There’s a question that’s been nagging at me for a while now: whatever happened to Operations Research (OR)? In the age of artificial intelligence (AI), OR seems to have quietly faded into the background, forgotten amid the deafening hype around machine learning, large language models, and generative AI. And yet, as I reflect on the very early stages of my journey back in the late ‘90s — earning a MSc in OR and working with a company designing AI solutions in Barcelona, Spain (still going strong — AIS Intelligencia Artificial) — it strikes me that, even back then, AI and OR were used interchangeably. Of course, the AI of the early 2000s was nowhere near what it is today, but the interplay between these two fields has always existed.
Having worked across both disciplines for years, I can’t help but notice how OR’s foundational role in optimization has become overshadowed by AI’s promise of prediction and personalization. But here’s the thing — OR hasn’t gone anywhere. It’s still quietly running the logistics, scheduling, inventory management, and supply chain optimization that keep businesses alive. The difference is that while AI dazzles with its ability to learn patterns and predict individual behaviors, OR focuses on optimizing systems and processes. In fact, without a solid foundation in OR, AI often becomes a glamorous but fragile layer — predicting demand without understanding operational constraints. And that, I believe, is a distinction we’re dangerously close to forgetting.
The Illusion of Progress
AI’s rise to prominence makes perfect sense: it’s exciting, adaptive, and promises insights that feel almost magical. In industries like hospitality, AI can optimize customer behavior, recommending products based on personalized preferences and even predicting demand at a granular level. That’s something OR traditionally approached through segmentation, missing the rich nuance AI now captures. But here’s where it gets interesting: optimizing customer behavior and optimizing business operations are two very different things.
Take this example: AI might tell a coffee chain to stock more blueberry muffins because sales are surging. But without OR, that recommendation doesn’t account for storage capacity, supplier lead times, or the risk of food waste. OR models the system constraints and builds optimal replenishment schedules. AI and OR together would ensure the right amount of muffins, at the right locations, with minimal waste — that’s optimization in action.
The Missing Chapter in African Business
This brings me to an observation I can’t shake: in Western markets, large corporates have been using OR for decades to quietly, consistently optimize their operations. AI is simply the new kid on the block — building on that solid foundation. But in many African businesses, it feels like we’re jumping straight to AI, bypassing OR entirely. The result could be a dangerous illusion of progress — businesses that appear digitally advanced but are operationally fragile.
When the underlying processes haven’t been optimized, AI becomes a thin layer of intelligence on top of inefficiency. You can’t just “AI your way” out of a disorganized supply chain, inefficient logistics, or poorly planned inventory. Without OR, you end up with fragmented insights and recommendations that are impractical to execute. In Africa, where many industries are still building foundational systems, skipping OR isn’t leapfrogging — it’s more like building a skyscraper on sand. OR is about systematic, fact-based optimization — the kind that can create stability and resilience in markets that often face volatility.
Getting the Balance Right
I’m not saying African businesses should slow down on AI adoption — far from it. AI offers unprecedented opportunities for growth and innovation. But I do believe that businesses here could benefit immensely from first (or simultaneously) applying OR principles to build robust, efficient operations. Optimization and prediction are two sides of the same coin. AI makes you smarter; OR makes you stronger.
In our race to adopt AI, let’s not forget the quieter, less glamorous art of optimization. The future of African corporates may depend on getting this balance right.