Guide

Forecasting Guide: How AI-Powered Continuous Planning Mitigates Economic Volatility

Business leaders face an ever-evolving set of challenges–from the need to rapidly grow the business, to accurately predicting future demand, to anticipating unforeseen market circumstances like tariff volatility. With increasing volumes of data across the organization, it can be difficult for decision-makers to zero-in on the necessary data and extrapolate the proper insights to move from static planning cycles to continuous planning to make confident decisions. To further exacerbate the problem, many tools leverage primarily high-level historical data, forcing decision makers to re-forecast from scratch as soon as unforeseeable market shifts hit—costing their companies millions in lost opportunities or sunk-cost.

With Board Foresight, teams across finance, supply chain, and human resources can make more confident decisions with accurate forecasting. This solution is part of the Board Enterprise Planning Platform, a leader in the 2024 Gartner® Magic Quadrant™ for Financial Planning Software, giving real-time access to over 5 million global data sets, AI-powered predictive modelling, and expert analysis to generate more accurate forecasts, fast.

Whether you’re a merchandise planner at a global retailer or a Director of FP&A at a multi-national auto manufacturer, our forecasting guide, “How AI-Powered Continuous Planning Mitigates Economic Volatility,” will help your teams navigate uncertainty to plan confidently in a rapidly changing world.

We’ve compiled from Board’s team of expert economists the answers to the most important questions about how AI-powered continuous planning can mitigate uncertainty due to emergent economic volatility.

Understanding the Current Economic Climate

In what ways is economic volatility manifesting for enterprises in 2025, both in the US and internationally?

It’s a particularly complex economic environment right now. In the US, our baseline scenario suggests we’re in a period of mild stagflation, with below-trend growth coupled with persistent inflation pressures and a weakening labor market. This creates a challenging dynamic where businesses are dealing with softer domestic demand while facing continued cost pressures from price increases and supply chain constraints.

Internationally, the picture varies significantly by region, but many enterprises are grappling with divergent monetary policies, trade policy uncertainty, and shifting consumer behaviors.

What indicators or data points are most critical for planning teams to monitor in today’s volatile economic environment?

Beyond the traditional headlines of GDP and inflation metrics, planning teams are focusing heavily on supply chain stress indicators, labor market dynamics, and consumer sentiment at a much more granular level. Trade flows, commodity prices, and regulatory policy announcements have become critical leading indicators, especially for businesses with complex supply chains.

What’s particularly valuable is combining high-frequency data, like shipping container movements or even credit card spending patterns, with traditional economic indicators. This gives planning teams a more comprehensive view of economic conditions as they’re actually unfolding, rather than waiting for official statistics that can lag by months.

What are the primary challenges organizations face when forecasting in a highly dynamic market?

The biggest challenge is that traditional forecasting models, built on historical relationships, can break down when the underlying economic structure is shifting. Businesses across every industry struggle with forecast accuracy because the relationships between economic drivers and business outcomes have fundamentally changed.

Many organizations are also dealing with data silos – their economic assumptions aren’t integrated with their operational forecasts, leading to disconnected planning processes. When economic conditions change rapidly, teams end up re-forecasting from scratch rather than dynamically adjusting their models, which costs valuable time and resources during critical decision-making windows.

Market Dynamics and Impacts on Business

Which sectors are most vulnerable to current economic fluctuations, and why?

Consumer discretionary sectors are particularly exposed right now. With persistent inflation eating into real income growth and consumer sentiment remaining subdued, businesses in retail, hospitality, and non-essential services are seeing demand volatility that’s difficult to predict using traditional methods.

Manufacturing sectors with complex international supply chains are also highly vulnerable, particularly those dependent on specific trade routes or raw material inputs. The combination of trade policy uncertainty and supply chain constraints means these businesses need intentional scenario planning to navigate potential disruptions.

How is consumer behavior evolving in response to recent economic pressures, and what planning implications does this have?

There is a persistent and fundamental shift toward more cautious, value-conscious spending patterns. Consumers are increasingly prioritizing essential purchases over discretionary spending, but they’re also becoming more responsive to price signals and promotional activities. This creates both challenges and opportunities for businesses.

