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Financial services CIOs – building GenAI at scale while managing risk

GenAI provides financial services CIOs with an opportunity to create a bold vision and lead transformation by example.

In brief:

  • Chief Information Officers (CIOs) must lead by example, transforming the tech workforce and workflow with GenAI.
  • The opportunity to reimagine operations with GenAI requires building a solid tech foundation to bring use cases efficiently from pilot to production.
  • The CIO is an integral player in the board-level GenAI discussion of transformation governance and risk.

There is no shortage of use cases to be visioned for generative artificial intelligence (GenAI) in financial services. In fact, some would argue that there is a sea of noise obscuring the distinction between quick hit, incremental value projects and game-changing transformation. The CIO stands at the center of this conversation as both an internal disrupter and a trusted service provider to help the organization realize the potential value behind the prioritized use cases.

With the added burden of a highly regulated business, CIOs of financial institutions must be particularly wary of moving too fast without the proper governance and controls in place to manage risk. At the same time, they must be mindful that their organizations don’t fall behind competitors disrupting the market. Chasing use cases without developing strong foundational capabilities will leave the organization exposed to both competitors and risk. How quickly an organization can sort through use cases, grapple with governance and move beyond simply resourcing projects to build GenAI capabilities at scale will be the challenge for CIOs in the next 12 months. Outlined here are a few key areas that CIOs should be addressing to make an impact and develop a framework of action that can enable enterprise implementation.

Shift the organization to “AI-first” thinking and imagine the possible

Financial institutions have historically embraced AI and machine learning in the middle and back office to speed transactions, mitigate investment risk and reduce cost. Now, GenAI provides new opportunities across the tech stack, including the front office, to not only create further efficiencies, but also to reimagine everything from opening a new bank account, to managing an investment portfolio, to processing an insurance claim. GenAI brings new capabilities and makes existing AI capabilities more accessible to employees and clients. In this way, some see GenAI as an opportunity to revisit the AI adoption in an organization, not only to re-examine internal operations, but also to reimagine business models. The CIO can provide leadership here not only by setting a vision and agenda, but also by educating their business counterparts on the appropriate use of AI/GenAI and by building the foundational tech stack to enable GenAI across the enterprise.


With this disruption also comes the responsibility and the need for transparency and governance at a board level, where the CIO must provide support and guidance. Boards of directors and senior leadership teams will have a responsibility to analyze both the acute risks and ethical considerations presented by GenAI. GenAI introduces new risks and legal concerns in how content is created, what data is used and how the algorithms are tuned, which demands both controls and transparency. Financial institutions making lending or credit decisions (e.g., leveraging GenAI) must be prepared to prove that data inputs do not contain bias and that there is transparency in how data is processed.


Be the catalyst for workforce transformation

The CIO has a bold opportunity to lead by example. The implementation of GenAI will disrupt virtually every job in the enterprise, eliminate some, create others and transform most. This requires a mobilization of the entire organization as the CIO partners with the business executives, COO, CHRO, CDO and others across the executive suite to build an enterprise-wide plan to upskill the workforce. Prompt engineering, for example, is a new skill, or perhaps even a new role, within the organization that is needed to maximize the benefit of GenAI. This level of disruption can sometimes be met with trepidation, particularly in areas of the firm where the fear of being replaced is highest; however, showing results in initial use cases, with a focus on augmenting human capabilities, will allay fears and inspire curiosity for how employees can leverage the technology to improve their own performance.


In this CIO “lead-by-example” approach, GenAI can accelerate the coalescing of software development, devops and the overall cyber agenda across the software engineering lifecycle. Flowing down to the day-to-day activities of the developers, team members can leverage code copilots to find opportunities to optimize, uncover limitations and mitigate risks. For the broader employee population, it’s low risk and nonthreatening to demonstrate how GenAI can enhance productivity and improve employee experience.

