One such pattern consists of three steps: (1) process information; (2) evaluate/decide; (3) take creative action. In practice, this might be the workflow for replying to a customer inquiry, processing a supplier’s invoice, making a decision on a credit card application, monitoring an account for signs of money laundering, or writing a section of an investment prospectus. (See Exhibit 6.)
In legacy processes based on human expertise, a human sifts through the information, evaluates it, comes to a decision, and then takes action. But each of these stages in the pattern is an opportunity for predictive AI and GenAI to team up with the human.
Depending on the specific context, the first step (process information) might offer an opportunity to use GenAI to synthesize and condense large amounts of information into easily digestible summaries, or to engage the power of predictive AI to narrow the field of choices by extracting targeted insights from large data sets.
In the second step (evaluate/decide), a predictive AI model can reliably make automated decisions on cases that lie within its domain of expertise (typically the lion’s share of cases to be decided) and route the exceptional cases to a human in the loop. Here, the predictive model acts as the central steering mechanism for the process, independently determining the need for human involvement.
The third step (take creative action), whether it involves composing a loan rejection letter, a suspicious activity report, or a response to a customer’s question, can often be turned over to a GenAI model—for full automation of simple and/or non-mission-critical cases, or at least for preprocessing of repetitive elements when the occasional imprecision of GenAI is a risk to full automation.
Repetitive, high-volume workflows that follow a golden pattern of this sort in one or more places are game-changing opportunities to transform the process end-to-end.
Focus the Journey on People and Process, Not Just on Tech
Rapid advances in AI make it all too easy to become fixated on the technology, the IT implementation, and the data underlying it. And indeed, leaders face many important challenges here. AI is data-hungry and can lead to uncontrolled data proliferation, so a clear data strategy is essential. And although a GenAI model such as ChatGPT is very user-friendly, it is not at all IT-friendly to implement at scale.
But time and again we see instances where softer success factors—the target operating model and its organizational structures, the approach to AI talent and skills management, and the change management that must accompany any transformation—are underrepresented and underfunded within bank’s AI strategies and prove to be the most critical success factors.
Operating Model and Organizational Structure
AI enables significant productivity growth. Work is automated or augmented, and roles must be redesigned. We see four major types of impact on work that will alter roles across the organization (and drive the many examples listed in Exhibit 4):
- Repetitive tasks such as low-code/no-code automation
- Knowledge synthesis such as review of all commercial loan agreements
- Data-driven decisions such as automation of vendor negotiations
- Creative tasks such as augmentation of code generation
To adjust to this change, FIs must be bold in rethinking people-driven processes and reimagining whole functions. This effort will require the creation of more interdisciplinary teams with embedded data, business analysis, and legal capabilities; the implementation of a flatter and more agile structure for quicker iterations and decisions; and a reduction in spans of control in order to handle the increasingly complex nature of human work.
Learn More About Gen AI
Learn More About Gen AI
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Scaling artificial intelligence can create a massive competitive advantage. Learn how our AI-driven initiatives have helped clients extract value.
Finally, a platform operating model is critical to supporting successful AI adoption. An elevated market orientation with greater ability to rapidly deploy people, processes, and data will support faster and more assertive business model innovation and disruption. Cross-functional teams with end-to-end ownership of products, journeys, and services will support reimagining whole processes, and the platform operating model’s ability to drive scalability with standardization and without compromising on customization will be a key enabler.
Talent and Skills
Going forward, nearly every human role will have a relationship with AI:
- Roles that build AI such as technology specialists who create and monitor AI models and support tech platforms, leveraging deep technical capabilities
- Roles that shape AI such as functional experts who direct AI operations to deliver business outcomes and integrate models into business processes
- Roles that use AI such as practitioners who work with outputs from AI models, interpreting resulting content and data to deliver value to customers and employees
- Roles that govern AI such as specialists who monitor AI output to ensure that the software drives returns and to verify that the system uses tech safely and ethically
GenAI will have a high degree of impact on certain functions, including marketing, customer service, legal, and software development. These functions are likely to see extensive automation, resulting in significant opportunities for cost reduction, demand generation via higher-quality service, and the ability to focus resources on higher-value tasks.
Financial institutions must be pragmatic about implementing changes. This entails identifying which roles have the highest value to their particular GenAI strategy and then developing an appropriate value-added talent plan. (See Exhibit 7.) To manage the transition to GenAI well across all functions, executives must integrate GenAI directly into their workforce planning process, defining skills required in the future state, assessing current workforce potential, devising strategies for filling supply-demand gaps, and supporting comprehensive culture and change management to inform the organization’s “build, buy, or borrow” talent strategies.
a-generative-ai-roadmap-for-financial-institutions
