As we will explain, when these interdependent layers work in unison, they enable a bank to provide customers with distinctive omnichannel experiences, support at-scale personalization, and drive the rapid innovation cycles critical to remaining competitive in today’s world. Each layer has a unique role to play—under-investment in a single layer creates a weak link that can cripple the entire enterprise.
The following paragraphs explore some of the changes banks will need to undertake in each layer of this capability stack.
Increasingly, customers expect their bank to be present in their end-use journeys, know their context and needs no matter where they interact with the bank, and to enable a frictionless experience. Numerous banking activities (e.g., payments, certain types of lending) are becoming invisible, as journeys often begin and end on interfaces beyond the bank’s proprietary platforms. For the bank to be ubiquitous in customers’ lives, solving latent and emerging needs while delivering intuitive omnichannel experiences, banks will need to reimagine how they engage with customers and undertake several key shifts.
First, banks will need to move beyond highly standardized products to create integrated propositions that target “jobs to be done.” 8 Clayton M. Christensen, Taddy Hall, Karen Dillon and David S. Duncan, “Know your customers ‘jobs to be done,” Harvard Business Review, September 2016, hbr.org. This requires embedding personalization decisions (what to offer, when to offer, which channel to offer) in the core customer journeys and designing value propositions that go beyond the core banking product and include intelligence that automates decisions and activities on behalf of the customer. Further, banks should strive to integrate relevant non-banking products and services that, together with the core banking product, comprehensively address the customer end need. An illustration of the “jobs-to-be-done” approach can be seen in the way fintech Tally helps customers grapple with the challenge of managing multiple credit cards. The fintech’s customers can solve several pain points—including decisions about which card to pay first (tailored to the forecast of their monthly income and expenses), when to pay, and how much to pay (minimum balance versus retiring principal)—a complex set of tasks that are often not done well by customers themselves.
The second necessary shift is to embed customer journeys seamlessly in partner ecosystems and platforms, so that banks engage customers at the point of end use and in the process take advantage of partners’ data and channel platform to increase higher engagement and usage. ICICI Bank in India embedded basic banking services on WhatsApp (a popular messaging platform in India) and scaled up to one million users within three months of launch. 9 “ICICI Bank crosses 1 million users on WhatsApp platform,” Live Mint, July 7, 2020, livemint.com. In a world where consumers and businesses rely increasingly on digital ecosystems, banks should decide on the posture they would like to adopt across multiple ecosystems—that is, to build, orchestrate, or partner—and adapt the capabilities of their engagement layer accordingly.
Third, banks will need to redesign overall customer experiences and specific journeys for omnichannel interaction. This involves allowing customers to move across multiple modes (e.g., web, mobile app, branch, call center, smart devices) seamlessly within a single journey and retaining and continuously updating the latest context of interaction. Leading consumer internet companies with offline-to-online business models have reshaped customer expectations on this dimension. Some banks are pushing ahead in the design of omnichannel journeys, but most will need to catch up.
Delivering personalized messages and decisions to millions of users and thousands of employees, in (near) real time across the full spectrum of engagement channels, will require the bank to develop an at-scale AI-powered decision-making layer. Across domains within the bank, AI techniques can either fully replace or augment human judgment to produce significantly better outcomes (e.g., higher accuracy and speed), enhanced experience for customers (e.g., more personalized interaction and offerings), actionable insights for employees (e.g., which customer to contact first with next-best-action recommendations), and stronger risk management (e.g., earlier detection of likelihood of default and fraudulent activities).
To establish a robust AI-powered decision layer, banks will need to shift from attempting to develop specific use cases and point solutions to an enterprise-wide road map for deploying advanced-analytics (AA)/machine-learning (ML) models across entire business domains. As an illustration, in the domain of unsecured consumer lending alone, more than 20 decisions across the life cycle can be automated. 11 Renny Thomas, Vinayak HV, Raphael Bick, and Shwaitang Singh, “Ten lessons for building a winning retail and small-business digital lending franchise,” November 2019, McKinsey.com. To enable at-scale development of decision models, banks need to make the development process repeatable and thus capable of delivering solutions effectively and on-time. In addition to strong collaboration between business teams and analytics talent, this requires robust tools for model development, efficient processes (e.g., for re-using code across projects), and diffusion of knowledge (e.g., repositories) across teams. Beyond the at-scale development of decision models across domains, the road map should also include plans to embed AI in business-as-usual process. Often underestimated, this effort requires rewiring the business processes in which these AA/AI models will be embedded; making AI decisioning “explainable” to end-users; and a change-management plan that addresses employee mindset shifts and skills gaps. To foster continuous improvement beyond the first deployment, banks also need to establish infrastructure (e.g., data measurement) and processes (e.g., periodic reviews of performance, risk management of AI models) for feedback loops to flourish.