From a planning perspective, this means demand forecasting needs to be much more granular and responsive. Businesses can’t rely solely on broad demographic or seasonal patterns anymore – they need to understand how economic conditions are affecting specific customer segments and adjust their inventory, pricing, and promotional strategies accordingly. This is where AI-powered forecasting becomes invaluable, because it can process these complex, multi-dimensional relationships in real-time.

What are examples of businesses that have successfully navigated recent volatility by changing their approach to forecasting?

Two Board customer examples, are Milwaukee Tool and Whataburger:

Milwaukee Tool, a leading manufacturer of hand tools, power tools, and construction accessories designed for professionals, sought a solution to better understand macroeconomic trends and anticipate their impact on key product categories amid the immense disruption caused by the COVID-19 pandemic. As Brad Sayers, VP Supply Chain at Milwaukee Tool, said, “Board Foresight is unmatched in its ability to visualize data, reveal correlations, and provide unique insights into our business. This all-in-one solution, coupled with continuous improvements and alignment with our approach to planning, has made a tangible impact, confirmed the feasibility of our plans and guided strategic investments.”

Whataburger, a fast-food restaurant chain founded in Texas with over 900 locations and $3 billion in revenue, faced challenges with forecast accuracy. While the company had a wealth of historical data, they lacked an external perspective on the factors that influenced their in-market performance. Whataburger recognized the need for a solution that could help them anticipate market trends, avoid costly misses, and make informed decisions to navigate the dynamic business landscape. As Pete Valadez, Director of Financial Planning & Analysis at Whataburger, said, “Board Foresight has been a game-changer for Whataburger. By incorporating external data and predictive analytics into our forecasting process, we’ve been able to dramatically improve our accuracy and avoid missed expectations. The real-time alerts and collaborative nature of the solution have empowered our entire team to make data-driven decisions with confidence.”

What’s the business impact that access to real-time external data sources play in capturing early signals of volatility for planning purposes?

The impact is transformational. Traditional planning cycles often mean businesses are making decisions based on data that’s already 30-60 days old. With real-time external data integration, planning teams can identify market shifts weeks earlier than their competitors.

This early warning capability translates directly to revenue results. Whether it’s adjusting production schedules before raw material costs spike or optimizing inventory levels before demand patterns change, the ability to act on early signals can drive the difference between capturing opportunities and missing them entirely.

AI/ML and Forecasting in Enterprise Planning

How do AI and machine learning improve forecasting accuracy compared to traditional methods?

The fundamental difference is that AI can process massive amounts of external data that traditional methods simply can’t handle. While conventional forecasting relies primarily on your internal historical data – what happened with sales in your last quarter – AI-powered forecasting can incorporate thousands of external economic signals that drive your business outcomes. We’re talking about employment trends, commodity prices, consumer sentiment, trade flows, and hundreds of other indicators that precede changes in your market by weeks or months.

What makes this powerful is the speed and scale of pattern recognition. Human analysts might track 10-20 key indicators, but AI can simultaneously monitor thousands of data points and identify which combinations reliably predict changes in your business. With Board Foresight, we consistently see significant improvements in forecast accuracy because we’re giving businesses an economic radar – the ability to see market changes forming before they impact your operations, rather than reacting after the fact.

Additionally, in the near future your resulting forecasts can be discussed and analyzed with our new agentic AI capabilities including our Board Economist Agent. This agent can pass data directly to an FP&A planning solution and will work seamlessly with our Board FP&A Agent. The FP&A Agent understands user context, anticipates planning team needs, and will proactively assist through natural language interactions and role aware workflows. Planners can query forecasts, request scenario simulations, or trigger reports using plain English to have the agents generate explainable insights, suggest new analyses, detect anomalies, and even recommend corrective actions.

In what ways can AI-forecasting tools help identify root causes of variances and reduce human bias in projections?

One of the most valuable aspects of AI-powered forecasting is its ability to automatically identify which external factors are driving forecast variances. When actual results deviate from projections, Foresight can quickly pinpoint whether it’s due to economic conditions, competitive actions, supply chain disruptions, or other factors.

This eliminates the guesswork and reduces the cognitive biases that often creep into traditional forecasting processes. Human forecasters tend to over-weight recent events or anchor on familiar patterns, but AI models evaluate all available data objectively and continuously update their assessments as new information becomes available.

