Skip to content

PlanningAI vs. Operations Research: Spotlight on optimization

Operations Research (OR) and PlanningAI share the same goal: to drive real-world impact through optimization. Both fields have proven their worth in real-world use cases, whether in logistics, scheduling, routing or resource allocation. But the way they achieve this impact is fundamentally different.

About PlanningAI

PlanningAI is set to disrupt the traditional OR space by delivering shorter time to value, lower implementation risk, and a pragmatic approach to building models to make it more accessible to a wider audience. Instead of requiring specialized OR or mathematical expertise, PlanningAI is developer-friendly, enabling engineers and IT teams to integrate planning models directly into enterprise systems.

The key differences

Time to Market: PlanningAI moves faster

OR solutions are often custom-built and require months, sometimes even years of modeling, validation, and refinement. PlanningAI, in contrast, is designed for speed. By using pre-built models and model templates, PlanningAI can be deployed in weeks, not months, dramatically reducing time to value.

Faster deployment means businesses can see results sooner, adjust quickly, and avoid the risk of sinking months into an optimization model before knowing if it works.

Proof of Value: Lower risk, faster ROI

PlanningAI allows for quick validation because of its fundamental approach of standardizing the model engineering process. Pre-built models cover 90% of features that have to be built from scratch in traditional OR projects. Because of this, businesses experience the ROI of PlanningAI projects way faster because of its shorter time-horizon to get into production.

Because OR projects have traditionally longer implementation cycles, they often carry greater risk. Businesses may not see the impact of their investment until much later, or worse, realize there is little or no ROI after implementation.

Code, not just math

One word: Accessibility. PlanningAI shifts the focus from math to code. This alone empowers an entire segment of practitioners that can tackle complex planning problems without having to model their problems in math equations. Furthermore, it integrates directly into enterprise software, allowing engineers and IT teams to manage and maintain solutions without requiring deep OR or mathematical expertise.

OR is mathematically complex to use and often requires specialized expertise (PhDs, operations researchers, and mathematicians). While this ensures rigorous, theoretically sound solutions, it also limits accessibility to a smaller group of experts. Many OR models remain in research or require dedicated teams for maintenance, making widespread adoption across business and IT functions more difficult. Without deep OR knowledge, many organizations struggle to implement and operationalize these models at scale.

The quest for optimality: Feasible vs. perfect

PlanningAI prioritizes feasibility and pragmatism. It focuses on finding a near-optimal solution fast rather than waiting for the absolute best answer. In dynamic business environments, where constraints shift and decisions must be made quickly, a good solution today often outweighs a perfect solution too late.

By optimizing for speed and adaptability, PlanningAI ensures businesses can act decisively rather than waiting on theoretical guarantees.

While not in every scenario, OR has a reputation for prioritizing proof of optimality, which means proving that a solution is the absolute best under given constraints. One can appreciate that this rigor is essential for academic and research settings.

But in many real-world scenarios, a perfect solution takes too long to compute or doesn’t hold up when conditions change. What is optimal in theory may not always be practical in execution, especially when business realities demand agility over perfection.

The takeaway

When speed, accessibility, and execution matter most, PlanningAI shines. When deep theoretical guarantees are required, OR remains essential. Understanding their differences allows businesses to choose the right technology for the right challenge.

Understanding their differences lies in recognizing which approach best aligns with your objectives and priorities and ensuring optimization is not just theoretical but also actionable and effective.