Forecasting Reimagined: Building Smarter, Adaptive Models

Overview

This is a rebroadcast from AICPA ENGAGE 2026.

Traditional forecasting methods often struggle to keep pace with today’s rapidly shifting markets, economic conditions, and business realities. Modern finance teams need forecasting models that are dynamic, responsive, and informed by both internal performance data and external market signals.

This webcast explores innovative approaches to forecasting that improve accuracy, reduce manual effort, and support better business decisions.

Learn how automated forecasting assistants can:

Streamline planning processes

Minimize common forecasting biases

Accelerate scenario analysis

You'll also discover how to incorporate industry benchmarks, macroeconomic indicators, and market intelligence into models that continuously adapt as conditions change.

Key features and benefits:

Learn how to combine internal and external data sources for more informed forecasts

Explore automated forecasting assistants that improve efficiency and reduce bias

Build adaptive models designed to respond to changing market conditions

Incorporate benchmarks, economic indicators, and market signals into forecasting processes

Enhance forecast accuracy and decision-making with smarter, more responsive planning techniques

Highlights

· Data-driven forecasting · Automated & AI-assisted forecasting · Adaptive planning models · Scenario analysis & decision support · Market intelligence, benchmarks & economic indicators

Prerequisites

3-4 years in the profession

Designed For

Corporate finance and controllers

Objectives

Analyze how to integrate internal data with external intelligence sources to create more robust and responsive forecasting models.
Identify how to deploy automated forecasting assistants that reduce manual effort, eliminate common biases, and accelerate scenario planning cycles.
Apply techniques for creating forecasting models that automatically adjust to changing market conditions and business environments without constant manual recalibration.
Determine how industry benchmarks, macroeconomic data, and market signals can enhance forecast accuracy and relevance.