The need to predict behavior

Humanity forms a global community, composed of unique individuals. Societies function cohesively because we can build mental models of other people. Predicting human behavior is essential for understanding and interacting with the world. We all build mental models to anticipate what brings joy, amusement, positive motivation, as well as what causes sadness, disappointment, anger, or boredom. We learn learn to navigate social interactions effectively and avoid detrimental behaviors. This applies to both individuals and organisations pursuing their goals.

Individuals inherently possess an advantage over organizations in their ability to instinctively grasp social cues. They also operate within smaller groups. Scaling this capacity to large populations presents a significant challenge for organizations. Today, AI models finally offer a solution to this problem.

The organisational challenges

While modern companies have teams of Data Scientists and ML engineers, scaling data science in organizations remains challenging. The traditional approach to data science prevents teams from reaching their full potential due to several key challenges:

• Addressing numerous business uses cases with specific models and customized inputs requires extensive manual work, as models and features are handcrafted,

• Constantly evolving data sources and schema necessitate continuous maintenance of data pipelines, leading to significant re-work,

• The increasing amount and dimensionality of data demand expert knowledge, creating bottlenecks.

Consequently, the combination of manual work, re-work, and the need for experts results in insufficient capacity. This situation forces teams to selectively use and understand data, introducing bias and limiting their ability to address all desired use cases and meet business expectations. As a result, teams become entrenched and defensive, ultimately hindering their ability to fully leverage their potential.