McKinsey to Banks: Rewire First, Make Money With AI Later
Have you heard that one before?
McKinsey tells banks that if they want to “extract value” from AI, they will first need to rewire by reimagining complex workflows.
Pity poor bankers. Digital profits are always just over the crest of the next hill and consulting contract.
When digital hit hard over a decade ago, they were told they needed digital transformation. Then big data hit, and they were told the money would flow if they could eliminate silos.
Now, banks, all waiting for GenAI's promised big bucks, are told they must “reimagine complex workflows with multiagent systems.”
Bankers are notoriously gullible, but how long can McKinsey and so many others continue to claim that profits are coming soon?
McKinsey has also made a subtle bait-and-switch. GenAI is no longer making the banks money; it's “multiagent systems,” or agentic AI, which is far more complex.
To their credit, McKinsey isn’t entirely wrong.
I agree that banks need to reimagine the nature of work to understand how to best apply AI. And, of course, McKinsey’s building blocks, management vision, comprehensive AI stack, and AI enablers matter—how could they not?
The problem is that most banks lack McKinsey’s required building blocks to complete their blueprint, so a much better paper would show how banks can make money with AI even if they lack one or two!
Now that’s something more bankers would relate to and pay for.
👊WHAT BANKS EXCELLING AT AI DO WELL👊
🔹 Set a bold, bankwide vision for the value AI can create.
Leading banks have an expansive outlook on the role that AI can play, viewing the technology not just as a driver of cost efficiencies but also as a way to enhance revenues and significantly improve customer and employee experiences.
🔹 Root the transformation in business value by transforming entire domains, processes, and journeys rather than just deploying narrow use cases.
Banks that excel in AI resist the temptation to launch narrow use cases such as a chatbot or a conversational Q&A tool in isolation. Although these might be fast to launch and potentially low risk, in isolation, they won’t unlock material financial value.
🔹 Build a comprehensive stack of AI capabilities powered by multiagent systems.
Running complex banking workflows, such as evaluating a commercial customer’s loan application, involves highly variable steps and the processing of a mix of structured and unstructured data. While traditional automation cannot handle such tasks, gen-AI-enabled systems can.
🔹 Sustain and scale value by setting up critical enablers of the AI transformation.
These include cross-functional business, technology, and AI teams along with a central AI control tower that coordinates enterprise decisions across functions, drives governance and adoption of standardized risk guardrails, and promotes the reusability of AI capabilities.
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