88% of global organisations now deploy AI across at least one operational function, and boards no longer ask whether to fund it. [6]
Yet only 25% of organisations have carried more than 40% of their proof-of-concept projects into enterprise-grade production environments. [1]
The gap is not a model problem. It is an infrastructure problem.
Public Cloud environments that supported rapid experimentation were never built to carry the cost, latency, and compliance load of production-grade generative AI at enterprise volume. Closing the gap between sandbox and scale requires a deliberate move away from single-cloud dependency toward a managed, hybrid, and observable Cloud foundation, one built for sustained inference rather than short-lived pilots. This eBook sets out that foundation: the economic pressures driving it, the architecture that supports it, the financial governance it demands, and the enterprise evidence that already validates it.