Artificial intelligence (AI) can be leveraged to understand patterns and use this to supply information used for strategic business decisions. Leading businesses and their data teams are leveraging artificial intelligence to improve demand planning and forecasting accuracy, and these businesses are seeing results. But in order to build effective AI systems and ML models to perfect your forecasting methods, it's essential to invest in large amounts of data and the right types of data, which really means quality data. And it doesn’t stop there. In the rapidly evolving landscape of artificial intelligence, the key to smarter decision making lies not just in the training data we feed into our models, but in the real-world context it can tap into once these models are trained. Allowing for more accurate and dynamic understanding to ensure the models recommendations are able to navigate volatility. The world is dynamic, and the models that give us a competitive edge need to be as well.
Data challenges when adopting AI systems
To effectively train artificial intelligence systems for demand modeling, teams must think about the data they’re using. While likely obvious to forecasting experts and data gurus, a Gartner survey run during a 2023 Data & Analytics Summit found that 33% to 38% of organizations reported failures or delays in AI projects due to poor quality data. It’s easy for businesses to leverage historical sales data and seasonality trends. But there are a vast amount of external factors that cause unexplained demand anomalies that significantly impact the bottom line. Event data provides insight into many external demand anomalies. You have the ability to train AI models to learn the patterns of events causing business disruptions and pivot strategies around inventory management, pricing, labor optimization, and more.
AI systems demonstrate an unprecedented capacity to process vast quantities of data rapidly. However, they often lack the ability to understand the dynamic and fluid nature of real-world events. Traditional datasets, even the most comprehensive ones, can't keep pace with the constantly changing nature of event data. Think about changed dates, changed start and end times for events, cancellations and more. Even some of the largest events can get changed last minute.