As we noted at the beginning of this series on the AI bank of the future, disruptive AI technologies can dramatically improve banks’ performance in four key areas: higher profits, at-scale personalization, smart omnichannel experiences, and rapid innovation cycles. The stakes could not be higher, and success requires a holistic transformation spanning all layers of the organization’s capability stack.
Our previous articles have focused on the capability stack’s technology layers: reimagined engagement,
AI-powered decision making,
and modern core technology and data infrastructure.
Leveraging these capabilities to create value requires an operating model combining structure, talent, culture, and ways of working to synchronize all layers of the stack. Synchronizing these layers is not easy. Any organization undertaking an AI-bank transformation must determine how to structure the organization so that its people interact and leverage tools and capabilities to deliver value for each customer at scale. In this article, we take a closer look at the need for a platform operating model, the categories and scope of operating models, and the building blocks of effective models.
The heart of an AI bank is always-on customer interaction
The need to change a bank’s operating model arises from a combination of external and internal circumstances. Externally, as consumers and businesses increasingly rely on AI technologies in daily life, banks are shifting the foundation of their business models from products to experiences. In other words, as many traditional banking products become embedded—or even “invisible”—within beyond-the-bank journeys, experiences become the more salient element of a customer’s relationship with the bank. This shift involves a rapid increase in the number of customer interactions, and at the same time, the revenue associated with each interaction is declining. This is a fundamental change: just a few years ago, customers conducted business with the bank by visiting a branch once or twice a month; more recently, they would conduct transactions several times each week through the bank website; now many customers interact with their bank daily through their mobile banking app, and often several times a day through wearable devices. In short, banks and their customers now have an interconnected, always-on relationship.
Circumstances within the bank are changing as well—albeit at a slower pace, due largely to the complexity of legacy technology and operating models coupled with the steadily rising cost of maintaining and upgrading IT infrastructure. Siloed structures also hamper organizations’ ability to transform themselves. Decision making at traditional banks is typically slow and cumbersome, and ineffective prioritization (done at too high a level without understanding underlying resource contentions) results in frequent project delays and cost overruns. Insufficient domain expertise and blurred accountability—particularly between business units and technology teams—too often cause new solutions to fall short of customer expectations. What is more, multiple systems perform similar functions, and the increasing complexity of IT architecture with a proliferation of applications weakens system resilience and stability and increases risk when changes are made.
The widening divide between fast-evolving customer expectations and inertia within the bank reinforces silos and weakens the bank’s ability to respond to the demands of the new machine age. The challenge for leaders is to shift the organization from this siloed structure to a radically flattened network of platforms.
Platforms focus on delivering business solutions
Today, banks that recognize the value of AI and technology enabling better customer and business experience are moving steadily toward a platform operating model, leveling command-and-control structures to speed decision making and bring people together in teams relentlessly focused on delivering solutions that customers value. In this agile approach, each platform can be thought of as a collection of software and hardware assets, funding, and talent that together provide a specific capability. While some platforms, such as those for retail mortgages, deliver business-technology solutions to serve internal or external clients, others enable other platforms with shared services and support functions (for example, payments and core banking). Each platform is largely self-contained in producing business and technology outcomes and autonomous in prioritizing its work to meet strategic goals within clearly defined guardrails, such as common standards, finance, and risk control.
As banks think about setting up a platform operating model, they should bear in mind that each platform comprises three main elements. When structured correctly, these elements will help a platform team set its North Star and carry out its mission in a way that creates value for customers and the enterprise.
- Strategy and road map. The joint vision combines business and technology outcomes to deliver end-to-end value. Close alignment between the business unit and the technology group on performance objectives and agenda unites all members of the platform around a shared strategic vision, with a road map for executing priorities that balance change and resiliency.
- Organization and governance. Organization of business-facing platforms (e.g., retail mortgages) should be based on a “two in a box” engagement model, meaning business and technology leaders own joint performance metrics that track both commercial and technological outcomes. Each platform manages its business and technological priorities through a shared backlog of work and delivers through persistent cross-functional agile teams, each of which builds its platform over time and focuses not only on one project, but continually improves the platform.
