Predictive Budget Modeling

90% of infrastructure projects go over budget by 30% or more. And if you're a CFO in energy, telecom, oil & gas, or construction, you're probably dealing with: - Unrealistic initial budgets - Last-minute vendor costs - Scope creep with no visibility But what if you could prevent that, before the first shovel hits the ground? Here’s what I discovered leading AI implementations for infrastructure clients generating $10M to $100M+ in annual revenue: The #1 system that changed everything? An AI-Driven pre-construction cost predictor. Let’s break it down. What it does: -> Aggregates your historical cost data (past projects, labor rates, fuel spikes, vendor quotes) -> Pulls in public datasets (like regional inflation trends, commodity indexes, subcontractor activity) -> Applies a supervised machine learning model to create a baseline cost structure -> Uses anomaly detection to flag missing scope and underpriced inputs Why this is crucial for YOU as a CFO? - You gain 100% visibility into real projected costs - You can challenge engineering & PMs early, with data to back it - You align leadership around realistic capital outlay The tech stack that works (I tested dozens): - Data warehouse: Google BigQuery or Snowflake - Visualization: Looker Studio + Figma dashboards - ML modeling: AutoML (Google) or AWS SageMaker - LLMs: ChatGPT for translating results into natural language outputs for decision-makers Real Story: A client in the energy space asked for help. Their budget variance between estimate vs. actual was sitting at -38% for 2 consecutive quarters. They were building substations in the Southwest with 15+ vendors. The problem? Their spreadsheets were based on old data, and every bid came back 20% higher than expected. What we did: - Trained a model on 5 years of vendor invoices - Pulled in regional commodity pricing from government databases - Created a real-time dashboard where finance could see cost delta before contracts were awarded The result? Variance dropped from 38% to 13% in 3 months CFO reported a $1.7M cost avoidance across 4 active projects Leadership now uses AI estimates as the single source of truth And most importantly? They trust finance more than ever. Why this works: Because it puts finance in the driver’s seat. No more post-mortem surprises. You become the proactive partner, not the clean-up crew. What you can do TODAY: Export 5 years of project actuals & estimates Normalize the data into a simple table Run an initial clustering analysis using free AutoML tools Ask ChatGPT to identify outliers and suggest missing scope Even that simple workflow could save you hundreds of thousands. Want to go deeper? I've built this system 3x in the past 4 months for companies just like yours. Enjoy this? Repost to your network and follow Lylya Tsai for more no-fluff, ROI-driven AI finance strategies. Want the full blueprint? DM me "PREDICT" and I’ll send it over to you.

Most budget debates sound like this: Let’s put $100K into Channel X because last quarter ROI looked solid. Translation: You’re gambling on a single point estimate. I introduce confidence bands, an idea borrowed from finance, to make marketing spend a calculated risk, not roulette. How it works: 1️⃣ Model Return Distribution: ↳ Take the last 12 months of channel ROI. ↳ Build a simple 80 % confidence interval (CI). ↳ GA4 + BigQuery make this a two‑line SQL script. 2️⃣ Assign Risk Tiers: ↳ Channels with narrow CIs = predictable (low risk). ↳ Wide CIs = volatile (high risk). ↳ Create three tiers: Core. Growth. Experimental. 3️⃣ Allocate by Risk Appetite: ↳ Core gets stable funding. ↳ Growth receives incremental budget as long as ROI stays within band. ↳ Experimental gets capped spend, think venture bets with predefined exit rules. Result: Budgets adjust automatically to performance volatility, not politics. One e‑commerce client reallocated 15 % of ad spend from volatile display ads to a stable influencer program and saw a 26 % lift in blended ROAS, no additional dollars required. Executives love it because it turns marketing magic into disciplined portfolio management. Which risk tier currently eats most of your budget? A) Core (predictable) B) Growth (moderate risk) C) Experimental (high risk)

