Decision intelligence: moving from prediction to action

The issue is rarely a lack of information. More often, it is deciding what to do when objectives compete, resources are constrained, and uncertainty is unavoidable. The potential variables and possible outcomes are too vast and complex to rely on intuition alone.

This is where decision intelligence becomes valuable.

At its core, decision intelligence provides a structured way to act more effectively when complexity makes human capabilities alone insufficient. It combines data, mathematical modelling, optimisation, and domain expertise to evaluate trade-offs and identify actions that are not only effective in theory, but practical to implement.

The distinction matters because insight alone rarely changes outcomes.

Many organisations have invested heavily in AI and predictive analytics to understand what may happen next, with 78% of organisations reporting AI use in at least one business function in 2024, up from 55% the previous year. Forecasting demand, predicting equipment degradation, or estimating disruption risk can all improve visibility. Yet prediction is only one part of the problem. 

Knowing what may happen does not automatically tell you what should be done about it.

This is where many initiatives fail to create lasting value. Predictions are mistaken for decisions, and technically strong outputs struggle to translate into action because the harder question remains unanswered:

What is the best course of action within real-world constraints?

Consider electricity demand across a typical day. Some generators are low-cost but slow to respond. Others are flexible but more expensive. Some are weather dependent. Forecasting demand matters, but the harder task is determining the best mix of generation while balancing cost, reliability, resilience, and operational limits. Testing every possible combination manually is impractical.

Decision intelligence helps close this gap. Rather than relying on intuition, or trial and error, it evaluates trade-offs systematically and identifies the best available course of action within defined boundaries.

In practice, value is created not simply by knowing more, but by acting better.

AI and optimisation are often discussed interchangeably; actually they are distinct but can be combined in practice. For instance:

Predictive AI models estimate what may happen, while optimisation determines the best course of action given those insights. For example, forecasts may estimate electricity demand or the likelihood of asset failure; optimisation determines how to respond, selecting actions that respect operational constraints and deliver the best overall outcome.

On the other hand, AI can help generate candidate solutions that can be used to warm-start optimisation solvers, improving convergence and reducing solve times.

Used together, they provide insight and enable decision-makers to act.

AI can be used alone to find solutions to optimisation problems but remains prone to inaccuracies, often leading to the creation of infeasible solutions. This lack of reliability becomes a major risk in highly regulated or safety-critical environments where incorrect solutions can lead to severe consequences. Decision-making processes must therefore be based on methods that explicitly enforce constraints.

Furthermore, decisions must remain explainable, aligned to operational requirements, and open to scrutiny. Optimisation is particularly valuable because constraints can be explicitly defined and respected within the final recommendation.

This distinction matters. Organisations often invest heavily in prediction while underestimating the difficulty of operational decision-making.

The impact of decision intelligence becomes most clear in environments where vital strategic choices must be made effectively, efficiently, and under constraint.

For satellite operators such as Inmarsat, bandwidth is finite and demand constantly shifts. The challenge is deciding how to allocate limited capacity efficiently while maintaining performance across a complex communications network. Smith Institute developed optimisation algorithms that increased bandwidth efficiency by up to 40%, demonstrating how decision intelligence can improve outcomes where resources are constrained and trade-offs unavoidable.

In energy systems, however, the challenge is often not simply to identify the most efficient option but to build confidence in decisions where reliability cannot be compromised. For NESO, Smith Institute modernised the dispatch and reserve setting algorithms used for balancing generation and demand whilst maintaining sufficient reserve generation. Crucially, these recommendations needed to be explainable and robust enough to support operators in a safety-critical environment.

These examples point to a broader pattern: decision intelligence creates value where complexity, uncertainty, and competing priorities mean traditional approaches are insufficient.