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In this article, Institute Founding member Nathan Stevenson, CEO of ForwardLane – a financial technology firm founded specifically to help financial institutions leverage the power of AI – takes us on a far-ranging journey across the financial services industry to explore how financial firms are grappling with the issue of value creation and measuring ROI on strategic AI deployment.
This article is the second in our new ongoing series on AI and the Financial Services Industry that will offer a deep-dive exploration into the impacts of AI on the industry. In our first article “Future of AI in Financial Services 2030 – A Thought Experiment”, Nathan explored potential AI integration scenarios for 2030 and how these developments might reshape the industry, impact society, and challenge our very conception of financial services. We will continue to provide cutting-edge insights, diverse perspectives, timely content, and practical relevance on topics such as: AI-Driven Distribution, Beyond Prompts, The Dawn of the Agentic Economy, and other critical trending topics.]
The ROI of AI in Financial Services: Navigating Value Creation by Nathan Stevenson, CEO, ForwardLane
On a crisp morning in October 2023, the executive team of a major European bank gathered to review their €50 million AI investment portfolio. Despite considerable spending on everything from chatbots to risk models, they struggled to answer a seemingly simple question: What returns were they actually generating? This scenario plays out in boardrooms worldwide as financial institutions grapple with measuring and maximizing their AI investments.
The stakes could not be higher. With McKinsey estimating potential global economic value of $13 trillion by 2030 from AI, financial institutions are pouring unprecedented resources into AI initiatives. Yet many struggle to move beyond simplistic ROI calculations to truly understand and optimize their returns.
The Evolution of AI Value Measurement
Consider the experience of Morgan Stanley, which implemented an AI-driven wealth management advisory system in 2022. Initial ROI calculations focused solely on cost reduction through automated portfolio management. However, the most significant value emerged unexpectedly through improved client retention and increased share of wallet – benefits that traditional ROI metrics would have missed entirely.
“Traditional ROI calculations fail to capture AI’s multifaceted impact” explains Erik Brynjolfsson. “Organizations need frameworks that capture both immediate financial returns and longer-term strategic value creation”. This insight aligns with the ROI Institute‘s comprehensive methodology, which demonstrates how value creation occurs across multiple levels, from initial reactions to long-term business impact.
Understanding the Value Chain
The journey from AI implementation to value creation follows a natural progression that successful institutions have learned to track and optimize. Take the case of DBS Bank in Singapore, which transformed its customer service operations through AI. Their success came from carefully monitoring and optimizing each stage of value creation.
The process begins with stakeholder reaction – how employees, customers, and partners initially respond to AI systems. When Australia’s Commonwealth Bank launched its AI-powered customer service platform, they discovered that early user reactions predicted long-term adoption rates with surprising accuracy. Negative initial responses from front-line staff signaled implementation challenges that, if addressed early, could be transformed into opportunities for improvement.
Learning and capability building form the next crucial link. Zhang’s comprehensive study of AI in banking demonstrates that successful institutions invest heavily in developing both technical and business capabilities. JPMorgan Chase, for example, committed to training 30,000 employees in AI fundamentals, recognizing that technical implementation alone would not drive value.
The Application Challenge
The critical transition from learning to application separates high-performing AI implementations from the rest. Goldman Sachs’ experience with AI-powered trading algorithms illustrates this challenge. Despite sophisticated technology and well-trained teams, initial returns disappointed until they focused explicitly on measuring and optimizing how traders actually used the AI tools in their daily work.
The ROI Institute’s ROI Methodology® provides specific frameworks for measuring this crucial transition. “The gap between knowing and doing represents one of the biggest risks to AI ROI”, notes Patricia Anderson. “Organizations need systematic approaches to bridge this gap, ensuring that learning translates into changed behaviors and improved processes.”
Converting Impact to Financial Value
The path from application to business impact often takes unexpected turns. Consider UBS’s implementation of AI for wealth management. While designed primarily for portfolio optimization, the system’s greatest value emerged from its ability to identify and prevent potential client departures, saving billions in assets under management.
