In an age where AI is often synonymous with machine learning, one of the most powerful, but often overlooked, tools in the AI toolbox is optimisation. At the heart of many real-world, high-stakes decisions lies a mathematical engine built to deliver the best possible outcome. And IBM ILOG CPLEX continues to be that engine of choice.
As we move deeper into 2025, one hot trend is hybrid AI, the combination of predictive models with prescriptive optimisation. Why predict what might happen if you can also decide what should happen? That’s where CPLEX shines.
Whether it’s dynamically routing fleets, allocating resources under uncertainty, or scheduling energy consumption during peak hours, organisations are leveraging CPLEX not just as a solver, but as a strategic decision engine.
Here are a few standout use cases:
Retail & E-Commerce: Predicting customer demand using ML, then using CPLEX to optimise fulfilment across a decentralised warehouse network.
Utilities: Combining real-time sensor data with CPLEX-based scheduling to balance load in smart grid systems.
Finance: Creating portfolio allocations that meet regulatory requirements and maximise return, all while adapting to market volatility.
Another major shift? Optimisation in the cloud. IBM's Cloud Pak for Data is helping companies operationalise CPLEX models in ways that were unimaginable just a few years ago. Think seamless integration with data lakes, real-time dashboards, and API-first deployment models.
As business environments grow more complex, the ability to make data-driven, optimal decisions in real-time becomes a true competitive advantage. CPLEX brings mathematical certainty to uncertain times, and when combined with machine learning, it offers a full-spectrum AI approach that’s both predictive and prescriptive.
Are you exploring optimization as part of your AI strategy? Let’s connect, happy to exchange thoughts on where prescriptive analytics is heading next.
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