AI Strategy & Product
The hardest part of AI is not building models — it is deciding what to build, when, and how to measure whether it worked. This section covers the strategic and product management layer: how to identify and prioritise AI opportunities, make build-vs-buy decisions, roadmap AI products, measure ROI, manage the workforce impact, and understand the economics of open-source AI.
In This Section
Use Case Discovery & Prioritization
How to find, evaluate, and rank AI opportunities against business outcomes.
Build vs Buy vs Fine-tune
A decision framework for choosing between SaaS, APIs, fine-tuning, and custom model development.
AI Product Roadmapping
From idea to shipped AI feature — how to structure an AI product roadmap that accounts for uncertainty.
AI ROI & Value Measurement
Metrics, baselines, and business case structures for proving the value of AI investments.
AI Workforce Impact & Change Management
Role changes, skill shifts, reskilling strategies, and how to manage AI adoption across an organisation.
Open Source AI & Economics
Inference cost trends, model commoditization, and what the open-weight movement means for builders and buyers.