This complexity makes converting impact to financial value challenging but crucial. The ROI Methodology) provides frameworks for monetizing benefits across multiple dimensions:
- Direct cost savings and revenue increases
- Operational efficiency gains
- Strategic value creation
- Risk mitigation benefits
HSBC’s experience implementing AI for fraud detection illustrates the importance of comprehensive value measurement. Beyond direct fraud prevention savings of $100 million annually, the system generated substantial value through improved customer trust, reduced operational risk, and enhanced regulatory compliance.
Implementation Success Factors
Even with clear frameworks for measurement, implementing AI successfully requires careful orchestration. The MIT Sloan AI Success Study(4) identifies organizational alignment as the critical factor – ensuring technology, processes, and people work in concert toward clear objectives.
Bank of America’s success with its AI-powered virtual assistant, Erica, demonstrates the importance of this alignment. By establishing clear success metrics and engaging stakeholders across the organization, they achieved both impressive adoption rates and measurable business impact.
The Ethical Dimension
The relationship between ethical AI implementation and financial returns grows increasingly clear. When a major European bank faced backlash over biased lending algorithms, the incident highlighted how ethical considerations directly impact ROI. Organizations that proactively address fairness, transparency, and privacy typically achieve higher sustained returns than those focused solely on technical performance.
Looking Ahead: The Generative AI Era
As we enter the generative AI era, measuring and maximizing ROI becomes even more critical. Early experiments with generative AI in financial services suggest its impact will be even more transformative – and harder to measure – than previous AI applications. Some institutions are already exploring new frameworks for capturing value from generative AI applications in areas like product development, risk assessment, and client communication.
Conclusion
Success in measuring and maximizing AI ROI requires striking a careful balance between rigorous measurement and practical implementation. Organizations must move beyond simplistic calculations while avoiding overly complex measurement systems that consume more value than they track.
The future belongs to institutions that can master this balance, incorporating both comprehensive measurement frameworks and practical implementation guidance. As one banking CEO recently observed, “AI’s potential is limitless, but realizing that potential requires us to be as sophisticated in measuring value as we are in creating it”.
References:
- Brynjolfsson, E., & Mitchell, T. (2022). “Measuring AI’s Business Value.” Harvard Business Review, 100(2), 86-95.
- Chen, Z., & Davidson, S. (2024). “Generative AI: New Frontiers in Value Creation.” MIT Technology Review, 127(3), 78-92.
- Kumar, V., & Chen, Z. (2023). “AI Value Creation: A Systematic Approach.” MIT Technology Review, 126(2), 45-59.
- Phillips, J. J. (2024). “What’s the ROI of AI.” Presentation materials, ROI Institute.
- Ransbotham, S., et al. (2023). “MIT Sloan AI Success Study.” MIT Sloan Management Review, Special Report.
- Williams, R., & Thompson, S. (2023). “AI ROI Measurement Systems.” Journal of Business Analytics.
- Zhang, V. (2024). “AI in Banking: A Comprehensive Study.” Financial Technology Review, 12(2), 67-82.
- Zhou, X., & Roberts, J. (2023). “The Responsible AI Index.” Journal of Digital Ethics, 8(4), 156-170.
This article was originally published here and is republished on Wealthtender with permission.
About the Author
Bill Hortz
Founder Institute for Innovation Development
Bill Hortz is an independent business consultant and Founder/Dean of the Institute for Innovation Development- a financial services business innovation platform and network. With over 30 years of experience in the financial services industry including expertise in sales/marketing/branding of asset management firms, as well as, creatively restructuring and developing internal/external sales and strategic account departments for 5 major financial firms, including OppenheimerFunds, Neuberger&Berman and Templeton Funds Distributors. His wide ranging experiences have led Bill to a strong belief, passion and advocation for strategic thinking, innovation creation and strategic account management as the nexus of business skills needed to address a business environment challenged by an accelerating rate of change.
