“The widespread organizational adoption of business intelligence and analytics systems has not resolved a persistent structural problem: the gap between the availability of analytical information and its effective use in decision-making.” Drawing on evidence that includes public health dashboard failures during the COVID-19 pandemic, the author argues that information overload, insufficient narrative context, and poor communication design can prevent decision-makers from translating data into effective action."

“Despite advances in clinical care, technology, and health system infrastructure, maternal deaths [in the United States] remain higher than in most other high-income nations… Black women, in particular, continue to experience maternal mortality rates that far exceed those of their White counterparts, even after adjusting for socioeconomic status and education.” The authors further explained, “While advances in machine learning have improved the prediction of obstetric complications, these models rarely translate into actionable operational decisions.” As such, “Gradient boosting models were used to predict postpartum hemorrhage risk and short-term resource demand. These predictions were then incorporated into a reinforcement learning–based optimization system to guide dynamic resource allocation.” And the results indicated important positive outcomes: “Causal analysis indicated that adequate resource availability was associated with a 21% reduction in severe maternal morbidity (ARR = 0.79; 95% CI: 0.72–0.87), with stronger effects in structurally vulnerable populations.”

"One of the most persistent sources of confusion we see in organizations that want to improve decision making using AI and/or data science is a quiet category error: treating all decisions as if they were the same kind of problem. They aren’t.
...
This distinction isn’t academic. It shapes what methods work, what risks we take, and whether AI actually improves decision quality."

"Three consecutive drought years have tested every assumption built into the [Yakima River Basin's] governance framework. A fourth drought year in 2026 has just been declared.
Against this backdrop, decision intelligence (DI) has a specific and urgent role to play.
...
We will build a DI design through the construction of a causal decision diagram (CDD), which shows factors that cause different outcomes, variables that can be controlled, and where models or data feed into the decision process."

“Regulated industries and governments cannot rely on opaque 'black box' systems for consequential decisions. DI elevates explainability from a technical requirement to a governance imperative.”

"We are not lacking data, tools or talent. What we lack is a system and a mindset that turns intelligence into action. And that gap is exactly where decision intelligence begins.
...
This is a big shift from how most of us have traditionally thought about data and analytics. And honestly, this framework makes you look under the hood of how decisions really get made in your organization. That’s not always comfortable. ... But that's where the value is."

“... there are thousands of decisions that happen every day across complex supply chains, and we see decision intelligence as an opportunity to increase productivity"

"The business world is on the cusp of a profound shift, moving away from the 'data-driven' mantra to one that is 'decision-centric,' powered by Decision Intelligence Platforms.
...
Decision-making augmentation involves platforms ensuring a human has processed, integrated, and contextualized information, while also managing the approval workflow (like coordinating sign-offs)."

“Just 5% of integrated AI pilots are extracting millions in value, while the vast majority remain stuck with no measurable P&L impact. This divide does not seem to be driven by model quality or regulation, but seems to be determined by approach.”

"A dashboard is not decision intelligence. Visibility is not decision intelligence. Workflow automation is not decision intelligence unless it improves the quality, speed, and coordination of operational decisions. ... Supply chain technology is moving toward a layer that can sit above fragmented systems, interpret context, connect signals, support tradeoff decisions, and help coordinate action under changing conditions."

“The central thesis of this study is that value manifests only when AI evolves into true Decision Intelligence embedded within the institution's core processes.”

Causal Decision Diagrams help decision-makers visually capture the causal relationships between desired goals, the different choices of levers to pull, and any intermediate results that connect them.

Hosted by Teasha Cable, the Dynamic Decisions Podcast explores how leaders and organizations make better decisions in complex environments. Topics include AI, judgment, digital transformation, and the connection between choices and outcomes.

"The Getting Started with Decision Intelligence course gives you a “DI practice in a box”. A step-by-step, hands-on method that has been proven in hundreds of organizations worldwide.
*We’ll be using ChatGPT / generative AI in live course sessions to supercharge DI"

"Another important emerging technology is dual-enginedecision intelligence. Supported by both operations optimization and machine learning, the dual-engine decision intelligence system enables the dynamic, comprehensive and real-time resource allocation, such as real-time electricity dispatching, optimization of port throughput, assignment of airport stands and improvements in manufacturing processes. As such, the technology can also help businesses enhance operational efficiency." 

"Though it’s the stock response, 'insights' is precisely the wrong way to think about how an advance in AI will create value. In fact, we feel that 'insights' often is code for 'We don’t know what to do with our AI’s predictions.'

A much better answer would be to describe the decisions that the predictions will improve, because AI has value only if it leads to better decision-making."

"In this article, we’ll examine the phenomenon of bounded awareness—when cognitive blinders prevent a person from seeing, seeking, using, or sharing highly relevant, easily accessible, and readily perceivable information during the decision-making process. 'The information that life serves is not necessarily the information that one would order from the menu,' notes Dan Gilbert of Harvard University’s psychology department, 'but like polite dinner guests and other victims of circumstance, people generally seem to accept what is offered rather than banging their flatware and demanding carrots.'...

It’s important to note that bounded awareness differs from information overload, or having to make decisions with too much information and too little time. Even when spared a deluge of information and given sufficient time to make decisions, most individuals still fail to bring the right information into their conscious awareness at the right time."

"Decision intelligence (DI) is at the Innovation Trigger of the Hype Cycle. DI is a practical discipline used to improve decision making by explicitly understanding and engineering how decisions are made, and how outcomes are evaluated, managed and improved via feedback....

There is a need to transparently represent how decisions are being made.

From a pure business perspective, it makes sense to curtail unstructured ad-hoc decisions that are siloed and disjointed, and properly harmonize collective decision outcomes across an entire organization. Software tools are now emerging that will enable organizations to practically implement DI projects and strategies."

"According to Gartner’s AI Hype Cycle report which urges companies to, 'pay early attention to innovations expected to hit mainstream adoption in two to five years, including composite AI, decision intelligence and edge AI.'

Despite the shift to a data-centric approach, AI models still need attention to ensure the outputs continue to help us to take better actions... 

Decision intelligence and edge AI are both expected to reach mainstream adoption in two to five years and have transformational business benefits, according to Gartner. Innovations here include AI engineering, decision intelligence, operational AI systems, ModelOps, AI cloud services, smart robots, natural language processing (NLP), autonomous vehicles, intelligent applications and computer vision."

"In March 2020, the NHS needed to make life-or-death decisions on the back of understanding whether curves were trending up or down. For the first time ever, they connected data flows from across the entire system.

That is a potentially overwhelming amount of data – but, crucially, they weren’t paralysed by this sudden torrent... Why was that?

Well, working alongside the NHS, we applied approaches from a nascent field called decision intelligence. We used this data to build the understanding the NHS needed to make confident decisions about which hospital wards to open or close, and which resources to send where.

Rather than just showing data on current levels of patient demand, we could also show demand for tomorrow or next month, the reasons why demand was changing, how that affected resources such as beds or ventilators, and what actions they could take.

Rather than making decisions on data alone, the NHS made them on an understanding of how and why events were unfolding. The collective results have been widely credited with saving thousands of lives."

"So here’s a question for you: does the software system you’re building help people to 'do', or to think, or both? 

And if it helps them to think, then do you understand how they think today? Do you know what’s natural to them? Are you asking them to learn to think in an entirely different way, just to use your system? Or have you learned – like the great 'doing' software designers of the last century – that it’s your job to start with the user, to meet them where they’re at?"