Workshop contents

Session 1 · Layer 3

Workflows and Tools

From talking to working

The AI stops answering and starts working. It now has access to tools: code execution, charting, document generation.

Step 3.1 — Multi-step workflow, live demo

PromptClick the text, then ⌘C or Ctrl+C
WORKFLOW:
1. Query the DataCo dataset: pull all order lines flagged as late delivery.
   Drop any rows with null values in the target columns.
2. Segment by: region, shipping mode, customer segment, product category
3. Generate a visual dashboard with 4 charts. Use a consistent corporate
   colour palette (navy and gold) and label every chart axis clearly:
   - Late delivery rate by region (bar chart)
   - Shipping mode vs delay days (box plot)
   - Profit associated with late deliveries by category (treemap)
   - Trend: late delivery rate over time (line chart)
4. Identify the three strongest combinations associated with late
   delivery (e.g. Standard Class + West of USA + a specific category)
   and state what additional evidence would be required to establish
   causation
5. Draft an email to the VP of Logistics summarising findings with
   the dashboard attached

What happens. The AI produces analysis, four visualisations, a pattern assessment, and a draft email to the VP of Logistics — in one uninterrupted flow.

Facilitator asks the room

That took the AI sixty seconds. How long would it take your team?

Lesson. AI with tools becomes a work multiplier. Without them, it can only talk about the work. With them, it does the work.

Step 3.2 — Tool awareness

Point out what the AI just used:

  • Code execution — ran Python or SQL over the dataset
  • Visualisation — generated the charts
  • Document generation — drafted the email
  • Data retrieval — queried specific subsets
Key takeaway

An AI model without tools is a consultant with no laptop. It can advise, but it cannot deliver.