Workshop contents
Session 1 · Layer 2
Context Engineering
Giving it your business
Now we move from answering a question to understanding a business. The AI receives the same data, plus the company’s rules, targets, and constraints. Everything below that is not in the dataset is a controlled workshop assumption.
Step 2.1 — Add business context
COMPANY CONTEXT: - We are a B2B distributor serving corporate and home office segments - Our SLA commitment: delivery within scheduled shipping days - Board KPI: reduce late delivery rate from 54.8% to below 30% in 6 months - Current allocation by order lines: Standard Class 59.7%, Second Class 19.5%, First Class 15.4%, Same Day 5.4% - Budget constraint: logistics cost cannot increase more than 12% DATASET: [DataCo supply chain data attached] Given this context, analyse our late delivery problem and recommend a phased action plan that stays within budget. Prioritise by profit impact. Format the plan into three phases: Immediate (0-30 days), Short-Term (30-90 days), and Medium-Term. For each phase, explicitly state the expected budget impact against the 12% ceiling.
The 54.8% starting rate and the four allocation percentages come from the data Derived Evidence. The SLA, the board target and the budget ceiling are invented for the exercise Workshop Scenario.
What happens. The recommendations change completely. The AI will not propose shifting volume to premium shipping — it now knows both the budget ceiling and the fact that premium modes are the late ones. Expect it to move volume toward Standard Class, the cheapest and most punctual mode in this data, and to prioritise the segments named in the context.
Same data, same model, completely different recommendation — because it now understands the business. What other context would change the answer again?
Lesson. Context engineering is the practice of giving AI your company’s rules, KPIs, constraints, and priorities. This is where enterprise AI separates from consumer AI.
Step 2.2 — Add historical context, or memory
PREVIOUS ANALYSIS (Q1): - We shifted 15% of Second Class volume to Standard Class in West of USA - Result: late deliveries dropped 8% in West of USA but increased 3% in US Center - Root cause hypothesis: the Standard Class carrier has limited US Center capacity Given this history, update your recommendations. Do not repeat strategies that failed. Account for regional carrier capacity differences. Provide a "Revised Action" table contrasting the Q1 approach with the new approach for US Center.
What happens. The AI adjusts course. It keeps the direction the data supports, avoids the approach that failed in US Center, and proposes different tactics for different regions.
Lesson. Enterprise AI without memory will repeat the mistakes the organisation already paid to learn from.
Layer 2 debrief
Key takeawayAnyone can access the same AI model. Your competitive advantage is the context you give it — your rules, your constraints, your history.