AI Hype vs. Reality: Spotting True AI vs. Statistical Models

The term "AI/ML forecasting" is often overused in corporate performance management (CPM) solutions. Many vendors, for instance, label basic statistical models as artificial intelligence (AI), creating confusion for buyers. In this post, we clarify the difference between statistical models and true machine learning (ML) models, helping organizations spot AI hype and identify genuine innovation. Understanding these distinctions will help organizations make more informed decisions, ensuring any investment in solutions truly enhances business performance and drives meaningful outcomes.

True AI vs. Statistical Models

• Statistical Models: Rely on historical patterns from a single variable (univariate), such as sales trends, using techniques like linear regression or ARIMA. These models are fast but limited due to ignoring external factors and relationships between variables.

Machine Learning (ML) Models: Analyze multiple variables (multivariate) and automatically uncover patterns. ML models adapt and improve over time as new data becomes available. Critically, ML models can incorporate external variables such as market trends, economic indicators, weather patterns, and competitor activity — factors that significantly impact business performance. Plus, according to a study from Collibra, companies that incorporate external data are 58% more likely to exceed revenue targets, so their inclusion is essential for accurate forecasting.

How to Identify Whether a Vendor Uses True AI or Statistical Models

• Ask About Inputs: True ML models use multiple inputs, including external variables such as macroeconomic data, political and regulatory changes, or environmental factors. Conversely, statistical models typically rely on a single time series of data without incorporating external variables to directly influence the prediction process. Vendors offering statistical only models tout the ability to incorporate variables, such as events, but the variables do not influence the prediction. In turn, the inclusion of events in the process, but not the prediction, does not improve the forecast accuracy.
• Look for Adaptability: ML models retrain automatically as new data is introduced, ensuring predictions evolve with changing business conditions. The models can capture shifting trends, seasonality, and emerging patterns without manual intervention. In contrast, statistical models often require frequent manual adjustments, making the models less responsive to sudden market shifts or external disruptions.
• Evaluate Explainability/Transparency: Asking for insights into how forecasts are generated is essential as ML models often include important metrics that show which drivers influenced the predictions. To facilitate trust and ultimately user adoption, transparency into the forecast numbers is critical.

Why It Matters:

Relying solely on statistical models often results in rigid, oversimplified, and inaccurate forecasts that miss shifts in a fluid business environment. By understanding the difference between statistical methods and ML-based forecasting, organizations can cut through the AI hype and make more informed technology investments.

Introducing SensibleAI™ Forecast (previously known as Sensible ML)

OneStream’s SensibleAI Forecast is a no-code solution. With it, businesses can use true ML to efficiently create highly accurate forecasts in a fraction of the time of traditional processes. Sensible AI Forecast revolutionizes financial and operational planning by embedding AI-driven forecasting within a unified business planning platform. This cutting-edge solution enables organizations to harness the power of ML without requiring specialized data science expertise, delivering faster, more accurate, and scalable forecasting capabilities.

Here are a few key auto-ML capabilities of SensibleAI Forecast to empower business users to accurately and efficiently predict and steer the business:

1. State-of-the-Art ML Models: Out-of-box ML models cater to different forecasting needs. To consistently deliver the best results, the AI team also constantly evaluates the best ML models.
2. Model Arena: This proprietary engine selects the best model for each forecasted line item (product-location combination) to maximize forecast accuracy by tailoring to unique data patterns.
3. External Data: With just the click of a button, business users can bring in external data, including macro data, supply chain indicators, COVID-19 events, weather events, and much more.
4. Health Score & Automatic Model Rebuild: Dynamic market conditions constantly update the data feeding into models. To monitor model performance, SensibleAI Forecast’s Health Score automatically retrains and rebuilds models to adapt to evolving business patterns and data trends. This capability ensures year-round forecast accuracy.
5. Transparency: Looking at past patterns, SensibleAI Forecast quantifies the impact of company-specific or external events. The impact of holidays, promotions, price adjustments, and supply chain disruptions, for instance, tend to affect certain outcomes. Thus, SensibleAI Forecast transforms financial planning by bringing unmatched transparency into data-driven and justifiable strategic business decisions.

Conclusion

In a market flooded with AI hype, distinguishing true ML from basic statistical models is critical for organizations seeking accurate, agile forecasting. True AI models go beyond historical data, incorporating external variables and continuously learning to improve predictions. OneStream’s SensibleAI Forecast delivers on this promise by providing a no-code, transparent, and automated ML solution that enhances financial and operational planning.

Learn More

Are you prepared to separate AI reality from marketing buzz? Start by asking the right questions and demanding transparency from your vendors. To learn more on investing in AI that drives real business value, visit the OneStream AI Hub.