Additionally, banks will need to augment homegrown AI models, with fast-evolving capabilities (e.g., natural-language processing, computer-vision techniques, AI agents and bots, augmented or virtual reality) in their core business processes. Many of these leading-edge capabilities have the potential to bring a paradigm shift in customer experience and/or operational efficiency. While many banks may lack both the talent and the requisite investment appetite to develop these technologies themselves, they need at minimum to be able to procure and integrate these emerging capabilities from specialist providers at rapid speed through an architecture enabled by an application programming interface (API), promote continuous experimentation with these technologies in sandbox environments to test and refine applications and evaluate potential risks, and subsequently decide which technologies to deploy at scale.
To deliver these decisions and capabilities and to engage customers across the full life cycle, from acquisition to upsell and cross-sell to retention and win-back, banks will need to establish enterprise-wide digital marketing machinery. This machinery is critical for translating decisions and insights generated in the decision-making layer into a set of coordinated interventions delivered through the bank’s engagement layer. This machinery has several critical elements, which include:
Deploying AI capabilities across the organization requires a scalable, resilient, and adaptable set of core-technology components. A weak core-technology backbone, starved of the investments needed for modernization, can dramatically reduce the effectiveness of the decision-making and engagement layers. The core-technology-and-data layer has six key elements (Exhibit 7):
The AI-first bank of the future will need a new operating model for the organization, so it can achieve the requisite agility and speed and unleash value across the other layers. While most banks are transitioning their technology platforms and assets to become more modular and flexible, working teams within the bank continue to operate in functional silos under suboptimal collaboration models and often lack alignment of goals and priorities.
The platform operating model envisions cross-functional business-and-technology teams organized as a series of platforms within the bank. Each platform team controls their own assets (e.g., technology solutions, data, infrastructure), budgets, key performance indicators, and talent. In return, the team delivers a family of products or services either to end customers of the bank or to other platforms within the bank. In the target state, the bank could end up with three archetypes of platform teams. Business platforms are customer- or partner-facing teams dedicated to achieving business outcomes in areas such as consumer lending, corporate lending, and transaction banking. Enterprise platforms deliver specialized capabilities and/or shared services to establish standardization throughout the organization in areas such as collections, payment utilities, human resources, and finance. And enabling platforms enable the enterprise and business platforms to deliver cross-cutting technical functionalities such as cybersecurity and cloud architecture.
By integrating business and technology in jointly owned platforms run by cross-functional teams, banks can break up organizational silos, increasing agility and speed and improving the alignment of goals and priorities across the enterprise.
The journey to becoming an AI-first bank entails transforming capabilities across all four layers of the capability stack. Ignoring challenges or underinvesting in any layer will ripple through all, resulting in a sub-optimal stack that is incapable of delivering enterprise goals.
A practical way to get started is to evaluate how the bank’s strategic goals (e.g., growth, profitability, customer engagement, innovation) can be materially enabled by the range of AI technologies—and dovetailing AI goals with the strategic goals of the bank. Once this alignment is in place, bank leaders should conduct a comprehensive diagnostic of the bank’s starting position across the four layers, to identify areas that need key shifts, additional investments and new talent. They can then translate these insights into a transformation roadmap that spans business, technology, and analytics teams.
Equally important is the design of an execution approach that is tailored to the organization. To ensure sustainability of change, we recommend a two-track approach that balances short-term projects that deliver business value every quarter with an iterative build of long-term institutional capabilities. Furthermore, depending on their market position, size, and aspirations, banks need not build all capabilities themselves. They might elect to keep differentiating core capabilities in-house and acquire non-differentiating capabilities from technology vendors and partners, including AI specialists.
For many banks, ensuring adoption of AI technologies across the enterprise is no longer a choice, but a strategic imperative. Envisioning and building the bank’s capabilities holistically across the four layers will be critical to success.
Suparna Biswas is a partner, Shwaitang Singh is an associate partner, and Renny Thomas is a senior partner, all in McKinsey’s Mumbai office. Brant Carson is a partner in the Sydney office, and Violet Chung is a partner in the Hong Kong office.
The authors would like to thank Milan Mitra, Anushi Shah, Arihant Kothari, and Yihong Wu for their contributions to this article.
