Who
Should Attend
Business analysts who want AI to speed up analysis without sacrificing rigor.
What participants can do by the end
Identify AI tools that can support data analysis, ask questions in plain language, explore data automatically, and help predict trends, then match each tool to a practical business task. Use AI to analyze a GCC business dataset and identify useful patterns, unusual results, and relationships related to a specific business question. Check the quality and completeness of the data before using AI, including missing information, unusual values, and structural issues that could affect the results. Review AI-generated findings for accuracy, relevance, and reliability before presenting them to stakeholders. Build a practical AI-assisted workflow for one recurring analysis task, including the data needed, AI tools used, quality checks, and final output.
How the three days build
Day one covers natural language data querying without SQL, automated exploratory data analysis for pattern and correlation detection, and AI forecasting for business analysts, then works through framing the business question before touching the data, applying AI-assisted pattern detection, and running a full data quality audit for the missing values, duplicates, and format inconsistencies common in GCC data environments. Day two examines AI analytical errors directly, spurious correlation, overfit patterns, misleading aggregations, and how to validate AI insights against actual business logic, before covering how to communicate uncertainty and confidence levels to non-technical stakeholders and structure a recommendation as insight, so-what, and recommended action. Day three has participants design a repeatable AI-integrated workflow for one of their own recurring tasks, with quality gates specifying exactly where human review has to happen before output goes out.
The output is a workflow the analyst trusts enough to run every month, not just once.
Why this matters before anything else:
An AI tool that surfaces a correlation doesn’t know or care whether that correlation is meaningful, spurious, or the result of a data quality problem upstream, it will present all three with the same confident tone. Analysts who skip the quality-check step end up presenting insights that don’t survive a second look from a stakeholder who knows the business. This program is built so AI becomes a genuine speed advantage in analysis, without quietly lowering the bar for what counts as a validated insight.
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