Why most AI initiatives fail before the first line of code is written

“AI initiatives tend to collapse at the point where data meets reality.” — Cassie Kozyrkov

Organisations rarely fail at AI because of weak algorithms or inadequate platforms. They fail because they commit to AI delivery before establishing whether their data, governance, and operating model are actually fit to support it.

Artificial Intelligence is now firmly on the board agenda. Executives are approving AI strategies, funding pilots, and demanding measurable returns. Yet despite this focus, most AI initiatives continue to fail at scale. The reason is not weak algorithms, immature tools, or lack of ambition. It is far more fundamental.

Boards are approving AI investment without first approving AI data readiness.

This is not a technical oversight; it is a failure of governance.

Most organisations don’t fail at Artificial Intelligence because their models are weak.
They fail because trust collapses long before AI ever reaches scale.

When AI initiatives stall, are quietly shelved, or never move beyond pilots, the post-mortem usually focuses on technology: the wrong platform, the wrong data science approach, the wrong vendor. But this diagnosis consistently misses the real cause.

AI failure is not primarily a technology problem.

London has long been the global epicentre of management consulting. Strategy, transformation, and advisory firms sit at the heart of the City, embedded across financial services, insurance, government, infrastructure, and professional services.

Over the past five years, Artificial Intelligence has been positioned as the next major growth lever for this market. Practices were expanded, alliances announced, and AI positioned as a board-level imperative.

For decades, organisations have built Business Intelligence (BI) and reporting capabilities on data that was, at best, imperfect but tolerable.
Missing values were patched.
Inconsistent definitions were explained away in footnotes.
Manual reconciliations filled the gaps between systems.

And, largely, this worked.

But Artificial Intelligence has changed the rules.

For decades, organisations have built Business Intelligence (BI) and reporting capabilities on data that was, at best, imperfect but tolerable.
Missing values were patched.
Inconsistent definitions were explained away in footnotes.
Manual reconciliations filled the gaps between systems.

And, largely, this worked.

But Artificial Intelligence has changed the rules.

I am currently researching whilst ill in bed the future of AI and the pace of change towards the next major milestone in AI development “General AI”. The questions I am asking is what are the barriers to reaching this next goal and even if it’s a worthy goal in the first place.

The first one I have come across is “computational capacity”. Is this holding back what looks like the inevitable evolution of AI?

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