Deliver faster, more agile FP&A plans with AI-powered forecasts

Finance organizations have always been tasked with improving processes year over year. But when an organization relies on outdated tools and processes, improvement stalls. Over the years I have spoken to many organizations that are managing their financial and operational planning processes through spreadsheets, or through hundreds of disconnected and siloed datasets. 

These organizations are rarely able to optimize processes—in fact, they tend to build more and more silos and replicated datasets. They end up producing a finance-generated top-down forecast, often called a “finance forecast.” This forecast is ignored by the rest of the company, who feel it does not reflect reality.

To be successful, finance organizations must evolve their planning process and become integrated. Integration facilitates collaboration with the other departments in the organization (such as sales, marketing, promotions, pricing and operations). And it enables the organization to infuse artificial intelligence into its processes—thus enabling or accelerating the desired year-over-year improvement.

By integrating artificial intelligence (AI), a finance organization can increase efficiency through intelligent automation and improve data quality and forecast accuracy. For example, in the procure-to-pay process, AI models can detect fraudulent invoices. In record-to-report (general accounting), AI-powered workflows can reconcile subledger transactions and perform risk-based reconciliations. And in financial and operational planning and analysis, augmented intelligence can use algorithmic forecasting models to assess correlations, identify anomalies and adjust to fluctuations.

As a finance leader, integrating AI can give your finance team analytical and business performance superpowers. 

Financial planning and analysis (FP&A) teams can use AI algorithms to generate baseline forecasts, applying consistent logic across the forecast process and automating processing in overnight hours—so finance staff can be more productive when they start their day. AI algorithms also help teams move beyond traditional forecasting methods, develop more accurate predictions and respond in real time to market fluctuations. 

These changes fundamentally transform how work gets done. They free finance teams from tedious tasks, make them more productive and allow for more strategic contributions. On average, adopters of augmented intelligence deploy AI across 40% of finance function workflows, enabling teams to focus on higher-value, more strategic activities, such as readjusting planning decisions quickly in real time. 

By infusing AI into finance processes, organizations have reduced close cycle times and uncollectible accounts receivable balances. And by detecting anomalies, refining metrics and reducing forecast errors, they’ve also increased forecast accuracy, ultimately leading to more reliable and actionable business insights.

Collaborative forecasts integrate the deep learning of the other teams in the organization. They provide the ability to respond quickly to changes in market trends because teams are all communicating with each other. 

Once the planning and forecasting processes are collaborative, it is time to infuse AI into the process. Infusing AI has been proven over and over to increase forecast accuracy. By having systems in place to do the heavy lifting in your planning and forecasting processes, you empower your workforce to move from data mining to data analyzing and more value-added activities such as strategic growth initiatives. 

Choose a planning solution that is collaborative, secured and governed, with the flexibility and scalability to adapt forecasting methods to your organization’s changing needs. Best-in-class solutions have embedded AI algorithmic forecasting capability in the analytics engine. This allows your financial and operational forecasts to incorporate AI algorithms natively to generate accurate forecasts. 

When considering solutions, pay attention to scalability. The key to any AI strategy is the data. The more data you can correlate, the better. You need complete and accurate data to minimize drift and hallucination in your results.

A scalable solution can, for example, handle large datasets. An AI-powered forecast should typically draw data from at least twice the number of time periods you are trying to forecast, to account for trends and seasonality. (For example, for a typical 12-month horizon, you want to use at least 24 months of historical data.) A scalable solution can also ignore specific time periods, and it has machine-learning capabilities to adjust outliers. 

AI algorithmic forecasting natively allows your cross-functional team to schedule predictive baseline forecasts, which help streamline your forecast starting point by allowing you to apply standardized key forecast settings, such as version and forecast time horizon. They also enable the application of advanced capabilities such as outlier and anomaly detection.

Embedded AI algorithmic forecasting also provides planners with the flexibility to choose their AI forecasting methodology. Planners can opt for traditional time series forecasting (univariate) methodology, where the algorithm correlates historical trends and seasonality to project future time periods. Alternatively, they can incorporate external drivers that influence their business model but aren't found in existing financial and operational data. 

External drivers include weather, consumer spending indices, demographics, market constraints and more. When your AI algorithmic forecast correlates these drivers against your historical performance, it can generate more accurate forecasts and informed decisions. This capability can also enhance the allocation of resources by providing data-driven insights that align with real-time market trends and internal metrics. 

Whether you are a large multinational corporation or a local retailer, incorporating these recommendations into your planning process will empower your workforce to achieve more with less. Collaborative, AI-infused planning replaces spreadsheet analysis paralysis with data-driven actionable insights. It streamlines your processes and allows decision-makers to respond quickly to fluctuations in market conditions. Best of all, by using AI to handle both structured and unstructured data efficiently, you increase your forecast accuracy.

IBM Planning Analytics is an integrated solution with built-in modeling and real-time AI capabilities that support all your planning, analysis and reporting needs. Built on the powerful TM1 engine, this truly modern solution unifies and streamlines all processes in a secure environment, enabling enterprise-wide collaboration and transparency. 

IBM Planning Analytics enables you to create more reliable AI forecasts with accurate predictions, and deliver faster, more agile plans that pivot in real time to address changing demands. It supports dynamic process automation through AI-powered predictive capabilities to deliver timely budgets, forecasts, plans, reports and insights. This allows your organization to transition from being merely responsive to driving informed decisions with datasets you can trust. 

IBM Planning Analytics offers everything you need without modification, with a focus on ease of use, including flexible modeling, automated workflows, visualization, management, automated alerts and process reporting. Through its powerful engine, it helps automate data-driven decisions with the distribution, collection and aggregation of data from all the right data sources in your enterprise.

Automate and unify integrated business planning for FP&A