AI Prompt Engineering & Agentic AI for Business
Master prompt engineering and build autonomous AI agents that automate real business workflows.
The executive challenge
The problem this program solves
Your organisation has run ai prompt engineering pilots for two years. None has reached production, the pilot budget is now a permanent line item, and the board has started asking what it actually bought. Careers stall on the same failure: brilliant ai prompt engineering pilots, no adoption, no measurable value. This is where ai prompt engineering stops being a technology conversation and becomes an executive discipline you can defend in any boardroom.
Context
Why this matters now
Capability
What you will be able to do
- Evaluate where ai prompt engineering creates economic value across the organisation — and where it reliably does not.
- Read ai prompt engineering evidence — dashboards, model outputs, vendor claims — and know exactly what to trust.
- Construct a ai prompt engineering roadmap sequenced by value, feasibility and risk.
- Defend a ai prompt engineering investment case before a board that has been disappointed before.
The IPAT Course Standard
The five-day journey
Each day is a deliberate progression in managerial capability — from strategic context, to analytical frameworks, to applied practice, to executive judgement under pressure, to a capstone that produces something your organisation keeps.
IPAT Signature Case™
The AI Prompt Engineering Model Nobody Can Explain
A a national revenue authority deployed a ai prompt engineering model into daily operations. It performs — until a customer dispute, a journalist and the regulator arrive in the same week, and no one can reconstruct why the model decided what it decided.
Your decision: Pause the model pending an explainability rebuild, defend it as deployed, or restrict it to advisory use — each response signals something different to the regulator, the market and your own staff.
IPAT Data Lab™
A a fast-scaling e-commerce platform suspects its ai prompt engineering spending is aimed at the wrong customers — and the intuition cannot yet be defended in the boardroom.
Dataset: Synthetic ai prompt engineering panel for a fast-scaling e-commerce platform: the tables, formats and quality gaps your team would find on day one.
Your task: Structure the data, segment and score it, and translate the model into one operating decision about where the money should go.
A ai prompt engineering decision memo backed by an analysis you built and can defend.
Data → Analysis → Decision, not Data → Code → Certificate
Day 5 · The Capstone
What will you change when you return to work?
Build a twelve-month analytical and AI roadmap for your organisation: the value case, the data foundations, the governance, and the first 90 days.
Methodology
Frameworks you will master
- • IPAT Data-to-Decision Framework™
- • IPAT Executive Capability Model™
- • Value-versus-feasibility portfolio analysis
- • CRISP-DM analytical lifecycle
- • Model risk management principles (SR 11-7 adapted)
Cohorts
Upcoming cohorts
New dates are being scheduled
Register with your preferred start date and our admissions team will match you to the next available cohort — or contact us to arrange an in-house delivery for your organisation.
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