While making decisions is a natural part of most jobs, the time they take out of stakeholders' days can quickly add up. In a survey by McKinsey & Company, more than half of the respondents reported spending at least 30% of their working time on decision-making, while over one-quarter spent a majority of their time making decisions.

Regardless of their scale, business decisions often take time, effort, and a lot of back-and-forth discussion to reach any sort of actionable conclusion. Whether it be determining the best plan for a future situation — e.g. optimizing next week's production schedule at an industrial plant — or analyzing "what-if" scenarios to compare potential outcomes, solving business problems can easily become complex and arduous for an organization's key decision-makers.

Any means of streamlining this process and getting from complex problems to optimal solutions more efficiently and reliably is key. How can organizations optimize their decision-making to save time and reduce excess effort from those involved?

Decision-Making Starts with Predictions

Given their capacity to automate data collection and analysis, modern artificial intelligence (AI) solutions are becoming an increasingly important part of organizations' decision-making processes. And for good reason — these predictive tools are powerful solutions for forecasting outcomes based on historical data, expediting tedious data analysis and providing more actionable predictive insights in less time.

Research by Gartner found that 79% of corporate strategists see AI as critical to their capacity to make more efficient and insightful decisions, while additional analysis found that 43% of CEOs are already using Generative AI to inform their strategic decision making. Similarly, Predictive Analytics techniques such as Machine Learning (ML) and simulation are used across disciplines, from fraud detection in financial services to predictive maintenance in manufacturing.

These predictive technologies are the first piece in the decision-making puzzle. They help decision-makers shift focus from rudimentary data collection and management to completing more meaningful and insightful data analysis — reviewing past data in order to predict future outcomes — much more efficiently.

How Far Can Prediction Get Organizations?

Prediction alone, however, does not guarantee optimal decision-making. ML models typically assess individual decisions independently and can determine likely outcomes, but they do not inherently account for critical global constraints such as budget limitations, resource allocation, or regulatory requirements. They may be capable of assessing data and predicting an answer for a specific scenario, but they lack the capacity to handle more complex, multi-decision scenarios.

Imagine, for example, that an analyst is trying to determine the most cost-efficient way to manufacture and ship a new product line for their business. They could use an ML model to analyze things like past production, labor, market, or shipping data in order to forecast demand, predict costs, or estimate delivery times.

Even so, they can't focus solely on any of these factors without inadvertently impacting the others. An ML model might predict the cheapest factory based on cost, but it won't be able to account for that facility's maximum production capacity, minimum order quantities, transportation availability, or shipping times. These kinds of complex constraints require analysts to examine a larger number of predictions from the ML model, likely negating much of the efficiency promised by automation.

The Role of Prescriptive Decision Optimization

In any set of decisions that is subject to various constraints and the weighing of multiple performance indicators, analysts need to maximize some outcome while minimizing drawbacks. They need more than prediction-based decision-making — they need the prescriptive power of decision optimization.

Optimal decision-making requires an approach that integrates predictive capabilities with prescriptive methods. While predictive tools answer the question, "What is likely to happen if we choose this option," they do not answer the question "Given our constraints, what is the best possible combination of decisions we can make?"

For this, optimization techniques like mathematical optimization (MO) — more specifically linear or integer programming — are essential. These methods consider multiple decisions simultaneously, ensuring that overall objectives like maximizing profit or minimizing costs are met while respecting all necessary constraints. They are also becoming increasingly popular in business contexts, with over half (53%) those surveyed in a Gurobi report indicating that MO was gaining traction with decision-makers in their organization.

Returning to our manufacturing example, an analyst could take the various factors involved — production capacity, labor availability, shipping times and locations — and translate them into mathematical values. After feeding all of these interconnected factors into a MO algorithm, they would receive a list of prescriptive decisions that account for their impact on each part of the manufacturing process. This enables organizations to account for the various factors that might hinder an ML model and derive prescriptive insights that stakeholders can act on right away.

Even if factors were to change on the fly, it would be much easier to adapt to their impact. For example, a sudden labor shortage could completely throw off a manufacturer's production capacity. Instead of spending hours assessing the various predicted outcomes against one another, the manufacturer could adjust their MO model and quickly assess the resulting prescribed decision.

Conclusion

Any organization leveraging predictive technologies like AI and ML to enhance their decision-making processes is likely already on the path towards implementing more robust decision optimization. All decision makers need to do is take the step from prediction to prescription, leveraging methods like MO to assess complex questions and determine optimal results without requiring lengthy problem-solving timelines.

By combining predictive analytics with advanced optimization strategies, organizations can make complex decisions much more approachable, addressable, and far less time consuming.