Do you want to start using Machine Learning and Python for Budgeting? This is what I'd recommend: First, what is Machine Learning? Think of it as a way for computers to learn from data without needing to be told exactly what to do. Instead of following a strict set of rules, the computer looks at lots of information (data), finds patterns, and uses that to make decisions or predictions. As FP&A and #finance professionals, you don’t need to be a data scientist to use its power—you just need the right tips and tools to get started with Python and #AI ! If you are a beginner with Python, start here: https://lnkd.in/eNZqsHvi ✅ Automated Data Processing One key tip for this is to use Python’s pandas library for automating data collection and processing. You can quickly clean, sort, and organize large datasets without worrying about manual errors. This automation saves time, speeds up the budgeting process, and ensures data consistency. You can even ask ChatGPT for sample code on how to automate data imports! ✅ Trend Analysis I recommend using the matplotlib and seaborn libraries to visualize trends and patterns in historical financial data. Just ask ChatGPT for guidance on how to create visuals in Python. ✅Anomaly Detection A great way to detect anomalies in your financial data is by using the scikit-learn library. Start with unsupervised learning algorithms like Isolation Forest or clustering methods (e.g., DBSCAN) to spot unusual patterns or potential errors in your data. These models can help you identify fraud or prevent budgeting errors before they escalate. ✅Predictive Modeling Predictive modeling is easier than you might think. By leveraging machine learning algorithms such as Linear Regression or Decision Trees (available through scikit-learn), you can forecast future financial performance based on historical data. Once set up, these models will improve your budgeting forecasts' accuracy over time. ✅ Dynamic Budgeting Machine learning allows your budgets to be flexible. I recommend using real-time data adjustments with Python, updating your budgets automatically using tools like statsmodels or prophet. Read this to learn more about Prophet: https://lnkd.in/eB8Qm3EY ✴ Remember: Python is beginner-friendly, and many of the libraries I mentioned are easy to learn with some practice. Whenever you’re stuck or need help with code, you can ask ChatGPT for assistance! If you want to leverage GenAI and ChatGPT for Finance, Nicolas Boucher and I are having our 9th cohort of this training: Use this link to get a discount: https://lnkd.in/e4FugWeY

We helped a client recover $3.4M in hidden operational waste. All through predictive Finance Ops. Financial control used to mean “look back.” Now, it’s all about looking forward. We built a Predictive Spend Control Playbook that helps finance teams find waste before it hits the books. Here’s how 👇 1️⃣ Consolidate Financial Data in One CMDB-Linked View Every expense connects to an asset, CI, or service. No more orphaned spend lines. 2️⃣ Layer AI Forecasting Models We fed 12 months of historical spend into AIOps models. The system now predicts which cost centers will exceed budget within 30 days. 3️⃣ Automate Alerts for Overruns Flow Designer triggers alerts when forecasted spend > 80% of budget. It’s financial early-warning, not postmortem reporting. 4️⃣ Build Executive Dashboards Show CFOs potential cost overruns in real time — tied to business services. 5️⃣ Integrate Action Workflows From dashboard to decision: managers can freeze spend or reroute approvals instantly. That’s not finance automation. That’s financial foresight. Follow me on Instagram for more insights, trends & tips → https://lnkd.in/e4ekDV4C #Finance #ServiceNow #AIOps #PredictiveAnalytics #Automation

I’ve worked with some of the world’s largest companies to build annual plans. Tell me if this sounds familiar… It is month 1 or 2 of the new fiscal year – and you are already in trouble. You are in danger of missing the annual plan and aren’t entirely sure why. The next Executive Leadership or Board Meeting is already on your worry list. You’re unsure how to change the dynamic because your perspective on the business is limited. You are focused on the current month and quarter. Your plan has devolved into, “If I can make 1Q, I will figure out the rest later.” Here is a better idea. Invest in predictive models that highlight likely gaps-to-goal 12+ months into the future. Ensure the models span awareness-to-close; qualified-to-close isn’t broad enough. Devote at least 15% of leadership meetings to FUTURE quarters and leverage predictive models to drive the discussion. As you sit in month 1 or 2, use predictive models to evaluate the most likely outcomes in months 5, 6, 7, and beyond. Then, you can effectively evaluate the sales and marketing (S&M) investments that address predicted gaps. Make S&M investment decisions based on predictive models even the FP&A loves. YES, the FP&A team. I am talking about using real math! Let the math tell you where to spend–not the loudest voice in the room. The old way of closing gaps was to ask the CRO to do more. To find more. To sell more with existing resources. “Go get us a $1M deal.” The new way is to be methodical and leverage predictive modeling to drive decision-making. Try it. Looking farther into the future and taking action is way more fun than sticking your head into the ground.