- Technology. Each platform owns its technology landscape and standardized interaction mechanisms with other platforms (for example, leveraging APIs). It also has an inherent objective to modernize its technology.
In most cases, a platform can be thought of as a nimble fintech group in one of three main categories: business platforms, enterprise platforms, or enabling platforms (Exhibit 1).
Business platforms are aligned to business units and deliver joint business-and-technology outcomes. As an example, a business platform for consumer lending would include several cross-functional teams, each of which owns front-end technology assets and includes business teams for a specific function or service area.
One team might focus on preapproval and new-customer acquisition, with responsibility for next-generation credit-scoring models using traditional data sources (such as credit bureau reports and internal transaction histories) and nontraditional sources (including, upon the customer’s permission, tax returns, online presence, partner ecosystem transactions, and more). Another team often takes responsibility for loan underwriting, determining credit limits for individual accounts in accordance with enterprise risk policy. A third team might focus on consumer insights and personalized messaging, including machine-learning decision models and marketing technology (“martech”) tools to deliver intelligent credit offers to new and existing customers. The customization team owns the design, development, and management of product configurations to ensure that each solution addresses the customer’s precise needs.
Other teams focus on services and capabilities to support external developers and other technology partners, including, for example, partner onboarding and sandbox management and APIs supporting customer journeys and experiences (managing standards and documentation through development hubs or platforms). Still other teams support the consumer lending platform by managing technology—for example, provisioning of cloud infrastructure.
Enterprise platforms enable diverse business platforms by providing shared services such as vendor management and procurement, standardization of cloud and DevSecOps tooling,
build-to-stock process APIs and reusable microservices, and standardized data access and governance. Other enterprise platforms aggregate support functions such as finance, risk, and human resources within a center of excellence.
Enabling platforms support other platforms by ensuring that technical functionality is delivered quickly and securely at scale. This approach has proven effective at maximizing scale benefits while protecting the enterprise with standardized processes. Examples of enabling platforms include core technology infrastructure, DevOps tools and capabilities, and cybersecurity.
Implementing a platform operating model requires five main building blocks
The distinct advantage of a platform operating model is the foundation it provides for business-and-technology partnerships focused on delivering leading-edge AI-enabled solutions (Exhibit 2). As they begin planning the transition from hierarchical silos to a network of horizontally interconnected platforms, bank leaders should focus on five main building blocks: agile ways of working, remote collaboration, modern talent strategy, culture and capabilities, and architectural guardrails. The value and efficiency that can be derived from platform operating models are possible only if organizations design their operating model to enable these five elements. Once they have established their vision of the new management approach, they should develop a road map for implementing the platform model.
1. Agile ways of working
By extending the platform structure to all groups, an organization gains the ability to quickly redirect their people and priorities toward value-creating opportunities.
For this model to work, however, banks need to develop agile mindsets within each team and equip team members with agile ways of working, such as rapid decision and learning cycles, breaking initiatives into small units of work, piloting new products to get user input, and rapidly testing operational effectiveness before scaling.
This methodology, when deployed across the organization, underpins a new corporate culture that enables fast communication and collaboration within and among platforms. It gives the organization a strong and stable backbone for developing and scaling dynamic capabilities.
The starting point depends on where the bank is in its technology transformation. Some may set up an agile pilot within a platform and gradually train other groups in the new practices. For banks where diverse groups have already achieved a degree of organizational and operational flexibility, the time may be right for an end-to-end transformation program that “flips” the organization to agile.
Each platform consists of one or multiple squads or pods combining IT, design, and customer-journey experts, among others (up to nine people). Banks should also create “chapters” as cross-squad groups of employees with similar functional competencies to ensure growth of expertise and cross-training of colleagues across technologies. In some cases, a bank will need to create new roles, such as tribe leaders and agile coaches. It is also crucial to adopt a performance-management model that aligns all individuals with team goals.
