Getting the Most Value from AI

Have you noticed how much the conversation around AI has changed?

Not long ago, the question was “Which model should we use?”

Now, I’m hearing a different question:

“Where will AI actually create the most value for our business?”

That’s a sign the conversation is evolving.

The technology will keep improving. Models will get better, faster and cheaper. But that’s becoming less interesting than knowing where to apply AI and how to do it responsibly.

In banking for example, not every problem deserves the biggest or most expensive model. A customer service interaction, a marketing campaign, fraud detection and credit underwriting all have very different requirements. Treating them the same rarely makes business sense.

That’s why I think we’re moving beyond AI strategy to something more practical: AI portfolio management.

Just as banks manage portfolios of products, investments and risk, they’ll increasingly need to manage a portfolio of AI capabilities - matching the right tool to the right job while balancing customer experience, governance, speed and cost.

For boards and executive teams, that shifts the discussion from technology to capital allocation.

The question moves beyond “Can AI do this?” to:

“Is this the best use of AI, and will it create measurable value for customers, employees and shareholders?”

The organizations that optimize the impact of AI, while managing risk will be the ones that figure out how to make better decisions about where to invest.

Are your AI conversations still centred on the technology, or have they moved toward business value measurement and investment decisions?