AI-Augmented Analytics: From Data To Decisions In The GenAI Era

Most managers operate in a world of numbers – spreadsheets, dashboards, data, and operational metrics. Generative AI is now changing how this everyday analytical work gets done: accelerating analysis, automating reporting, and opening previously inaccessible data sources. Managers need to harness these shifts in their day-to-day numerical work, and to extend their analytical reach into textual and other unstructured data where it adds value. Equally important, they must learn where AI-augmented analytics excels and where it fails silently, so that AI-generated outputs translate into trustworthy business decisions rather than plausiblebut-wrong narratives. This programme responds directly to that current need..

The New Analytics Paradigm:

From spreadsheets and dashboards to AI-augmented insight; the evolving analytics workflow; where AI excels and where it fails.

AI-Augmented Analysis of Structured Data:

Natural-language interfaces to data, AI copilots, automated EDA, prompting for analytical tasks, and checking AI errors.

Going Deeper with Numerical Data:

AI-assisted forecasting, anomaly detection, scenario/what-if modelling, segmentation, and automated reporting.

Textual Data Analytics for Managers:

Classification, extraction, summarisation, sentiment and theme discovery on customer feedback, surveys, and transcripts.

Documents, Images, and the Multimodal Frontier:

Document intelligence, chart/image analysis, embeddings and semantic search, and choosing the right approach.

From AI Insights to Trusted Decisions:

Verification frameworks, correlation vs causation, AI-amplified biases, risk-tiering, and human-in-the-loop workflows.

Building the AI-Augmented Analytics Function:

The new analytics stack, build vs buy, team and skills, data privacy and IP, governance, and ROI measurement.

Capstone (Bringing It Together):

Participant presentations on real data problems, peer/faculty feedback.