The £200 Million Question
In 2014, Amazon built one of the most sophisticated AI hiring tools in corporate history. It ingested a decade of CVs and trained itself to identify the patterns that led to successful hires. Four years later, they scrapped it entirely.
The problem? The model had been trained on CVs submitted over a decade when Amazon's workforce was predominantly male. It learned to penalise CVs that contained the word "women's" — as in "women's chess club" or "women's university". It downgraded graduates of all-female colleges. It quietly, systematically discriminated against women — and nobody noticed for four years.
The Real Cost of AI Failure
The Amazon story is one of the most famous AI failures in history — but it's not the most common type. Most AI projects don't fail because of hidden bias. They fail because nobody asked the most fundamental question before the project started: Should we actually be doing this?
The question isn't just "can we build it?" — it's "should we build it?", "are we ready to build it?", and "what happens if it goes wrong?" These questions sound obvious. They are rarely asked.
Most AI failures share a common origin story: a compelling demo, executive enthusiasm, a budget approval, and a project that moved too fast to ask hard questions. The decision to deploy AI is often made before anyone has mapped the risks. This section gives you the framework to change that.
What This Section Will Do
By the end of Section 1, you'll have the Five-Dimension Framework — a structured evaluation model for any AI use case. You'll be able to assess a proposed deployment against five dimensions that determine whether it should proceed, be redesigned, or be declined outright. You'll also apply the framework to real scenarios so you can use it confidently in your own organisation.
You'll leave Section 1 with a repeatable decision tool that can be applied to any AI proposal your organisation receives — helping you say yes, no, or "not yet" with documented rationale.
