When I first started working the Middle East and Africa territory, market research meant weeks of work.
Country-level data scattered across trade reports, government publications, and industry databases that were either out of date or behind a paywall. Competitive intelligence that required actual conversations with people on the ground. Trend analysis that I was essentially doing by hand — reading, cross-referencing, synthesizing — and then presenting to leadership in a format that made it all look effortless.
It was not effortless. It was a grind. And I was never fully confident I had gotten it right.
That started to change when I genuinely incorporated AI into the process. Not as a shortcut — I want to be clear about that — but as a research and structuring partner that compressed weeks of work into something I could actually complete in a realistic timeframe.
Why the Middle East Market Is Particularly Hard to Research
The MEA region is not one market. It is twenty-plus markets with meaningfully different regulatory environments, purchasing behaviors, distributor relationships, and economic conditions. What works in the UAE does not automatically translate to Saudi Arabia. What is true about Egypt today was different three years ago and will be different again in three years.
Standard market research frameworks tend to flatten all of this. You get a regional overview that is technically accurate and practically useless. The nuance — which specific markets are moving, which distributors have real pull, which price points are realistic given current conditions — lives in the details that generic reports do not capture.
This is where AI has made the biggest difference for me. Not in generating reports, but in helping me think about the right questions to ask and then organize what I find.
How I Actually Use AI for Market Research Now
The process I have landed on goes roughly like this.
I start by building what I think of as a country brief — a structured summary of everything relevant I know or can find about a specific market. GDP trajectory, healthcare or dental spend as a percentage of GDP, regulatory environment for medical devices, key distributor relationships, competitive landscape, and any recent developments that affect our positioning.
I pull this from multiple sources — industry reports, news, conversations with local partners, our own sales data. It is a mix of structured and unstructured information, and the raw version is always messy.
Then I bring Claude in. I paste the raw material and ask it to help me structure the analysis — not to generate the analysis from nothing, but to organize what I have into something coherent. I ask it to identify gaps: what do I know, what do I not know, and what do I need to find out before I can make a recommendation?
This is the step that changed my research process most significantly. Having a tool that can look at messy raw material and tell me where the holes are is enormously valuable. The gaps it identifies are usually the right gaps — the things that, if I do not address them, will be the first questions leadership asks.
Building the Strategy From the Research
Once the country brief is solid, the strategy conversation with AI becomes genuinely useful.
I describe the market position — where we are now, what the competitive dynamics look like, what our realistic growth ceiling is — and then I use Claude to stress-test the strategy I have in mind. Not to generate a strategy from scratch, but to push back on the one I am already developing.
“What are the three most likely reasons this approach fails in this specific market?” That question, consistently applied, has caught more strategic weak points before a leadership presentation than anything else I have tried.
The output is not a finished strategy. It is a better-developed starting point that I then refine through conversations with our regional partners and my own judgment about what will actually work on the ground. AI does not know the specific distributor relationship that changes the equation in Qatar. I do. The combination is what produces something worth presenting.
What This Looks Like In Practice — The Saudi Arabia Example
Saudi Arabia is a market we have been trying to crack for a while. Complex regulatory environment, strong local preferences, several established competitors with deep distributor relationships.
The old research process would have taken me three weeks to produce something presentation-ready. A country brief, competitive analysis, channel strategy, and a recommendation on where to focus first.
With AI as a research partner, I did it in four days. Not because the AI did the work — it did not — but because it helped me stay organized, identified what I was missing before I wasted time going in the wrong direction, and helped me pressure-test the strategy before I put it in front of leadership.
The presentation landed well. More importantly, the strategy held up when our regional partner pushed back on it, which is usually where these things fall apart.
The Honest Limitations
AI does not know what happened in that market last month. It does not know the specific dynamics of a distributor relationship that has been cultivated over ten years. It cannot replace the phone call with the regional manager who tells you something that changes everything.
What it can do is make the structured parts of the research process significantly faster and more rigorous — so that you have more time and mental bandwidth for the parts that actually require human judgment and relationships.
That trade-off, in my experience, is worth a lot.