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Readiness is judged by opinion, so gaps surface late and plans are lists with no clear order.

Business cases are slow to build, hard to compare and slow to get approved.

Shortfalls are spotted only when workloads slow down, then options are found from scratch.

Choices default to one vendor, and lock-in quietly limits future choice and bargaining power.

Cost and speed get reviewed now and then, so savings are missed in between and commitments are over- or under-bought.

Peaks cause delays, or we over-buy capacity that sits unused most of the year.

Relying on one supplier leaves us exposed on price and availability, and juggling several feels hard to manage.

AI workloads often have no recovery plan, so outages last longer than the business can bear.

Physical limits block AI growth after the money has already been spent.

Committed or owned capacity sits idle, and heat from AI hardware is a cost instead of an asset.

AI energy and carbon go unmeasured, putting sustainability and ESG targets at risk.

We pay for space, power and connectivity across our sites that sits unused while AI work queues up elsewhere.

When the board, an auditor or a regulator asks, the evidence cannot be found, and mistakes repeat.

Outputs are scattered across email attachments, and reports are built by hand and out of date on arrival.

Data requests are unclear and updates are manual, so projects drag and information goes stale.