From Forecast to Action: Building Predictive Power into FP&A

In this Digital FP&A Circle, hosted by FP&A Trends and sponsored by CCH Tagetik, senior finance and planning experts explore how organizations are embedding predictive capabilities into everyday decision-making. The discussion covers predictive planning, AI-powered forecasting, scenario analysis, business drivers, data challenges, and the evolving role of technology in FP&A.

What is predictive planning in FP&A?

Predictive planning uses historical and current data, artificial intelligence, and machine learning to help FP&A teams anticipate future outcomes and improve financial plans. 

Unlike a traditional forecast that primarily describes an expected future outcome, predictive planning can help answer three connected questions: What is likely to happen? What is driving that outcome? And how can the business respond? 

For FP&A, this creates a shift from static or manually intensive forecasting toward more dynamic, data-driven planning. Predictive capabilities can support financial forecasting, scenario planning, performance analysis, and faster responses when business conditions change.

Why does predictive planning matter for FP&A?

Business conditions can change faster than traditional planning cycles. Changes in demand, costs, pricing, supply, or other operational factors can quickly affect financial performance. 

Predictive planning gives FP&A teams a way to incorporate more data into the planning process and identify patterns and underlying trends that may not be immediately visible through conventional analysis. 

The goal is not simply to produce another forecast. It is to provide insights that help finance and business leaders understand performance drivers and take action.

How can FP&A move from forecasting to action?

A more predictive FP&A approach connects forecasting with business drivers, scenarios, and decision-making. 

Instead of stopping at a projected number, finance teams can use predictive insights to explore:

• What factors are driving the forecast? 
• What could change the expected outcome? 
• How would different assumptions affect the plan? 
• Which business drivers require attention? 
• How can finance and operations align around the same information?

This makes forecasting part of a broader decision-making process rather than a standalone finance exercise.

What are the four elements of agile FP&A?

The Digital FP&A Circle discussion highlights four characteristics that support a more agile approach to FP&A: speed, flexibility, proactiveness, and technology enablement.

Speed

Finance teams need to reduce the time spent producing and updating forecasts so they can spend more time interpreting results and supporting strategic decisions. 

Automation and predictive technologies can help accelerate planning activities and reduce time-consuming manual work. 

Flexibility 

Plans need to adapt when assumptions change. Flexible planning allows finance teams to update forecasts and scenarios as new information becomes available rather than waiting for the next planning cycle. 

Proactiveness 

Predictive planning can help FP&A move beyond explaining past performance. By identifying patterns, trends, and drivers, finance teams can anticipate potential outcomes and provide forward-looking guidance to the business.

Technology enablement 

AI, machine learning, connected data, and modern planning technologies can help finance teams process more information and generate insights faster. 

Technology is most valuable when it supports human decision-making rather than simply adding another layer of automation. 

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How does AI-powered forecasting work in FP&A?

AI-powered forecasting uses algorithms and machine learning to identify patterns and relationships in historical and current data and use those insights to generate predictive forecasts.

For FP&A teams, the value goes beyond the forecast itself. AI can help identify what is likely to happen, what is driving the result, and where finance and the business may need to take action. 

CCH Tagetik Predictive Forecasting applies AI and machine learning to financial, operational, and external data. Its predictive models identify patterns and underlying trends, while explainable predictions provide insight into the rationale behind the results. 

This combination of prediction and explanation is important for finance teams that need to understand and communicate the reasoning behind a forecast.

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What are the main challenges of adopting predictive planning?

Data quality

Predictive models depend on the quality and relevance of the data available to them. Finance teams therefore need access to appropriate financial, operational, and external information and confidence in the data used for planning. 

Explainability

Finance professionals need to understand and communicate why a model produces a particular prediction. Explainable AI can provide visibility into the drivers and rationale behind predictive results.

Connected planning

Predictive insights are more valuable when financial and operational information can be considered together. Connecting relevant data and planning processes helps finance and operations work from a more consistent view of performance. 

Change management and skills

Technology does not remove the need for financial expertise. FP&A professionals still need to interpret results, challenge assumptions, communicate insights, and determine what actions the business should take.

What are the main predictive planning use cases in finance?

Revenue and profit forecasting

Predictive models can analyze historical and current information to identify patterns and trends that inform revenue and profitability forecasts. 

Scenario planning

Predictive insights can help finance teams explore how changes in assumptions and business drivers may affect future outcomes. 

Operational planning

Combining financial and operational data can help organizations understand how operational drivers influence financial performance and planning outcomes.

Rolling forecasts

When new information becomes available, predictive planning can help teams update their view of the future and adapt plans more quickly. 

Cross-functional planning 

Using financial, operational, and external data together can give finance and operational teams a more connected view of the factors influencing business performance.

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How can FP&A teams get started with predictive planning?

Organizations can approach predictive planning as an evolution of their existing FP&A processes. 

A practical starting point is to identify a planning or forecasting process where better forward-looking insight could support decision-making. Teams can then determine which financial, operational, and external data is relevant, identify the business drivers that matter most, and establish how predictive insights will be used alongside human judgment. 

The objective is not to replace the FP&A process with AI. It is to augment financial expertise with predictive intelligence and actionable insights.

What is the future of predictive FP&A?

The evolution of FP&A is increasingly about connecting technology with human expertise. 

AI and machine learning can process data, identify patterns, generate predictions, and highlight business drivers. Finance professionals bring business context, judgment, and the ability to translate insights into decisions. 

This creates a model in which technology supports finance teams in moving faster from data to insight, from insight to action, and from forecast to decision.

Who are the FP&A experts featured in the Digital FP&A Circle?

The panel brings together finance, strategy, FP&A technology, and planning expertise: 

• Yohei Kawazoe — Head of Corporate Strategy, Sony Europe
• Oscar Lanner — Senior Director, Corporate FP&A, Mars; Chicago FP&A Board member 
• Tammy Nisi — Director of FP&A Retail & Food Service North America, High Liner Foods; Toronto FP&A Board member
• Dominic Nguyen — FP&A Technology Advisor, Wolters Kluwer CCH Tagetik
• Olga Rudakova — FP&A Trends Ambassador and Discussion Facilitator

How does CCH Tagetik Predictive Forecasting support predictive planning?

CCH Tagetik Predictive Forecasting is an AI-based predictive planning solution designed to help finance teams make better decisions faster. Embedded artificial intelligence and machine learning identify patterns and underlying trends across financial, operational, and external data, helping teams understand what may happen and what is driving the outcome. 

The solution provides explainable predictions, helping finance professionals understand the rationale behind AI-generated results. It can also generate predictive plans and incorporate new data into planning, supporting faster adjustments when business conditions change. 

By combining predictive forecasts, explainable insights, and planning capabilities, CCH Tagetik Predictive Forecasting helps FP&A teams turn predictive intelligence into actionable insights and smarter decisions.