The agile way of working is a means to an end, not an end in it itself. As banks begin to implement a platform operating model, it is crucial that they set a North Star, not only to unite people around business goals but also to offer them a sense of meaning and purpose within society. Shared values reinforce team spirit and—when combined with opportunities to learn, experiment, and make a difference for customers—strengthen employee engagement. This stronger employee engagement can be measured in, for example, productivity and loyalty and can indicate how well an organization has embraced the agile transformation.
2. Remote collaboration
For a variety of reasons, including geographic distribution, work-from-home policies, travel restrictions, and other disruptions due to COVID-19, banks have moved to a fully or partially remote model. The sharp decline in co-location has put pressure on organizations to improve collaboration and consistency in ways of working. Given the expectation that a significant share of bank employees may not return to shared work environments,
banks need to develop mechanisms to support effective collaboration—and thus reduce errors—in distributed environments.
Indeed, banks need to revisit agile teams after an abrupt shift to remote models
and consider the types of work to be done remotely according to how well interaction models and system readiness can be adapted. Two criteria are key for determining which roles can function effectively in remote work arrangements. First is the required level of human interaction, such as the degree of real-time collaboration and creative work among groups of people and the degree to which work can be segmented and individualized. Second is bank systems’ readiness—particularly in terms of data accessibility, software accessibility, and tooling—to support secure and efficient remote work.
For example, setting clear decision-making and escalation paths is essential to maintain a fast cadence. Shared workflows, roles, and responsibilities help move work through the pipeline for even the most complex and highly interactive jobs.
Setting up a single source of truth or single backlog of work also helps keep different platforms aware of interdependencies. What is more, banks can and should ensure the security of remote working arrangements by leveraging specialized technology for managing remote access. Areas subject to management may include data retrieval (role-based access to data, restrictions in downloading sensitive data, restriction of all data copying even on encrypted removable hard drives), sophisticated detection (tracking and monitoring mechanisms to detect data breach), and governance procedures to review breaches and enforce corrective actions.
Banks should also set up mechanisms to address both interaction and security criteria. These mechanisms are particularly crucial for remote-working arrangements, which are increasingly important to top talent in technology-intensive industries, including financial services.
3. Modern talent strategy
A modern talent strategy for an AI bank is not only about the commitment and capability to hire the best engineering talent or the best business talent. The AI-bank operating model also requires leaders to rethink their strategy for hiring and retaining top talent in a world with blurring lines between business, IT, and digital expertise. Leaders must form a detailed picture of the diverse skills and expertise required to deliver business-technology solutions. Reskilling is equally critical to building teams with the right mix of talent.
This strategy focuses on attracting digital talent and requires that leaders understand the unique needs of digital talent. It employs a diversified approach to recruiting: engaging with technologist communities, sponsoring hackathons to scout talent, and ensuring that recruiters have experience in technology. The best technical talent has a disproportionately higher impact, so the ability to attract and develop superior candidates is crucial. In a similar vein, leading tech organizations enlist their top performers in the recruiting effort.
Furthermore, banks need to improve retention and reskilling. Reskilling may involve charting a clear career development path for digital talent, creating an environment that prioritizes and rewards learning, and rewarding deep expertise over fungible skill sets. There is also opportunity to build capability-development programs that help reskill nontechnical colleagues as technologists. Finally, so that attracting and developing digital talent can produce the desired results, banks need a clear strategy for retaining this talent, such as providing flexible and collaborative ways of working and empowering digital talent to implement change.
To develop a comprehensive talent strategy, an AI bank would first review existing initiatives, the structure and makeup of each platform, and the technical talent required to execute the strategy. The second step is to build from the ground up a model of talent required for the next stage of growth, including both existing and future initiatives. Next, it is important to create a set of talent interventions that can tap into existing talent within the organization, developing an “ecosystem” of partners (vendors, developers, gig workers, remote talent, and others) and using hiring mechanisms, including the acquisition of smaller companies and start-ups, to establish platforms requiring skills beyond the traditional scope of the bank’s roles and capabilities. Finally, banks have to make themselves externally appealing to fresh tech talent and internally exciting for their people. This means transforming themselves so top technical talent want to stay and grow within the organization and so all employees see and embrace the change and invest in upgrading their skills. In short, banks need to become great engineering organizations.
