New developments in data science offer a tremendous opportunity to improve decision-making. Machine learning, pattern recognition, and other predictive analytics tools can constitute a source of competitive advantage for those companies that adopt them early on; but like any new capability, there is an enormous gulf between awareness, intent and early engagement, and achieving significant business impact.

How can companies better manage the process of converting the potential of data science to real business outcomes?  How can companies go beyond merely generating new insights to changing behaviors — not only of their employees, but customers too? We would like to offer some lessons from AIG’s early experiences with deploying new analytical tools to leaders across industries who may be considering embarking on a similar journey.

In January 2012, AIG launched the “Science Team.” One might be surprised to find a Science Team in an insurance company. However, Peter Hancock, President and CEO of the global insurance giant, saw a huge opportunity to apply evidence-based decision making in an industry which was still very reliant on individual expert judgment and in so doing to create not only tactical but also competitive advantage. By early 2014, 130 people from diverse scientific and managerial backgrounds were devoting themselves to realizing the team’s mission: To be a catalyst for evidence-based decision making across AIG.

The Science Team intentionally refrains from using the words “data” or “analytics,” as the team’s capabilities stretch far beyond these two disciplines: behavioral economists, psychologists, engineers, and change management experts work hand-in-hand with data scientists, mathematicians, and statisticians. And for good reason: this multidisciplinary approach is essential to go beyond merely generating new insights from data but also to systematically enhance individual human judgment in real business contexts. Ninety percent of the team was recruited from beyond the insurance industry to enable it to challenge the status quo approach to decision-making. The Science Team not only prepares data and builds models, but also emphasizes the identification of business opportunities and education, change management and implementation—the complete value chain from framing questions through to changing behaviors.