Decision Intelligence

As a new data scientist, you’re struggling with a large data set and how you can make it tell a story. The stakes are high; you’ve been told by your manager that this data should inform strategic thinking at your company for approaching new AI methods. Before you get even more lost in your data, a colleague mentions a platform that pairs causal modelling and real-time feedback. Curiously, you dig deeper and realize this tool doesn’t just visualize data nicely—it also models outcomes, displays trade-offs, and highlights when decisions are deviating from long-term goals. With this solution, decision intelligence is to the rescue. 

Decision intelligence (DI) transforms raw data into practical, strategic choices by integrating artificial intelligence, data science, and decision theory.^{1, 2} DI can be seen as a bridge between analytics and action by helping organizations make quicker, more reliable choices. In its repertoire of multi-disciplinary uses, DI is able to consider novelty in company decision-making on several levels, ranging from strategy to operations, with context in mind.

It might be the first time you’ve heard about decision intelligence, but you may recognize its close relative, business intelligence (BI), and undoubtedly have heard about artificial intelligence once or twice. Depending on who you ask, DI and AI have vastly different scopes compared to their neighbor, BI. The lack of a standard definition differentiating these related concepts can make the field of DI feel rather muddy, so let’s make these distinctions before going deeper:^{1, 3}

DI assumes that the choices an organization makes are founded in recognizing that behaviors lead to results. Due to this inherent loop of cause and effect, DI analyses are often transformed into visualizations as a clear way to show what these relationships look like in reality. The data that DI deals with can come from many places, like structured data and unstructured data.³ In our efforts to get the story straight, our mutual friends AI and machine learning supplement how we find data patterns, anticipate trends to come, and make behavioral insights.¹ At its best, decision intelligence makes someone’s data story coherent and compelling without the prerequisite of being a data scientist. 

The key elements of decision intelligence in practice draw from fields related to AI and automation, as well as data and analytics. Some of these elements might include:³

Instead of the fallible gut instinct or the isolated siloing of decision-makers, decision intelligence gives a structured, reusable framework for navigating uncertainty and complexity.^{2, 3} Choices are not so simple nowadays, but they can be made more straightforward thanks to DI. In its many layers of applications, decision intelligence must consider the decision architects themselves—us humans, and how we make up our minds in relation to non-human intelligence.

Blending human and machine judgment

Don’t worry, DI isn’t taking your job: decision intelligence isn’t a replacement for human judgment, but an enhancement of it. Humans can be involved in AI decision-making processes to different degrees, with choice architecture oriented in one of three ways:³

1. Decision Support: Humans are “in” the loop with AI, as it gives us behavioral insights and makes simulated possibilities of paths not yet traveled.
2. Decision Augmentation: Humans are “on” the loop with AI, as it creates advice knowing that context matters and what impacts may occur. When humans are on the loop, we make the last call when it comes to making, amending, or rejecting a decision. 
3. Decision Automation: Here, humans are completely “out” of the loop, AI is acting on its own accord for our decision-making as it behaves within the parameters we’ve established. These behaviors can be audited, so we can keep track while outside the loop. 

This discipline can integrate behavioral science and systems thinking as it accounts for how real people interpret, respond to, and shape decisions in dynamic environments. Rather than replacing human judgment, decision intelligence provides support by making mental models visible or finding options we might otherwise overlook as human users. In this way, decision intelligence acts less like a final answer or a substitution for humans—it’s more like your data-friendly decision-making partner.