This is Part 2 of a three-part series on Decision Intelligence. In Part 1, I explored why most analytics teams are not set up for Decision Intelligence and why the gap between insight and action continues to persist in many organisations.
The gap is clear.
Despite significant investment in data and analytics, many organisations still struggle to make decisions that are consistently better and faster. The issue is not the availability of data or the sophistication of models. It is how organisations design for decision-making.
Closing the gap between insight and action requires a shift from analytics-centric thinking to decision-centric design.
The first step is to start with decision intent. Too often, analytics is structured around data domains or business functions. Decision Intelligence begins by identifying the critical decisions that drive value, risk, and performance. This aligns analytical effort with outcomes, rather than outputs.
The second step is to embed analytics into decision workflows. Research shows that delays between data availability and action are a major constraint in organisations, with many relying on data that is already outdated by the time it is used (IBM, 2023). Embedding intelligence directly into workflows reduces latency and ensures insights are available at the moment decisions are made.
The third step is to clarify decision authority. Decision bottlenecks are often not caused by lack of insight, but by unclear ownership and fragmented accountability. Studies have shown that ineffective decision processes consume a significant portion of managerial time, limiting organisational performance (McKinsey & Company). Establishing clear decision rights, including where decisions can be augmented or executed by Agentic AI, is critical to improving both speed and consistency.
The fourth step is to connect decisions to execution and learning. A decision only creates value when it leads to action. Organisations that excel in decision-making build feedback loops that track outcomes and continuously improve decision quality over time. This aligns with broader research showing that high-performing organisations treat decision-making as a system rather than a series of isolated events.
Together, these shifts reposition analytics from a support function to a core component of the decision system. Closing the gap is not about adding more tools or increasing data volume. It is about redesigning how decisions are made, governed, and executed across the organisation. Those who succeed will not simply have better analytics capabilities. They will have a sustained advantage in how decisions are made.
References
IBM (2023), The Cost of Delayed Data
McKinsey & Company (2019), Decision Making in the Age of Urgency