AI Strategy
Making defensible investment decisions about AI.
What does ai strategy cover?
Deciding what to automate, in what order, with what budget — and how to prove it worked.
Sub-topics
Governance & Risk
Policy, oversight, data protection and audit trails for AI systems operating inside a business.
Models & Providers
Comparing foundation models, providers, pricing and capability trade-offs for business workloads.
Definitions in this cluster
Return on investment
Return on investment for AI automation is the net benefit — labour hours recovered, revenue gained, error cost avoided…
Total cost of ownership
Total cost of ownership is the full lifetime cost of an AI system: implementation, model and infrastructure spend…
Build vs buy
Build vs buy is the decision between developing an AI capability in-house and licensing an existing product. The…
Proof of concept
A proof of concept is a time-boxed build that tests whether an AI approach can meet a defined success threshold on real…
Guides & analysis
An ROI Framework for AI Automation That Survives the CFO
A four-part model for evaluating AI automation investments — payback, unit economics, risk-adjusted return, and organisational cost.
Build vs Buy for AI: A Decision Tree
When to build custom AI, when to buy a platform, and when to wait — with a decision tree grounded in maturity, moat, and margin.
A Practical AI Governance Checklist for Mid-Market Companies
A pragmatic AI governance checklist covering policy, data, evaluation, incident response and vendor management — without the enterprise theatre.
Choosing an LLM for Your Business: GPT vs Gemini vs Claude in 2026
A candid comparison of the leading LLMs for business use — tool-use, latency, multilingual quality, cost, and where each one loses.