One of Australasia’s Largest Banks Unifies Their AI Efforts

Daniel Faggella

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders.

Unifying AI Initiatives and Focusing on Impact

The following is a case study for Emerj’s AI Opportunity Landscape research. To learn more about how we help companies develop winning AI strategies and identify the highest-ROI applications, watch the two-minute video summary of our AI Opportunity Landscape research.

Problem

The bank had many scattered AI projects, but struggled with:

  • Many AI efforts being duplicated
  • No strong AI foundation being built
  • No coherent, informed plan on what to invest in first

The client realized that they were wasting money and time on projects without investing properly in a few key projects that might genuinely be deployed or deliver value. 

By continuing to allow different departments to try to adopt AI on their own, the company was ill-prepared for the fundamental changes in data infrastructure and team dynamics that AI requires, and was unable to move their proof-of-concept ideas into production. 

Objective

In order to develop a coherent plan to organize their company-wide AI efforts, the bank wanted a strong understanding of where other top banks were investing, and on where near-term return on investment could be achieved.

Emerj Approach

Provided scored and sorted data and analysis on the vendor and banking enterprise AI landscape, with a focus on the seven largest US banks. Emerj simplified banking AI applications into business function categories (ie. customer service, fraud, cybersecurity), AI capabilities (conversational interfaces, prediction and forecasting, etc) – and by scoring both individual applications and application categories through Emerj’s proprietary AI Scorecards, including Ease of DeploymentEvidence of ROILevel of Adoption, and more.

Advised the company in three key areas:

  • First, identifying the areas of greatest near-term and long-term ROI in banking
  • Second, comparing known ROI areas with the client’s current data assets and key strategic initiatives in order to find areas of overlap
  • Third, we sorted current and near-term AI projects in a new priority order, factoring for near-term value, long-term value, and company resources and priorities

The company remains up to speed with the latest market developments through ongoing access to Emerj’s AI in Banking Opportunity Landscape service, and is now unifying its AI strategy towards areas of greatest promise.

Results

The bank was able to make quick, consistent, data-supported decisions about which AI initiatives to prioritize and which to scrap, with a focus on data infrastructure and capability building. The bank uses Emerj’s AI in Banking Opportunity Landscape as a framework to identify and score AI initiatives and the vendors to work with.

Emerj’s AI in Banking Opportunity Landscape provided a framework of business functions and application categories to facilitate executive-level conversations, and a comprehensive and scored vendor map to gain clarity.

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