How to operationalize AI in finance

Why it matters

As much as 88% of AI proof of concept projects never reach production, often due to unclear objectives, weak data foundations, and limited in-house expertise. Meanwhile, McKinsey finds that, while 90% of companies have launched digital or AI transformations, only about one-third of the expected revenue benefits have actually been realized.

The gap isn't technology – it's operationalization. Finance teams that succeed know how to select use cases strategically, prepare their data infrastructure, enable experimentation within clear guardrails, and scale what works while killing what doesn't.

What's inside

• Identify where AI belongs in finance: Learn the three scenarios where AI creates real value – including high-volume processes with variable inputs, multi-system synthesis that demands speed, and judgment calls with recognizable patterns – and where traditional automation or human judgment makes more sense.
• Build foundations that support scale: Understand how to evaluate data readiness, make smart build-vs-buy decisions, and set success criteria that tie AI investments directly to measurable business outcomes like forecast accuracy and faster close times.
• Enable your team to experiment and iterate: Get a framework for structuring cross-functional pods, building ownership instead of just technical capacity, and creating channels for continuous improvement that turn individual wins into organizational capabilities.
• Scale from pilots to production: Follow proven practices for designing reusable infrastructure, automating model monitoring with MLOps, and investing in the training and process redesign that determine whether AI adoption actually sticks.