Digital banking is one of many industries being revolutionized by generative AI (gen AI) and tackling the nuanced challenges brought on by the technology. Amid rapid implementation, banks and other financial institutions eager to capture the full value of gen AI must consider the capabilities of their operating model, or the blueprint for how a business puts strategy into action.
The right operating model should both enable scaling and align with the company’s organizational structure and culture, but how centralized should it be? For several reasons, McKinsey’s Kevin Buehler, Larry Lerner, and coauthors say that a high degree of centralization is best: “About 70 percent of banks and other institutions with highly centralized gen AI operating models have progressed to putting gen AI use cases into production, compared with only about 30 percent of those with a fully decentralized approach.”
Read the rest of the article for a 10-item checklist of essential decisions to consider when choosing and implementing a gen AI operating model, and don’t forget to check out QuantumBlack, McKinsey’s AI arm. Then, explore other insights for more on digital banking, including themes discussed by more than 280 financial executives attending McKinsey’s recent 21st Global Digital Banking Conference in Barcelona.
Scaling gen AI in banking: Choosing the best operating model
The changing landscape for banks
Capturing the full value of generative AI in banking
Been there, doing that: How corporate and investment banks are tackling gen AI
How generative AI can help banks manage risk and compliance
After the surge: Tien‑Tsin Huang on the future of payments
Beyond digital transformations: Modernizing core technology for the AI bank of the future
The economic potential of generative AI: The next productivity frontier
The rapid evolution of payments in Latin America
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A live take from McKinsey’s 21st Global Digital Banking Conference: Here come the incumbents
One year in: Lessons learned in scaling up generative AI for financial services