Banking
Becoming AI-first – How Banks Can Lead in the AI Revolution
Published by Jessica Weisman-Pitts
Posted on December 13, 2022
7 min readLast updated: February 2, 2026
Global Banking & Finance Review®
Company
Published by Jessica Weisman-Pitts
Posted on December 13, 2022
7 min readLast updated: February 2, 2026
By Nischal Tanna, CEO and Founder of TransformHub
Consumers today demand smarter, more convenient and safer ways to access, spend, save, and invest their money. Firms in the banking and financial services sector are compelled to up their game to meet these evolving needs and are increasingly turning to Artificial Intelligence (AI) to understand their customers better and provide them with seamless, innovative, and personalised experiences.
In fact, making sure AI technologies are used throughout the organisation is no longer a choice for many banks; it is now a strategic must. A recent report by tech firm Nvidia which surveyed more than 500 financial services professionals, data scientists and IT specialists from around the world found that the vast majority of financial services companies are already utilising AI in some capacity.
However, few banks have been successful in implementing and scaling AI technologies across the enterprise, despite billions of dollars being spent annually on change-the-bank technology efforts. The most frequent barrier impeding such efforts is a lack of a clear AI strategy. A weak core technology and data backbone and an outdated operating model and talent strategy are two additional problems that many banks face.
Before implementing AI technologies widely, therefore, banks must address a number of flaws in their legacy systems.
Modernising Legacy Systems
First and foremost, these legacy systems frequently lack the capability and adaptability necessary to handle the dynamic computing needs, data processing requirements, and real-time analysis that closed-loop AI applications demand. Core systems are also difficult to change, and their maintenance requires significant resources. Additionally, the data reserves of many banks are dispersed across many silos with different business and technology teams, and analytics efforts are often primarily concentrated on stand-alone use cases.
The old operational methods used by banks make it harder for them to innovate continuously. Most traditional banks are organised around discrete business lines with centralised technology and analytics teams set up as cost centres, Business owners set their own goals, and these goals are frequently insufficiently aligned with the bank’s overall technology and analytics strategy – assuming one exists. Working in siloed teams and using the “waterfall” method of implementation would only result in delays, cost overruns, and poor performance.
Banks therefore need a comprehensive strategy to overcome the obstacles preventing the implementation of AI technology across the whole organization. According to analysis from McKinsey, there are four layers that banks should look at, comprising customer engagement, decision making, core technologies, and platform operating models, for them to be leaders in the AI revolution.
Reimagining Customer Engagement
Customers expect banks to be there for them across their journeys, be aware of their context and requirements wherever they connect with the bank and provide them with a seamless experience.
To address these demands, banks need to incorporate personalisation decisions – such as what to offer, when to offer, and through which channel – into the customer’s journey. Banks also need to create value propositions that go beyond the fundamentals of banking to incorporate intelligence that automates decisions and actions on the customer’s behalf. Additionally, banks should work to incorporate pertinent non-banking goods and services that, when combined with the main banking service, fully meet the needs of their customers.
For omnichannel interaction, customers must also be able to switch between various modes – from online to mobile apps, branches, call centres, and smart device – and do so seamlessly within a single journey, with the most recent context of contact captured and updated constantly.
Developing AI-powered Decision-making
AI technologies can either completely replace or supplement human judgement across domains within the bank with significantly better results, with higher accuracy and speed, improved, personalised customer experiences, and actionable insights for employees along with stronger risk management.
To achieve these, banks will need to have an enterprise-wide road map for implementing advanced analytics and machine learning models across whole business domains, rather than aiming to develop specialized use cases and point solutions.
They will also need to add fast-evolving features to in-house AI models, such as natural language processing, AI agents and bots, and augmented or virtual reality, to their core business operations, and set up an extensive digital marketing infrastructure. This infrastructure is essential for converting judgments and insights from the decision-making layer into a series of coordinated interventions that are given through the engagement layer of the bank.
Improving the Data and Technology Infrastructure
A collection of fundamental technological components that are scalable, durable, and adaptive is needed to implement AI capabilities across the enterprise. The efficiency of the decision-making and interaction layers can be drastically impacted by a poor core technological foundation that is in need of modernisation.
To improve their infrastructure, banks should look at components such as leveraging cloud, data streaming, and data analytics to support machine learning to get their data management platforms ready for the AI world, and using cloud native tooling to build a modern, scalable API platform that supports complex architectures. Another key consideration is implementing robust cybersecurity and centralized controls in a hybrid infrastructure.
Transitioning to a Platform Operating Model
A platform-based operating model will help banks achieve greater responsiveness and release value across the organisation. This involves establishing platforms made up of cross-functional business and technology teams. Each platform team oversees its own resources, finances, performance metrics, and personnel, while providing goods or services to the bank’s end users or to other platforms within the bank. The platforms would be interlinked with cross-cutting technological capabilities such as cloud architecture and cybersecurity.
Such a model would help banks eliminate organisational silos, boost agility and speed, and better align objectives and priorities throughout the whole organisation by integrating business and technology on jointly owned platforms managed by cross-functional teams.
The journey towards AI-first is hence a journey with many steps. A practical way to get started for banks is to clarify their strategic goals – whether it is growth, profitability, customer engagement, or innovation – and then look at the variety of AI technologies that might enable them to meaningfully meet these objectives. Next, banks should carry out a thorough diagnostic across the four layers listed above to pinpoint areas in need of significant changes, extra funding, or additional personnel.
Depending on where they are in terms of market position, size, and goals, banks may not need or want to build all capabilities on their own. They can choose to focus on certain core capabilities and keep those in-house, or they can decide to partner with technology solutions providers who have the strategic capabilities, consulting insights, and technological expertise to help them achieve their AI goals. The important thing is for banks to take the first steps towards achieving transformation and AI leadership.
About Author:
Nischal Tanna, Chief Executive Officer at TransformHub, drives key functions for Innovation, Growth and Customer Success, oversees and governs each function within TransformHub, and plays a key role in business networking, customer acquisition, and customer retention.
Nischal has almost 16 years of experience in Engineering, IT and technology leadership, specialising in the Banking, Fintech and Financial services sectors.
Prior to establishing TransformHub, he was the Vice President of DBS Bank where he focused on Engineering, IT strategy, and drove transformation in Cloud, Mobile, API & DevOps. He was also Senior Vice President of Ciitbank, where he lead fintech & banking as a service. During his time in both organisations, he was a key leader in programs like Citi Fintech and Open Banking, DBS Digital Bank, DBS Consumer Banking Applications, and DBS Agile and Digital Transformation
Nischal holds a Bachelor in Engineering, Electronics and Telecommunication from the University of Mumbai, an MBA in Finance and Operations from SP Jain School of Global Management, and is a certified Oxford Fintech expert. Nischal is also a certified TOGAF & SOA architect, AWS Solution Architect and a Scrum Master.
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn. In banking, AI is used to enhance customer service and streamline operations.
Legacy systems are outdated computing systems or applications that are still in use. They often pose challenges for modernization and integration with new technologies in the banking sector.
Data infrastructure refers to the underlying systems and technologies that manage and store data. In banking, a robust data infrastructure is crucial for effective decision-making and AI implementation.
A platform operating model in banking involves creating interconnected teams that work collaboratively across functions. This model enhances agility and responsiveness to customer needs.
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