How can AI-powered continuous planning help organizations react faster to sudden changes in demand, supply, or pricing?

Speed is everything in volatile markets. AI-powered continuous planning enables organizations to detect and respond to changes as they’re happening, rather than waiting for the next planning cycle. When economic indicators suggest shifting demand patterns, or when supply chain data indicates potential disruptions, the system can automatically trigger scenario analyses and recommend adjustments. Furthermore, Board has an AI-powered correlation engine to simulate complex scenarios for forecasts and resource allocations via plain English prompts. And our Board Agents can autonomously produce scenarios with contextual interpretation of internal and external data, interact with other Board tools, other agents, and even collaborate directly with your planning teams.

Organizations are reducing their response time to market changes from weeks to days, simply by automating the process of connecting external signals to internal planning models. This agility creates a significant competitive advantage when economic conditions are changing rapidly.

What steps should enterprise planning teams take to integrate AI/ML seamlessly into existing planning cycles?

The key is starting with external data integration rather than trying to overhaul entire planning processes at once. Begin by identifying the external economic indicators that most strongly correlate with your business outcomes, then gradually incorporate AI-powered analysis of these signals into your existing forecasting workflows.

It’s also crucial to maintain human oversight and interpretation. AI excels at pattern recognition and data processing, but human expertise is essential for understanding the business context and making strategic decisions based on AI-generated insights. The most successful implementations combine AI’s analytical power with human judgment and industry knowledge.

Moving Toward Continuous Planning

Why is continuous planning more effective than periodic or static planning in today’s business climate?

Static planning cycles simply can’t keep pace with the rate of economic change we’re experiencing. By the time traditional quarterly or annual planning processes are complete, the underlying assumptions may have already shifted significantly. Continuous planning allows organizations to constantly recalibrate their strategies based on the latest economic conditions and market signals.

This is particularly critical when dealing with economic volatility, because the windows of opportunity and risk are much shorter. Businesses that can adjust their plans monthly or even weekly have a significant advantage over those locked in quarterly planning cycles.

How does continuous forecasting enable more agile responses to unexpected economic shocks or opportunities?

Continuous forecasting creates an early warning system that helps organizations anticipate and prepare for economic shifts before they fully materialize. Rather than reacting to changes after they’ve occurred, businesses can proactively adjust their strategies based on emerging economic signals.

This proactive approach is especially valuable during periods of economic uncertainty, when the ability to quickly pivot strategies, reallocate resources, or adjust operational plans can determine whether an organization successfully adapts to or merely survives economic turbulence.

What are best practices for shifting planning organizations from static annual forecasts to dynamic, ongoing scenario forecasting?

The transition should be gradual and focused on high-impact use cases first. Start by identifying the business areas most affected by economic volatility, typically demand planning, supply chain management, and financial forecasting, and implement continuous planning processes there first.

Cultural change is equally important as technological implementation. Planning teams need to shift from a mindset of creating perfect annual plans to one of continuous improvement and adaptation. This requires executive support, clear communication about the benefits of agility over precision, and training teams to use AI-powered tools effectively.

How does AI enable ad-hoc scenario analysis and support cross-functional decision-making in real time?

AI dramatically reduces the time required to run complex scenario analyses from days or weeks to minutes or hours. When economic conditions change unexpectedly, cross-functional teams can immediately model different response scenarios and understand the potential impacts across finance, operations, and sales. Teams can also build baseline, optimistic, and pessimistic forecasts grounded in real-time.

This real-time capability enables much more collaborative and informed decision-making. Rather than each function making assumptions about how economic changes might affect their area, teams can work with shared, user-generated scenarios that account for the interconnections between different business areas.

What cultural and organizational changes are necessary to support a successful transition to AI-powered, continuous planning?

The most important cultural shift is moving from a plan and execute mindset to a plan, learn, and adapt approach. This requires organizations to become more comfortable with uncertainty and to view planning as an ongoing process rather than a periodic event.

Leadership needs to champion this change by demonstrating that agility and responsiveness are valued over adherence to original plans. Teams also need training not just on assessing AI tools, but on how to work with AI to interpret and act on AI-generated insights within their specific business context.

Learn more about Board AI & Board Foresight.