗙𝘂𝗻𝗱𝗿𝗮𝗶𝘀𝗶𝗻𝗴 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 Stop spraying and praying with LPs. AI can systematically identify and score potential LPs based on investment patterns, portfolio fit, and timing signals. Your team should be spending time on high-conversion conversations, not cold outreach. 𝟮. 𝗢𝗿𝗶𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗮𝘁 𝗦𝗰𝗮𝗹𝗲 The best deals aren't on everyone's radar. AI can surface off-market opportunities by analyzing alternative data sources and pattern-matching against your investment thesis. Screen 10x more deals in the same time, but only dig deep on the ones that fit your platform or add-on strategy. 𝟯. 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗗𝘂𝗲 𝗗𝗶𝗹𝗶𝗴𝗲𝗻𝗰𝗲 Yes, every deal feels unique. But 80% of your diligence process follows repeatable patterns. Map out your "special sauce" diligence framework once, then let AI handle the routine analysis while your team focuses on the truly deal-specific insights. 𝟰. 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 If your monthly reporting cycle is a fire drill of Excel gymnastics and last-minute adjustments, you're leaving value on the table. Clean data pipelines from portCo's → fund → LP reporting should be table stakes. If I were a PE CTO today, I would 𝘀𝘁𝗼𝗽 𝗳𝗼𝗰𝘂𝘀𝗶𝗻𝗴 𝗼𝗻 𝗔𝗜 𝘁𝗼𝗼𝗹 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻. Instead, I would start by picking one of these four areas and 𝗴𝗼𝗶𝗻𝗴 𝗱𝗲𝗲𝗽. The compound effect of getting even one of these right will transform how my fund firm operates.