AI spending is expected to more than double by 2027, according to the International Monetary Fund citing IDC data¹. Yet many financial institutions still struggle to turn ambition into execution.

In this article from A&M’s Financial Services Industry Group, we examine common pitfalls in AI implementation and share how firms can drive real ROI through a focused, coordinated approach.

Some of the key challenges we've identified include:

1. Bad/poorly understood data or data limitations
2. Legacy operating models and processes
3. Unclear impact on organization and role design

To fully realize the potential of AI, financial institutions must address these barriers with alignment across business functions. It is also essential for firms to identify and tackle specific use cases to maximize value rather than adopt a “pie in the sky” view.

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