> ## Content Index > Fetch the complete content index at: https://www.financealliance.io/llms.txt > Use this file to discover other available public pages before exploring further. # Why ‘black box’ AI is failing FP&A and what to use instead - URL: https://www.financealliance.io/why-black-box-ai-is-failing-fp-a-and-what-to-use-instead/ - Published: 2026-07-21T08:00:00.000Z - Updated: 2026-07-28T09:14:13.000Z - Description: If you’ve ever doubted an AI-generated forecast, this article shows you how to finally understand and trust the numbers. - Author: Christian Martinez - Tags: AI & Automation, FP&A, Membership content, Articles Over the past decade, we’ve watched [artificial intelligence](https://www.financealliance.io/ai-in-fp-a/) seep into every corner of our professional lives. But something feels different now. The buzz isn’t just about potential anymore, it’s about impact. When ChatGPT reached 100 million users in just two months, it was a wake-up call that we’re in a new era of [productivity](https://www.financealliance.io/operationalizing-productivity-in-a-1b-construction-vertical/) and innovation. At the intersection of finance and technology, a transformation is underway, and [financial planning and analysis](https://www.financealliance.io/fp-a-salary-and-career-path-guide/) (FP&A) professionals are uniquely positioned to lead it. I want to share what I’ve learned about how we can leverage AI in financial forecasting, so in this article, I’ll cover: - [Understanding AI and it’s role in FP&A](https://www.financealliance.io/p/81a99c50-22f3-4182-b0ec-22582845ba19/?member%5Fstatus=paid#%3Cstrong%3Eunderstanding-the-role-of-ai-in-fp&a%3C/strong%3E) - [How AI is impacting FP&A](https://www.financealliance.io/p/81a99c50-22f3-4182-b0ec-22582845ba19/?member%5Fstatus=paid#%3Cstrong%3Ehow-ai-works-in-fp&a%3C/strong%3E) - [AI algorithms to use](https://www.financealliance.io/p/81a99c50-22f3-4182-b0ec-22582845ba19/?member%5Fstatus=paid#%3Cstrong%3Eai-algorithms-you-can-use%3C/strong%3E) - [Practical AI: From data to forecast](https://www.financealliance.io/p/81a99c50-22f3-4182-b0ec-22582845ba19/?member%5Fstatus=paid#%3Cstrong%3Epractical-ai:-from-data-to-forecast%3C/strong%3E) - [Explainable AI ](https://www.financealliance.io/p/81a99c50-22f3-4182-b0ec-22582845ba19/?member%5Fstatus=paid#%3Cstrong%3Ethe-shift-toward-explainable-ai%3C/strong%3E)(moving away from 'black box' AI) - [AI tools for forecasting](https://www.financealliance.io/p/81a99c50-22f3-4182-b0ec-22582845ba19/?member%5Fstatus=paid#%3Cstrong%3Eai-tools-for-forecasting%3C/strong%3E) - [How to prepare for AI and ML in FP&A ](https://www.financealliance.io/p/81a99c50-22f3-4182-b0ec-22582845ba19/?member%5Fstatus=paid#%3Cstrong%3Ehow-to-prepare-for-ai-and-ml-in-fp&a%3C/strong%3E) ## **Understanding the role of AI in FP&A** ![the age of AI](https://storage.ghost.io/c/a5/73/a5734519-d6ac-4b17-95b8-f7a3866730ab/content/images/2025/05/Screenshot-2025-05-29-at-09.20.21.png) Let’s take a step back. What exactly is [artificial intelligence](https://www.financealliance.io/ai-in-finance-business-strategy/)? At its core, AI is the science of creating machines that can perform tasks that normally require human intelligence. In finance, AI is used in three main ways: - Data-driven decision making - Automation and efficiency - Predictive analytics ![what is AI?](https://storage.ghost.io/c/a5/73/a5734519-d6ac-4b17-95b8-f7a3866730ab/content/images/2025/05/Screenshot-2025-05-29-at-09.20.46.png) In FP&A specifically, AI offers a wide range of [opportunities](https://www.financealliance.io/fp-a-exit-opportunities/). I like to categorize its use into three pillars: - Simplifying complexity - Generating new knowledge - Saving time Let’s unpack these a bit more. ![When to use AI in FP&A](https://storage.ghost.io/c/a5/73/a5734519-d6ac-4b17-95b8-f7a3866730ab/content/images/2025/05/Screenshot-2025-05-29-at-09.21.04.png) ### **1\. Simplifying complexity** AI tools like ChatGPT, [Microsoft Copilot](https://www.financealliance.io/how-to-use-microsoft-365-copilot-in-excel/), and Google Bard/Gemini have made it easier than ever to demystify financial concepts. Imagine asking a chatbot to explain IFRS 10 to your marketing manager, and getting an answer that makes sense. AI-powered reporting is another major benefit. It consolidates your financials and provides real-time [reporting](https://www.financealliance.io/what-is-a-compliance-report-compliance-reporting-finance/) capabilities. You can even build your customized version of GPT, embedded with your company’s internal policies so that it becomes an expert in your context. ### **2\. Generating new knowledge** This is where [financial forecasting](https://www.financealliance.io/how-ai-forecasting-drives-smarter-financial-planning/) comes into play. AI allows us to go beyond basic reporting into predictive and prescriptive analytics. It not only tells us what might happen (predictive) but also what we should do about it (prescriptive). AI can help identify patterns, uncover hidden trends, and