Who it's for
Business analysts who want AI to speed up analysis without sacrificing rigor
What participants can do by the end
Identify the AI tools that augment data analysis workflows, natural language querying, automated exploratory data analysis, and predictive analytics, and map each to an analyst use case. Apply AI-assisted analysis tools to a GCC business dataset to identify patterns, outliers, and correlations relevant to a defined business question. Analyze the quality and completeness of a dataset before AI integration, identifying missing values, outliers, and structural issues that would distort AI outputs. Examine AI-generated analytical insights for accuracy, business relevance, and statistical validity before presenting them to a stakeholder. Build an AI-integrated analysis workflow for one recurring analytical task, specifying data inputs, AI tools, quality checks, and output format.
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 specifically for analysts:
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