4. Culture and capabilities
As banks build sophisticated technical solutions, they also need to develop a culture suited to the experts building these solutions. Organizations need to manage culture and capabilities to create a virtuous circle that attracts talent, sparks innovation, and creates impact. This underscores the importance of talent and culture in tech-enabled transformations,
including AI-bank transformations.
For the platform operating model to work, leaders need to steer their organizations to focus on the end user, collaborate across silos, and foster experimentation. Establishing this digital culture across the bank involves addressing four dimensions of culture: understanding/conviction, reinforcement, reskilling, and interaction.
First, understanding and conviction follow largely from the bank’s leadership, expressed through role modeling and encouraging desired behaviors, including continuous learning, knowledge-sharing, and interdisciplinary collaboration. For example, if a top team visibly takes part in upskilling programs for AI and machine learning, this demonstrates to all in the organization the importance of automation and evidence-based decision making to all parts of the business. Another approach is to support technology start-ups by giving them access to nonsensitive code and shareable data to build their own “open solutions” related to AI banking.
The second is to reinforce new practices with formal mechanisms, so that the structures, processes, and systems of the AI bank become embedded within the culture. For example, banks might consider organizing institution-wide innovation challenges or inviting managers to daily huddles where they actively work with the centers of excellence to solve problems and own outcomes.
Third, leaders need to ensure that every individual has access to the skills they require to be effective. One way to do this is by developing entirely new tools and technology using in-house open-source systems. Another is to ensure transparency by setting up digital wikis that anyone can use to access knowledge. Organizations can also learn from others by sending employees on “innovation tours” or actively encouraging and sponsoring attendance at high-quality conferences.
Finally, leaders should model various approaches to interaction. Banks can visibly change the ways managers interact with teams, such as by moving from meetings to offline asynchronous communications using highly collaborative tooling. Leaders can also use symbols in remote and in-person meetings to emphasize enterprise values such as customer centricity. At a leading bank, for example, every meeting has an empty chair to remind participants of the customer for whom they are building solutions.
5. Architectural guardrails
Each platform is responsible for its own technology landscape, but standardized mechanisms for interaction among platforms should be jointly designed across all platforms. It is important, therefore, to ensure that architectural guardrails are observed so that each platform can easily interact with others. These guardrails should not be perceived as restricting platforms from developing and improving their own technology and technical decisions.
As each platform is free to build the technology elements required to deliver on its mandated business goals, there is potential for miscommunication among platforms. For example, instead of developing its own interest rate calculation, a consumer lending platform would leverage a single, standard calculation via an API. With no guardrails in place, there would be significant inefficiency, because efforts would be duplicated in some areas and tasks would be unfinished in others. By contrast, guardrails support efficient management and operation of the overall IT landscape, with responsibility for various elements of the enterprise architecture delegated to individual platforms. These various responsibilities are formally documented and communicated widely. Without such guardrails, inefficiencies would multiply.
These architectural guidelines should focus on strategic activities rather than operational tasks, which are subject to the discretion of the platform. This requires significant time upfront for strategic planning, and each platform must stay alert to new value-creation opportunities related to its mandated strategic objectives.
Further, platform owners can evaluate the effectiveness of these guardrails by tracking the number of business capabilities in accordance with these guardrails, rather than simply counting the various technology applications found within the organization.
Mapping the operating model of a financial-services organization
A large global or regional AI bank implementing a platform-based operating model would typically have 20 to 40 platforms, each focused on a specific type or set of services, such as payments, lending, infrastructure, or cybersecurity (Exhibit 3). As noted above, these platforms are often grouped into one of three areas.
- Business platforms typically include a consumer platform, which is linked to channels (digital, branch) and products (wealth, consumer) as well as customer relationship management and analytics; a corporate platform, which spans channels and products (transaction banking, lending) and relationship management (corporate servicing); and a global-markets platform, which covers channels, products, and global market operations, as well as market and credit risks.
