Page 8 - Pioneering Intelligent Banking

Pioneering Intelligent Banking eBook

Overcoming Challenges to Achieve Key challenges to AI adoption by banks (banking executive view)

Artificial Intelligence at Scale

Challenges across the four pillars of People, Technology, Business, and Finance hinder AI
adoption at scale : 74% of banking executives reported a lack of mid & senior AI talent and
61% of governance model, 62% an inability to track AI performance, and 62% an ambiguity on
AI ROI.

More than 60% of AI projects fail to move beyond pilot stage and 80% of banking executives
say that achieving technology and business at scale with AI pilot projects is a significant
challenge. With three in five banks not achieving quantifiable benefits and one in four banks
not realizing any benefits from AI initiatives, banks need to recalibrate their AI adoption by
focusing on specific challenges in each pillar to harvest maximum value. Source: Capgemini State of AI executive survey 2020; n=100

Banks struggle to harvest value and scale AI projects
To maximize value and achieve AI@Scale, banks must overhaul their
legacy environment.”
AI projects fail to move beyond pilot stage

Calibrating systems to get the most out of data is key. To obtain effective results from AI
adoption, banks need to develop a robust and modular infrastructure and become data-centric
minded. However, banks face major issues due to legacy systems, high maintenance cost, and
outdated and slow data processing capabilities. of banking executives say that achieving
technology and business scale with AI pilot
While data centricity is key to AI efficacy, only 26% of banks extract the best out of existing projects is significantly challenging.
data. Others either lack data management skills (storing, accessing, sharing, securing) or
know-how on data utilization (process improvements and personalization). They are data-rich Source: Capgemini State of AI executive survey 2020; n=100
but not data-centric.

Key issues faced by banks due to legacy systems
To achieve AI@Scale, banks must revamp the core and become
data-centric.” High IT maintenance cost 62%

Increased IT complexity 60%
Inefficient integration with emerging technologies 55%
Inability to connect easily to third-party APIs 49%
Lack of scalability 48%

For more information about how Capgemini can help you achieve artificial Slow data processing capability 47%
intelligence at scale, contact us at financialservices@capgemini.com Source: Capgemini World Retail Banking Report 2020, n=81

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