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AI in production: the operating model between pilot and scale

AI in production: the operating model between pilot and scale

AI pilots are now common across financial institutions and energy-market participants. The harder question is whether those pilots can run safely and reliably inside live workflows, with clear ownership, trusted data, approved controls, monitoring, evidence, support and measurable business value.

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Summary

Many institutions have proved that AI can generate useful outputs. Fewer have proved that AI can operate as a dependable production capability.

The barrier is rarely the model alone. It is the operating model around the model: who owns it, what data it uses, how outputs are reviewed, how controls are evidenced, how performance is monitored, how incidents are managed, and how benefits are measured after go-live.

This paper sets out a practical path from experimentation to controlled production. It is written for leaders who need AI to move beyond pilots and into live operation without creating unmanaged risk.

What this paper covers

  • Why AI pilots stall in regulated institutions.
  • What changes when AI moves from pilot into production.
  • The five stages from proof of concept to scaled capability.
  • The ten domains of AI production readiness.
  • Which use cases are most likely to scale.
  • Which use cases need stronger governance.
  • What regulators now expect in the UK, Australia, the EU and the US.
  • How to design the operating model, ownership, controls and benefits case.
  • How Capmark helps clients move from pilot activity to controlled production capability.

Who should read this

This paper is for COOs, CIOs, CTOs, CDOs, Heads of Transformation, Heads of Operations, Risk leaders, Compliance leaders, business sponsors and technology leaders in banks, asset managers, wealth firms, superannuation and pension funds, insurers and energy-market participants. It is especially relevant for institutions that have already launched AI pilots but are struggling to move them into live, supportable and controlled operation.

Capmark perspective

AI value lands in workflows, not in demonstrations.

A pilot may show that a model can summarise a document, triage a case, generate code, support a compliance analyst or answer a knowledge query. Production requires more. It requires a controlled service that is owned by the business, supported by technology, trusted by risk and compliance, adopted by users and measured against a real business outcome.

Capmark helps clients bridge the gap between strategy and implementation. We work with senior leaders to prioritise the right AI use cases, design the operating model, build the governance and control environment, integrate AI into real workflows, lead delivery, support adoption and transition the capability into business-as-usual operation.