Six Self Limiting Myths About GenAI BUSTED!
The first bank to turn GenAI into a profit center will reap amazing rewards!
BCG goes “myth-busting” by looking at the top six GenAI myths that have some stuck in a self-limiting doom loop that assures them GenAI can never work for them!
Kudos to BCG for one of the more useful papers on GenAI in a while, and I hope it helps bust some self-limiting myths.
My favorite them all is unsurprisingly the first: “GenAI is mostly a cost-cutting tool.”
With all that is happening with Agentic systems, we are entering a new world of human-machine interactions that go far beyond cost-cutting.
These agentic systems will be the products themselves, whether they help us manage money, pay, or choose the right car to drive. Whatever they do, they won’t always be free, and we should consider them as potential profit centers.
GenAI may save costs, but focusing on that element is self-limiting because it makes us think of what is, rather than what could be.
This isn’t the first time limiting beliefs confounded banks using new technology.
In the early days of WeChat mini-programs, banks had no idea what to do with them, so they turned them into glorified advertisements. They were costs, not profit centers.
That didn’t last long. Within a few years, they were selling credit cards and managing assets on them, turning them into profit centers. They cracked the code!
AI will follow a similar trajectory. At first, no one knows what to do with it, but the first bank to crack the code and turn it into a profit center will reap tremendous rewards!
Are you willing to try?
👉THE SIX GENAI MYTHS:
Myth 1: GenAI is mostly about process effectiveness and is mainly a cost-cutting tool.
Myth 2: GenAI applications are limited due to a requirement in banking for transparent and predictable decisions.
Myth 3: The risk of so-called AI hallucinations and uncontrollable outputs undermines customer-facing solutions.
Myth 4: GenAI software should be bought off-the-shelf.
Myth 5: GenAI is an extension of machine learning/ predictive AI and therefore brings similar implementation challenges.
Myth 6: Data privacy and data residency regulations limit GenAI’s potential in banking.
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