generate insights. Beyond forecasting, AI can be used for clustering, predictive [insights](https://www.financealliance.io/transforming-financial-data-into-compelling-stories/), and scenario planning. ### **3\. Saving time** AI can automate tedious tasks like file merging and data consolidation, freeing up FP&A teams to focus on strategic initiatives. I often tell my clients: "Let the bots crunch the numbers, so you can focus on the business." [FP&A: The key to unlocking a company’s financial potentialIn this article, we’ll explore the importance of FP&A and how it can be used to drive business growth and success.![](https://storage.ghost.io/c/a5/73/a5734519-d6ac-4b17-95b8-f7a3866730ab/content/images/icon/android-chrome-192x192-133.png)Finance AllianceAsif Masani![](https://storage.ghost.io/c/a5/73/a5734519-d6ac-4b17-95b8-f7a3866730ab/content/images/thumbnail/Copy-of-FA_Website_Article_Images_Author_Highlight--6--2-1.png)](https://www.financealliance.io/fp-a-the-key-to-unlocking-a-companys-financial-potential/) ## **How AI works in FP&A** To grasp AI's potential in [forecasting](https://www.financealliance.io/10-tips-to-eliminate-forecast-bias/), it's important to understand the four stages of data analytics: ### **Descriptive analytics: What happened?** Descriptive Analytics focuses on summarizing and interpreting historical data. It answers questions about what occurred in the past, such as sales trends customer behavior, patterns, and [financial performance](https://www.financealliance.io/why-cfos-need-to-be-people-managers/) over time. ### **Diagnostic analytics: Why did it happen?** Diagnostic Analytics digs deeper into data to understand the causes behind observed events. It shares insights on the reasons for past performance or trends, such as why sales dropped in a particular quarter or why a marketing campaign was successful. ### **Predictive analytics: What could happen?** Predictive Analytics uses statistical models and forecast techniques to make educated guesses about future events. It reveals what might happen next based on patterns and trends identified in historical data, such as predicting future sales growth or market trends. ### **Prescriptive analytics: How can we make it happen?** Prescriptive Analytics goes beyond predicting future outcomes by suggesting actions and strategies to benefit from these predictions. It answers questions about the best course of action, such as what strategies should be implemented to increase market share or how to optimize resource allocation for maximum efficiency. Forecasting falls into the predictive analytics bucket, but its real power is unlocked when combined with prescriptive insights. [The £7.5 million lesson: What real FP&A influence looks likeHow do you close the FP&A influence gap? You clarify, challenge, and connect so that finance moves from being reactive to being influential.![](https://storage.ghost.io/c/a5/73/a5734519-d6ac-4b17-95b8-f7a3866730ab/content/images/icon/android-chrome-192x192-06abfd2f-57ff-4c40-a743-0cf3d2116d05.png)Finance AllianceAlexander Roche![](https://storage.ghost.io/c/a5/73/a5734519-d6ac-4b17-95b8-f7a3866730ab/content/images/thumbnail/Copy-of-FA_Website_Article_Images_Author_Highlight--19--314a13ba-1653-4a45-85f9-3a9bcbeed237.png)](https://www.financealliance.io/what-real-fp-a-influence-looks-like/) ## **AI algorithms you can use** Various algorithms can support financial [forecasting](https://www.financealliance.io/top-down-vs-bottom-up-forecasting/). Here’s a quick rundown: - Intuitive Forecasting (Human judgment) - Run Rate Analysis (Using historical trends to project future performance) - Linear/Logarithmic Regression (Modeling relationships between variables) - Time Series Models (ARIMA, SARIMA) - Machine Learning Models (Random Forest, Neural Networks) - Prophet (Developed by Meta for seasonal time-series forecasting) I personally find Prophet to be highly effective for datasets with strong seasonality patterns. ## **Practical AI: From data to forecast** There are three main stages in financial forecasting where AI plays a role. The first is [data collection](https://www.financealliance.io/data-cleaning-techniques/) and consolidation. [FP&A teams](https://www.financealliance.io/fp-a-team-structure/) spend a massive chunk of time gathering data from different departments. AI and automation tools can reduce this effort by up to 60%. Imagine pressing a button and having all your data consolidated and cleaned in minutes. With data ready, the next step is building forecasts. Using AI models, you can generate more accurate predictions. Better yet, AI allows you to test different assumptions and scenarios quickly. Finally, forecast optimization is where explainable AI becomes crucial. Rather than being a black box, explainable AI allows users to see why a forecast is what it is. You can understand the key drivers and make informed decisions. ## **The shift toward explainable AI** ![explainable AI](https://storage.ghost.io/c/a5/73/a5734519-d6ac-4b17-95b8-f7a3866730ab/content/images/2025/05/Screenshot-2025-05-29-at-10.30.02.png) One