- Enterprise platforms provide shared services across different business platforms across the enterprise on administrative elements such as customer servicing; employee services; finance; HR; risk, legal, and compliance; and technology platforms usable by business platforms such as payment infrastructure, cloud infrastructure, data, and API management.
- Enabling platforms support business and enterprise platforms to deliver technical functionality quickly. These platforms include enterprise architecture, delivery enablement, access and authentication management, cybersecurity, and infrastructure/site reliability engineering (SRE).
The platform model can help organizations seize new opportunities
Executing on a platform operating model is arduous. However, when done correctly, it has the potential to deliver four main benefits to all stakeholders: value-oriented business-technology partnerships, stronger performance (speed, efficiency, and productivity), transparency, and a future-ready business model.
The collaborative framework of the platform model brings business and technology leaders together as co-owners in creating value for the enterprise. Joint owners of business-facing platforms share accountability for outcomes, merging business knowledge of market opportunities with expert insight into how technological advances can enhance customer experiences. The leader of the platform facilitates the interaction of business and technology owners in determining the right balance between run-the-bank and change-the-bank initiatives. All members of a particular team are unified in delivering a solution (just as those of the entire “tribe” of a platform are focused on a service line) in order to create value in alignment with enterprise strategic objectives. This unity is reinforced by the fact that all team members share in performance metrics for both business and technology outcomes, including impact on users (internal and external), on-time delivery of solutions, customer and employee satisfaction ratings, and more.
The platform approach can strengthen an organization’s performance in terms of speed, efficiency, and productivity when each platform is large enough to address a set of use cases crucial to realizing the business model of the enterprise but small enough to keep the team agile. Each team enjoys a degree of autonomy, with a budget and mandate to experiment and discover the best way to maximize value within a discrete domain in alignment with predefined guardrails (for instance, finance, risk, compliance) without having to wait for approvals from finance and allocations from IT and human resources. This autonomy speeds up decision making, innovation, and solution delivery. The use of automated tools, enterprise standards, and agile patterns of communication and collaboration increases efficiency in two ways. First, this approach minimizes duplication of effort by documenting repeatable processes and cataloging technology tools and analytical models available for deployment in diverse contexts. Second, it allows individuals to access data (according to clearly defined need-to-know criteria) and advanced analytical tools to extract insights to augment their impact. Over time, persistent agile teams build their domain expertise and agile skills for collaboration and timely delivery.
In addition to the emphasis on interdisciplinary collaboration, the platform model is designed to increase transparency, accountability, and knowledge sharing to the fullest extent possible. Transparency should be high not only so employees can clearly identify the services available from each platform but also to support independent benchmarking of team performance and identification of best practices. Each platform should also be clear about how it prioritizes work, tracks initiatives in the pipeline, and manages the backlog.
Finally, shifting to a platform model can help an organization future-proof its business model because each platform is incentivized to continuously improve on its technology landscape. Within a culture of continuous learning, team members are accustomed to change and adept at finding the best response to fast-evolving circumstances. Interdisciplinary initiatives led by business-technology co-owners strengthen a team’s capacity to anticipate and consider potential challenges and opportunities before they appear on the horizon. Enterprise-wide standards, rigorous documentation of processes, and consistent cataloging of technology assets enable teams to apply best practices as they develop and implement new solutions.
By underpinning business-technology co-ownership of solutions delivery and value creation, the platform operating model offers banks an opportunity to maximize the impact of their technology capabilities in ways that count for customers. The implementation of the platform model begins logically with the formation of joint business-and-technology teams focused on the design, development, and implementation at scale of new AI-bank innovations, always striving toward a more intelligent value proposition and smarter experiences and servicing. Further, the creation of cross-functional platforms is also an excellent approach to increase business–technology collaboration, developing an IT operating model that generates immediate and tangible business value and moves the full organization, not just technology, to an agile way of working. However, to derive maximum value from platforms and the people who make up these platforms requires new skills, mindsets, and ways of working. Bringing all these elements together is a powerful mechanism to optimize the full capability stack, from core technology and data infrastructure to AI-powered decision making and reimagined customer engagement. The platform operating model ensures that these layers run in sync to spur the growth of an AI bank of the future.