Introduction
Artificial intelligence was the talk of the American Banker Digital Banking conference held this week in Boca Raton, Florida. The buzz is easy to understand, given its potential to boost productivity and lift so many elements of banking today to a new level, particularly in the context of several sectoral headwinds.
Challenges in AI Deployment
For CIOs and C-suites thinking about digital and AI, the well-known challenges of deploying AI (e.g., risks like hallucinations and bias, and the difficulty of scaling pilots) are compounded by three major factors: the need to demonstrate a return on investment, or ROI, on past technology investments; the need to differentiate the bank from competitors; and the need to achieve success in their existing transformation efforts.
Current Industry Performance
To date, our industry’s record at these is uneven. Demonstrating the ROI on tech investments is not simple — indeed, for the industry, higher revenue remains very strongly correlated with more manual work. If tech were truly resulting in automation, we should be seeing significant returns to scale, but these appear absent from the data. Factor in that a lot of the spending has been (justifiably) on infrastructure modernization and risk management which does not necessarily generate revenue.
Key Insights
A key insight that our research has found is that capturing value from technology and AI requires taking actions beyond just those domains. For example, in surveys we have conducted, 60% of executives cite skill gaps as an obstacle that they have had to address in their digital transformations, and 70% say they faced fundamental resistance to change.
Future Outlook
Against this background, AI now looms ever larger for banks. But to capture any meaningful value from AI, the actions need to reach much further than just building sophisticated models. For example, at some institutions, even the process of validating machine learning or AI models can stretch to as long as two years. While there are often good reasons for this duration, in many cases relooking at these processes can compress the time taken while preserving the risk management (and sometimes even enhancing it).
Critical Questions
To that end, we see three critical sets of questions for banking leaders as they head home from this week’s gathering in Florida:
First, can you objectively identify areas where tech/AI can generate the most business value in your context (e.g., reducing risk, introducing new revenue, cutting cost)?
Second, are you materially reallocating your spending toward those areas or are you being incremental and ‘peanut buttering’ investments — this includes funding the required changes in enabling functions like data, or risk management and compliance?
Third, beyond the tech or AI model deliverable, do you have a change management formula in place that reaches ‘beyond the CIOs office’ and benefits from your past lessons from other similar epochal programs?
While we have seen many ‘digital-native banks,’ the world has yet to see an ‘AI-native bank.’ To get there, banks will need to internalize the great lesson of the past — that ironically, the secret to successfully deploying any technology is never just technology.
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