of the biggest barriers to adopting AI in FP&A is trust. Traditional AI models often operate like black boxes: you feed in data and get an answer, but you don't know how the system arrived at that answer. Explainable AI, or "glass box" AI, changes the game. It offers transparency by providing the reasons *behind* its predictions. This makes it easier for FP&A professionals and stakeholders to understand, evaluate, and trust model recommendations. So, what does that look like? ![Improving forecasts with AI](https://storage.ghost.io/c/a5/73/a5734519-d6ac-4b17-95b8-f7a3866730ab/content/images/2025/05/Screenshot-2025-05-29-at-10.30.49.png) Well, with the black box approach, we begin with traditional data and the model will start learning from that training data. The key output will be a decision or recommendation. Now the problem with this is that the only output is that learned function and that decision without actually explaining the drivers or the root causes of that forecast. It's very similar to if you need to create a [sales forecast](https://www.financealliance.io/how-to-improve-sales-forecast-accuracy/). You just say that the number of sales that you are going to sell the next year is going to be one million units and then you stop there. You don't say anything else. With that, the actual humans consuming those models will have a lot of questions. Like, why did AI do that? Why not something else? How should we interpret those results? And even more importantly, how do we trust that model? With the second example, we have a different approach: the Glassbox AI approach. We still have the training data, but then we have this explainable AI and machine learning algorithm. With that, the key output is not just the decision or recommendation, but also an explainable model. So something for the actual human to understand why the sales forecast was one million, why not two million, or why not 500,000. With this approach, the [humans](https://www.financealliance.io/beyond-automation-why-human-skills-are-the-future-of-finance/) in FP&A can actually interpret and improve that forecast. Most importantly, they can trust that forecast. Glass-box AI enables: - Transparency - Auditability - Trust in model outputs ## **AI tools for forecasting** I get asked all the time, "Which [tools](https://www.financealliance.io/15-best-fp-a-tools-and-software/) should I be using?" Here’s a quick guide to some of the most useful ones for financial forecasting: - **Microsoft Copilot Pro:** Integrated with Excel, Power BI, and Fabric. You can generate reports, analyze trends, and ask financial questions using natural language. - **Azure:** Offers robust cloud-based AI capabilities for scalable models. - **Power BI:** Leverage Copilot in Power BI for dynamic, interactive dashboards. - **Excel + Python:** With Python integration in Excel (rolling out now), you can run models like Prophet or ARIMA directly in your spreadsheets. - **Google Colab:** Great for building and running Python-based models in a browser-based environment. I often teach a four-step process for using ChatGPT for forecasting: ![Prompt steps](https://storage.ghost.io/c/a5/73/a5734519-d6ac-4b17-95b8-f7a3866730ab/content/images/2025/05/Screenshot-2025-05-29-at-10.53.29-1.png) ## **How to prepare for AI and ML in FP&A** To stay competitive, finance teams need to prepare. Here are five steps I recommend: ### **1\. Data quality and integration** Data is the lifeblood of AI and ML. Organizations must ensure data quality, consistency, and integration across various sources. This includes cleaning and structuring data to make it machine-readable. ### **2\. Talent acquisition and training** Hiring data scientists, analysts, and AI/ML experts is essential. Training existing FP&A teams in AI and ML techniques can also bridge the skills gap and empower them to leverage these technologies effectively. ### **3\. Selecting the right tools** Choosing the right AI and ML tools and platforms that align with FP&A goals is crucial. Options range from predictive analytics software to AI-driven financial planning platforms. ### **4\. Change management** The integration of AI and ML will bring organizational change. It’s important to communicate the benefits and challenges to all stakeholders and facilitate a smooth transition. ### **5\. Continuous learning** AI and ML are ever-evolving fields. Continuous learning and staying updated on the latest advancements are essential to maximize their potential in FP&A. ## **Attend one of our finance summits to learn more about AI in finance** Join our global summit series in a city near you to discover how to unleash the full strategic potential of your finance function. Get in a room with industry leaders as they share best practices and common challenges to help you build the skills you need to increase accuracy and predictability, integrate AI effectively, and foster a high-performing team. [Find an event near you](https://events.financealliance.io/)