The “Intelligence Value Gap” in Decision Sciences represents a critical juncture where traditional Business Intelligence (BI) transitions into more advanced Decision Sciences methodologies. This gap, as depicted in the diagram below and highlighted in blue, signifies the point where reliance on historical insights (descriptive and diagnostic analytics) must be enhanced with predictive and prescriptive analytics to support effective decision-making.

The Intelligence Value Gap arises primarily due to the limitations of traditional BI that’s heavily focused on describing what has happened and diagnosing why it happened. While these insights are valuable, they inherently lack the foresight and sophistication required for anticipating future challenges and opportunities. This gap often gets filled with human intuition, which, while necessary, can introduce biases and inconsistencies, undermining the reliability and scalability of decision-making processes.

A 2017 McKinsey study of executive-level decision-making highlights the critical need to overcome biases that drive intuition-based decision making, revealing a strong correlation with value creation. The study, which surveyed 1,271 executives, found that less than half of them believed their companies considered a range of potential outcomes or scenarios for a given investment. Furthermore, less than one-third reported using common checks on biases beyond scenario analysis. The study also indicated that companies employing evidence-based decision making were 36% more likely to report faster growth and 22% more likely to achieve higher profitability compared to their peers who did not.²

This underscores the need for a significant recalibration in decision-making processes, shifting from intuition to scientific, evidence-based approaches. Despite a deluge of reports and historical data available in most companies today, the effective and timely interpretation of these data into strategic recommendations to support decision-making remains minimal, with most decisions still heavily reliant on intuition. To bridge this gap — to close the information value gap — there is a pressing need for specialized talent focused on evidence-based decision making.

To this end, organizations are increasingly leveraging predictive analytics, which forecasts future outcomes based on historical data, and prescriptive analytics, which not only predicts but also suggests course actions and shows the implications of each decision. Predictive analytics identifies what is likely to happen, while prescriptive analytics builds upon these insights to provide direction on how best to respond.

By leveraging outcomes predicted through predictive analytics, prescriptive analytics is better positioned to craft actionable strategies that businesses can implement to optimize results. In combination, these methodologies advance decision-making from reactive to proactive, enabling businesses to anticipate changes and make informed decisions that are evidence-based.

As per the diagram below, also increasingly important in the transition from predictive to prescriptive analytics is causal inference, which is the study of cause-and-effect relationships. Causal inference encompasses a range of methods designed to work with experimental or observational data. By controlling for or minimizing confounding variables, these methodologies help us to determine which actions or decisions are likely to lead to desired outcomes, empowering decision-makers to implement strategies that are not only based on correlation but are truly causative.

“Successfully integrating all three levels of analytics in the context of business process improvement requires combining data science with decision science. Data-driven strategies that are not decision-driven strategies can be seriously flawed. From a methodological point of view, transitioning from data science to decision science requires (1) a holistic view of the process—from data to descriptive, predictive, and prescriptive analytics—and (2) generating the correct inputs through predictive analytics to inform prescriptive analytics.”⁴

With the advent of Cognitive AI and more recently, Generative AI (GenAI), the landscape of decision-making tools has expanded significantly. Cognitive AI, as a part of decision automation, goes beyond prediction to include decision-making capabilities by simulating human thought processes in complex situations. GenAI further extends this by not only predicting outcomes but also generating new content and ideas, thus potentially filling the Intelligence Value Gap more completely. 

I think this quote from the book Prediction Machines (Harvard Business Review) is very interesting to consider; “The new wave of artificial intelligence does not actually bring us intelligence but instead a critical component of intelligence – prediction.”⁵ The quote emphasizes that AI, particularly in its “traditional” forms, enhances decision-support by improving our predictive capabilities. However, as the authors suggest, prediction is merely a component of intelligence, not a substitute for the comprehensive decision-making process. This observation remains relevant even in the age of GenAI. While GenAI transforms how predictions are generated and interacted with, turning data into more actionable insights, the necessity to evaluate “what-if” scenarios, discover causal relationships, and integrate contextual intelligence into decision-making persists.

However, as GenAI continues to evolve — because we are still in its infancy and are already being blown away daily by continuous advancements — its ability to not just predict but also reason and create under diverse scenarios might soon diminish the distinction highlighted in the Intelligence Value Gap. The future might see GenAI not only providing inputs but orchestrating complex decision strategies, thereby integrating predictive intelligence seamlessly into decision frameworks.

In summary, the Intelligence Value Gap in decision sciences underscores a crucial need for advanced analytical tools that extend beyond traditional data descriptive capabilities to predictive and prescriptive insights. While tools like GenAI are changing the game as we speak (providing important tools to help narrow the information gap) the complete closure of this gap involves not just advanced technologies but also a strategic overhaul of decision-making processes so that we embrace these advancements effectively (topic for another post). This (r)evolution will dictate how organizations leverage data to make not just informed but also foresighted decisions that can dynamically adapt to changing market conditions and business needs.

1. Implementing Advanced Analytics (Deloitte) ↩︎
2. Decision Science Helps Boost Business ↩︎
3. From Meaningful Data Science to Impactful Decisions: The Importance of Being Causally Prescriptive ↩︎
4. From Meaningful Data Science to Impactful Decisions: The Importance of Being Causally Prescriptive ↩︎
5. Agrawal, A., Gans, J., & Goldfarb, A. (2022). Prediction machines : the simple economics of artificial intelligence (Updated and expanded.). Harvard Business Review Press. ↩︎