Summiting in AI: Insights from the Sharp End
Findings from Celent’s AI in Action Survey
“It's always further than it looks. It's always taller than it looks. And it's always harder than it looks.”
Reinhold Messner, one of the few people to have climbed all fourteen peaks above 8,000 meters
Like Messner, banks at the sharp end of AI innovation are reaching new summits. We surveyed these banks and found they are leveraging AI to realize not only step change, that is, an opportunity to take the lead but also game changing disruption, that is, an opportunity to generate a sustainable competitive lead.
- A striking 86% of respondents state that AI is very to extremely important to competitive differentiation
14% have greater than 10 AI projects underway in the Treasury Trade Services line of business
- This share nearly triples with 38% expecting to have over 10 projects underway over the next two years
The focus of AI initiatives has moved from primarily the middle and back office to the front office. Four use cases are tied for #1 in production: customer-facing intelligent virtual assistants (IVAs) and predictive analytics and employee-facing client analytics and payment fraud reduction. Four use cases stand out as priorities(not in production yet) customer-facing prescriptive analytics, business advice, and IVAs for onboarding, and employee-facing product pricing.
Realizing game changing competitive differentiation is akin to summiting an 8,000’er. Success rests upon several soft and hard key success factors (KSFs). It takes a highly dedicated and competent team. An effective, collaborative organizational dynamic, including employee buy-in, is paramount. Mastering the 4 Cs of data (capture, clean, cache, and call) is an ongoing exercise in commitment and perseverance. Even the banks at the sharp end have not fully accomplished all the KSFs. Of the five KSFs covered in our Survey:
42-47% state it is “work-in-progress”
- Only 1 out 10 have “achieved”
Like all mountaineering, it is extremely difficult to catch up in the AI quest if one falls behind. Machine learning models get smarter the more data they consume over time. At a minimum, banks should explore how AI can help them realize their overall digitization strategy and begin assessing how to fulfil the prerequisites for successful AI scaling. With most banks still at base camp, banks that start heading for camp 1 will likely have a sustainable lead.
Checking out the findings from our AI in Action Survey.