Optimize and modernize IT operations

Demonstrating GenAI value by transforming how IT operations function, particularly in software and solutions development, has direct implications from a design and cost standpoint, as well as serving as a showcase for the rest of the firm. Several areas rise to the top as key opportunities for the CIO:

  • Operational efficiency – Technology operations is an area where GenAI can yield not only gains in productivity, but also cost savings. For the CIO, there’s an opportunity to be an even greater contributor to the financial goals of the firm and show impact to the bottom line, through both cost takeout and potential new revenue streams. Not only are there opportunities to shift costs of development from rote coding tasks to a more strategic impact, but GenAI-assisted development also delivers improved time to market and reduced risk for the firm.
  • Risk takeout – Legacy systems in need of modernization represent a risk to the organization on several levels. Solution and code functionality is often poorly documented and not fully understood as the original developers leave the firm or former skills stagnate. Legacy solutions were not architected with current standards and best practices in mind, leading to evolving workarounds and increased tech debt. GenAI represents an opportunity to understand and audit this code efficiently, allowing the organization to stabilize legacy solutions and undertake modernization efforts. On a go-forward basis, GenAI can also be leveraged for ongoing code and application review, proactively mitigating risk as platforms evolve and age.
  • Workforce optimization – While some think of GenAI as workforce replacement, extending human capabilities and optimizing time will be its main benefits in the short and medium terms. Firms hope to meaningfully reduce developer time spent in areas like code review and quality assurance, initial software development, security and compliance. This can elevate the entire development cycle toward a focus on design, architecture and performance vs. code development and testing. 
  • Accelerated software engineering – In the software development cycle, GenAI can be a valuable accelerator. A variety of tools in the marketplace are focused on use cases, such as auto-generating project plans, drafting user stories, rapid proto-typing, code refactoring and optimization, and synthetic data generation for testing can reduce development time and impact time to market for new applications.

In addition, GenAI could power a decision-maker dashboard on the desks of executives, traders, claims processors or be customized for any employee where real-time access to synthesized information can improve outcomes. Industry knowledge, trading and transaction patterns; synthesizing and correlating news events with markets; risk assessment; and macro trends from historical data can be leveraged internally or offered externally as a service where appropriate.

A starting point for GenAI transformation

One of the biggest challenges in GenAI is the sheer scale of the opportunity and the complexity of moving parts to build capabilities. For the CIO, achieving value creation at an enterprise level means tackling the challenges systematically. The key areas for action can be summed up as follows:

Define the strategy – AI strategy is typically developed at an enterprise level and requires alignment between the business, technology and people strategy in the organization, and it links directly to the core strategic tenets of the organization. 

Build a foundation – A foundational set of capabilities that encompasses AI platform architecture and tooling, model developer tooling, governance framework, model ops framework and finops framework is critical for AI adoption across the enterprise. While certain financial institutions have established a strong foundation through past investments in AI capabilities, they may need to realign their strategies, AI architecture and platforms to enhance their existing AI capabilities to enable GenAI. 

Implement use cases and scale – Deployment of a sole use case is tactical; measuring value and creating scale is strategic. Creating a continually updated and repeatable AI deployment playbook with KPIs, defined technology choices, including risk management and ethical use guidelines, will ensure that investments create strategic enterprise value.

Operate and monitor models – Unlike traditional AI, which demands frequent data and algorithm updates, LLMs possess the ability to learn and adapt in real-time. Continuous monitoring and recalibration of these models is critical to ensure regulatory compliance and business value realization. Periodic testing for data appropriateness, measuring transparency and explainability of black box models, model version control and monitoring, including red team testing, are crucial considerations for success. 

Equip and upskill the workforce – The level of education in using GenAI spans everything from basic productivity to ethics. The CIO must think through what future skills the organization will require. What is the current skill set? How can the gaps be filled? New roles will be needed in data, analytics and prompt engineering. New risks around cybersecurity and data leakage require that process, controls and education be mitigated.

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Shobhan Dutta and Tommy D. Vater contributed to the article.


In times of great disruption, clear leadership is imperative. GenAI is a classic people, process and technology triad that CIOs and IT operations have been balancing for decades. We commonly hear that business drives technology. GenAI has the potential to significantly accelerate that paradigm shift. By creating a vision, building a plan and asking the big questions, the CIO can drive the agenda as an advocate and a true business enabler for the opportunities presented by GenAI.

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