Forecasting/Budgeting for Procurement: When forecasting and budgeting procurement costs - especially direct material costs - several factors need to be taken into account. The starting point is an understanding of the underlying cost structure. The first step is to identify the key cost drivers such as raw materials (commodities) and other blocks such as wage or process costs. The entire procurement portfolio should be segmented into categories based on comparable cost drivers. Only through this structuring is a targeted and reliable budget planning possible. The application of the identified cost drivers then forms the input for the procurement budget. To validate these approaches, it is advisable to analyze historical cost trends. The analytical forecast method is used to estimate the price development of central cost blocks. It starts at the macro level with economic and political framework conditions. These overarching assumptions must be agreed with management, as they serve as the basis for all further derivations. They are then refined in a multi-stage process - starting with global commodity markets and industry-specific developments through to product-specific factors such as specifications, batch sizes and delivery times. This results in a well-founded, comprehensible forecast that provides a solid basis for the procurement budget. Another key aspect is the choice of planning approach: top-down or bottom-up. In the top-down model, management defines financial targets that are cascaded downwards. With the bottom-up approach, planning takes place at operational level, based on specific requirements and detailed knowledge. In practice, a hybrid approach is often recommended in order to combine both strategic control and operational realism in the planning of direct material costs. Finally, basic principles for budget and forecast planning in procurement must be observed. These range from avoiding unrealistic expectations and focusing on relevant cost drivers to clearly assigning process responsibility. It is particularly important to emphasize that budget cuts should never be made purely top-down without defining responsibilities for individual material costs and savings projects. Only methodically sound and organizationally embedded planning can sustainably strengthen the role of procurement and lay the foundation for efficient decisions and strategic development. Dr. Mario Büsch, PURCHNET.de

“Goodbye Project Control. Hello Predictive Cost Assurance.”🚀 💡 “Why did our $5B project still go over budget… despite weekly cost reports?” This was the exact question a senior leader asked me during a review a few years ago. The truth? We weren’t doing cost assurance — we were just doing cost reporting. 📊 Traditional Project Controls were designed to track numbers, not challenge them. But in today’s world of mega CAPEX projects — energy, infrastructure, industrials — that’s no longer enough. Here’s how cost assurance is changing the game: 🔹 Front-End Confidence: Estimates validated at every decision gate with benchmarking & independent reviews. 🔹 Risk-Adjusted Thinking: Contingencies modeled through probabilistic analysis, directly tied to project risks. 🔹 Digital Insights: AI & analytics now flag overruns before they happen. 🔹 Predictive Modeling: Historical project data + machine learning are being used to forecast cost/schedule outcomes with higher accuracy, reducing bias and improving decision-making. 🔹 Shared Accountability: Engineers, procurement, finance, and leadership all contribute to governance — not just project controls. The result? Projects move from “let’s hope we’re on budget” ➝ to “we have confidence in our capital.” For companies like ADNOC, Aramco, Reliance, or global EPC leaders, cost assurance isn’t just about monitoring spend — it’s about turning data into foresight. 🚀 I believe Predictive cost assurance is going to define the next decade of project governance. 👉 What do you think — is Predictive modeling the future backbone of CAPEX project delivery? #AACEI #ProjectControls #CostAssurance #PredictiveAnalytics #CapitalEfficiency #EPC #ProjectManagement #CAPEXProject #DataAnalytics #AI #MagaProject #CostControl #Benchmarking #PMO #ProjectGovernance #UnderBudget #BigEnergyProject #CostReporting

You and your Finance team just completed the Mona Lisa of budgets. Months of work putting together the most collaborative, top-down, bottom-up budget ever created. Fast forward three weeks... Revenue targets? Missed. Your beautiful budget? Destroyed. Team morale? Tanked. In startups and scale-ups, change is inevitable. So why do so many companies insist on sticking to static, year-long budgets? Whenever I see this, I instantly know they're approaching budgeting like it's 2005, not 2025. But they seem to ignore the reality: → Technology made updating forecasts effortless  → Long-term projections are increasingly complex → Live reforecasts deliver more value than outdated targets This is why rolling forecasts are recommended, even for large companies. Instead of a single, static budget, here are the forecast models you'll maintain: 1. A yearly budget: This serves as a reference for external commitments and outlines what long-term success should look like 2. A live reforecast: This reflects your quarterly goals and should be updated each month alongside the executive team. It includes current actuals, pipeline, and priorities, ensuring targets are relevant and actionable. Agile planning is of the essence. A rolling forecast allows you to: → Move quickly: Your targets should move as fast as your tactics. Rolling budgets keep your team agile → Simplify everything: Forget multiple, confusing spreadsheets. One live reforecast streamlines the process → Iterate faster: Frequent updates help you learn, adjust, and reduce volatility → Reflect reality: Actuals, pipeline, and SQLs change monthly. Your targets should too → Spot problems early: Regular updates let you identify and address issues before they snowball  → Better assess opportunity costs: Evaluate new options monthly rather than on a one-off basis to make more informed decisions → Impress investors: Focus on what happened and what you’re doing about it—not why you missed a static target Static models don’t work in fast-moving environments. Rolling forecasts help finance teams stay connected to reality, adapt quickly, and drive better decisions. I've been sharing insights on how top finance teams are building better forecasting processes in our 'FP&A Stories from the Trenches' newsletter (new edition every Sunday). This week we broke down the exact steps to make rolling forecasts work: Blog: https://lnkd.in/deYpF7bp Sign up: https://lnkd.in/dYhxB4Yp