Qwen for Business Intelligence Training in Dublin, Ireland

The Qwen for Business Intelligence Training is a practical, analytics-focused program that teaches how to use Qwen large language models to enhance Business Intelligence (BI), analytics, and decision-making workflows.
Participants learn how to apply Qwen for market analysis, reporting, data interpretation, insight generation, and executive-level intelligence, bridging traditional BI with modern Generative AI.

This course is ideal for BI professionals, analysts, consultants, data teams, and business leaders looking to adopt AI-driven intelligence.

Introduction to Qwen & AI-Driven BI

• Overview of Qwen LLM capabilities

• Evolution of BI with Generative AI

• Traditional BI vs AI-augmented BI

• Enterprise BI use cases with Qwe

BI Data Types & Intelligence Sources

• Structured data (tables, metrics, KPIs)

• Unstructured data (reports, news, documents)

• Internal vs external data sources

• Preparing data for AI-driven analysis

Prompt Engineering for Business Intelligence

• Designing prompts for analytical tasks

• Asking the right business questions

• Structured prompts for insights and KPIs

• Reducing bias and hallucinations in analysis

Market Research & Competitive Intelligence

• Market sizing and trend analysis

• Competitor benchmarking

• SWOT and PESTLE analysis using Qwen

• Synthesizing multi-source intelligence

AI-Powered Reporting & Dashboards

• Generating narrative insights from data

• Executive summaries and management reports

• KPI explanations and anomaly detection

• Supporting dashboards with AI insights

Financial & Performance Analysis

• Revenue, cost, and profitability analysis

• Variance and trend explanations

• Scenario and what-if analysis

• Decision-support insights

Customer & Sales Intelligence

• Customer segmentation analysis

• Voice-of-customer insights

• Sales pipeline and performance summaries

• Churn and retention intelligence

Operational & Strategic BI Use Cases

• Supply chain and operations insights

• Risk and opportunity identification

• Strategic planning support

• AI-assisted decision frameworks

Automation of BI Workflows

• Automating recurring analysis tasks

• AI-assisted data interpretation pipelines

• Integrating Qwen into BI tools and workflows

• Human-in-the-loop review patterns

Accuracy, Explainability & Trust

• Validating AI-generated insights

• Fact-checking and grounding strategies

• Explainable AI for business users

• Managing confidence and uncertainty

Security, Governance & Responsible AI

• Handling confidential business data

• Access control and compliance considerations

• Ethical use of AI in BI

• Enterprise governance models

Deployment & Adoption Strategy

• Rolling out AI-powered BI solutions

• Change management and user adoption

• Measuring ROI and business impact

• Scaling AI across BI teams

Hands-on Exercises

Summary and Conclusion