Choosing a new core market is an expensive decision.
It can require localisation, payment infrastructure, local hiring, product changes, regulatory work and sustained acquisition investment. A decision at this scale needs stronger evidence than a list of large or familiar markets.
I built an AI-enabled market-selection system covering 200 markets, 25 commercial and operating variables and more than 14,500 internal and external source inputs.
Its purpose was to give leadership a structured way to compare opportunities, understand uncertainty and decide where deeper work should go next.
The starting point
The organisation already had substantial market knowledge. It was spread across a large global workbook, presentations, internal assessments, risk material and stakeholder preferences.
The material was valuable, but it had been assembled by several contributors using different methods. Scores could not always be reproduced, missing information was handled inconsistently and the evidence behind a market conclusion was difficult to review.
The decision needed to bring together regulation, compliance, payments, competition, positioning and wider commercial conditions. Each conclusion also needed a clear source.
Building the system with AI
I preserved the original internal material as a read-only reference and built a separate research and decision layer around it.
AI was used throughout the process to combine internal evidence with external sources, including government data, legislation, regulatory material, market studies, company information and competitor research.
It helped interpret the evidence, compare markets and apply consistent decision logic across the complete market set.
I developed a research protocol defining which sources should be prioritised, what counted as sufficient evidence, how each variable should be assessed and how missing information should be handled.
Every assessed factor followed a simple structure: result, score and source.
This made the logic visible. A manager could see the conclusion, the evidence supporting it and the rule used to reach it.
I tested the process on pilot markets before scaling it. I also tested lower-cost AI for the reasoning work, but its output did not meet the required quality level.
Higher-capability AI was therefore used for evidence interpretation. Predictable tasks such as moving data, updating the workbook and validating the results were handled through automation.
This division made the system both scalable and easier to control. AI handled the complex interpretation, while automation kept the underlying process consistent.
The audit that improved the model
A complete-looking workbook was only part of the objective. I also wanted to know whether the scores were genuinely supported by evidence.
An independent audit reviewed the source quality, formulas, internal data and a sample of market conclusions.
It found that some scores lacked sufficient support, some sources no longer worked and missing information could sometimes influence a total as if it represented an actual market outcome.
That finding changed the model.
I rebuilt the priority-market analysis with stricter evidence rules. Missing information remained blank, unsupported scores were removed and comparable totals were calculated only from variables supported across the full comparison group.
The revised analysis contained fewer numeric scores, but the evidence behind them was stronger. Uncertainty became visible instead of being converted into artificial precision.
This was an important improvement. A market should score well because the evidence supports it, rather than because its information happened to be easier to find.
The practical value
The finished system is a repeatable screening and decision-support process.
It gives leadership a common way to compare markets, inspect the evidence, identify gaps and challenge assumptions before committing substantial resources.
The original internal knowledge remains protected. Research can be updated without rebuilding the entire analysis, and every important conclusion can be traced back to its source.
The system also creates a clear division between AI-supported analysis and executive judgement.
AI can collect, combine and interpret a very large evidence base. Leadership can then apply commercial priorities, risk tolerance and strategic judgement with a clearer view of the available facts.
The result is a transparent and reusable market-selection process. It already supports executive discussions, focuses deeper research on the strongest opportunities and makes uncertainty visible inside the decision.
How much more value can it create?
The next stage is to apply the stronger evidence standard across more priority markets and add commercial information that changes frequently, including customer value, acquisition economics and competitor investment.
The system can also become a continuously updated market-intelligence capability.
Evidence can be refreshed as regulation, payment conditions, competition and market economics change. Alerts could show when an important source becomes outdated or a previous assumption needs to be reviewed.
Legal, payment and commercial specialists can validate the strongest candidates, while detailed business cases connect the research to hiring, localisation, product and acquisition plans.
Real market performance can eventually feed back into the system. This can improve future comparisons and show which variables are most useful when evaluating expansion opportunities.
This project shows how AI can turn fragmented research into governed decision support when the evidence standards are clear and the outputs remain auditable.
The system already gives leadership a stronger way to compare markets. Every update and real-world result makes that capability more valuable.