The ROI of Generative AI in Investment Banking: What CXOs Should Expect

The rise of Generative AI in investment banking is redefining what’s possible, promising both radical efficiency and new avenues for value creation. As financial institutions race to stay ahead, CXOs are increasingly confronted with a pivotal question: What is the real return on investment (ROI) of generative AI in investment banking, and what should leaders expect as they embark on this transformation?

This comprehensive blog unpacks the ROI of generative AI in investment banking, blending statistics, real-world examples, and practical insights for leaders ready to explore the next frontier of AI-powered banking. Whether you’re a CXO planning your AI roadmap or a decision-maker seeking tangible outcomes, this guide is your blueprint for success.

The Current State of AI and GenAI ROI

AI and Gen AI are rapidly reshaping the priorities of finance leaders worldwide. After years spent experimenting through pilots and proof-of-concept projects, organizations are now moving these technologies into large-scale deployment, transforming functions such as accounting, treasury, financial planning, mergers and acquisitions, and more.

The enthusiasm is undeniable, and the budgets are increasing accordingly. Yet one challenge persists: turning that promise into tangible, measurable returns.

In March 2025, BCG’s Center for CFO Excellence conducted a comprehensive survey of more than 280 senior finance professionals from large global enterprises actively using AI and GenAI within their finance functions. This groundbreaking research provided one of the first detailed, data-driven insights into the true ROI of these technologies in finance, pinpointing which strategies and applications are delivering results.

Organizations that are getting AI and GenAI right are doing things differently. Rather than experimenting without direction, they prioritize delivering value from day one. They approach AI adoption as an enterprise-wide transformation rather than a collection of isolated use cases. They forge close partnerships with IT teams and trusted vendors instead of relying solely on internal resources. And they roll out initiatives in carefully planned phases, capturing value step by step.

Ultimately, the finance teams seeing the highest returns recognize that success with AI and GenAI depends on more than just creating impact, ensuring that the return justifies the time, resources, and energy invested. For leaders determined to get it right, this is the practical roadmap for turning ambition into tangible, measurable results.

What is Generative AI in Investment Banking?

Generative AI refers to advanced machine learning models, such as large language models (LLMs), that can generate new content, automate complex tasks, and deliver insights by analyzing massive datasets. In investment banking, generative AI is deployed for:

• Automating pitchbook creation and financial modeling
• Enhancing risk analysis and compliance
• Personalizing client interactions and investment advice
• Detecting fraud in real time
• Streamlining due diligence and deal origination

Why Investment Banking Needs Generative AI Now More Than Ever

Traditionally, investment banks have relied heavily on vast teams of analysts, research specialists, and high-frequency traders to parse mountains of data, predict market movements, and craft complex financial instruments. But even the sharpest minds hit human limits when faced with the data deluge of today’s markets.

Generative AI in investment banking offers a breakthrough: AI models that don’t just analyze data but create, drafting reports, generating scenario analyses, synthesizing market research, and even stress-testing investment hypotheses.

A McKinsey report estimate that AI could deliver up to $1 trillion of additional value yearly in global banking. And within that, Generative AI is quickly emerging as the crown jewel for tasks requiring deep domain knowledge and content creation.

Why ROI Matters for CXOs

For CXOs, adopting generative AI in investment banking is not just about technology, it’s about measurable impact. ROI is the north star that guides investment decisions, resource allocation, and strategic planning. The right generative AI initiatives can:

• Boost productivity and revenue
• Reduce operational costs and manual errors
• Strengthen risk management and regulatory compliance
• Enhance client satisfaction and retention

Of course, the exact ROI will vary depending on:

• Data quality and integration readiness
• Workforce upskilling
• Regulatory and ethical guardrails
• Vendor partnerships and AI governance

But how do you quantify these benefits? Let’s dive into the ROI drivers and supporting data.

Quantifying the ROI: Where Do the Gains Come From?

Let’s break it down. CXOs evaluating the ROI of Generative AI in investment banking should focus on three primary dimensions:

1. Operational Efficiency and Cost Savings

• Automated Research Reports: Generative AI can produce high-quality draft equity research reports in seconds, allowing human analysts to focus on judgment calls rather than rote work.
• Deal Documentation: Drafting pitch books, financial models, and legal documents, once a tedious manual task, can be accelerated by up to 70% with generative AI copilots.
• Compliance Automation: Generative AI can summarize regulatory changes and generate compliance checklists, cutting hours of manual review.

Goldman Sachs predicts that AI-driven automation could boost banking productivity by up to 30%, potentially saving billions in labor costs annually.

2. Revenue Acceleration

Generative AI’s capacity for scenario generation and real-time data synthesis can unlock more accurate forecasts and better client advisory services.

For example, banks using Generative AI can:

• Generate multiple M&A deal scenarios at speed.
• Produce customized client reports in real-time.
• Enhance client engagement with hyper-personalized recommendations.

JPMorgan Chase has piloted Generative AI models to help investment bankers draft pitch materials for corporate clients rapidly. The bank reported a time reduction of over 40% in preparing these documents, translating into more deals closed faster.

3. Innovation and Competitive Advantage

Generative AI also fuels entirely new value streams:

• Designing bespoke financial products tailored to niche client segments.
• Powering AI-based advisory chatbots for institutional clients.
• Creating AI co-pilots that work alongside bankers during negotiations.

Morgan Stanley recently deployed OpenAI’s GPT-powered assistant to help its wealth management advisors find investment research faster. The result? Enhanced advisor productivity and more time for client-facing interactions, a clear competitive edge.

4. Enhanced Risk Management

Generative AI enables more accurate risk assessment by processing vast datasets and identifying patterns that human analysts might miss. This leads to better-informed lending and investment decisions, reducing losses and increasing ROI.

5. Client Experience and Personalization AI-driven personalization helps banks deliver tailored investment advice and services, boosting client satisfaction and loyalty.

The Intangible ROI: Risk Reduction and Talent Uplift

The value of Generative AI in investment banking isn’t just in cost-cutting. It’s also about managing risk more proactively:

• AI models can simulate macroeconomic scenarios faster than traditional models.
• They help banks identify hidden patterns and early warning signs in portfolios.
• Automating grunt work enables human talent to focus on strategic, relationship-driven tasks, which are the fundamental revenue drivers.

This balance between automation and augmentation will define future-ready investment banks.

Statistics: The Business Case for Generative AI

Real-Time Success: Who’s Winning with Generative AI?

JPMorgan Chase

The bank’s COIN platform (Contract Intelligence), an early precursor to Generative AI, analyzes legal documents and extracts critical data points in seconds, saving 360,000 hours of lawyer time annually. Building on this, their pilots with GPT-like models for pitch materials hint at even greater gains ahead.

Morgan Stanley’s AI Assistant

By giving its wealth advisors a GPT-powered co-pilot, Morgan Stanley saves time and enhances client service quality. Advisors get instant answers instead of combing through countless PDFs.

Overcoming the ROI Pitfalls

CXOs must watch out for these common ROI blockers:

• Siloed Data: Generative AI feeds on high-quality, integrated data. Without it, output quality suffers.
• Hallucinations: Generative AI can produce plausible but incorrect outputs, requiring human validation loops.
• Regulatory Scrutiny: Any AI-generated content must comply with strict disclosure and compliance standards.
• Talent Gaps: Effective AI adoption demands upskilling bankers to work alongside AI co-pilots.
• Value Realization: A BCG study found that the median reported ROI is 10%, with top performers achieving 20% or more. The difference lies in focusing on value-driven use cases, broad transformation, and strong IT-vendor collaboration

How Should CXOs Get Started?

Identify High-Impact Use Cases

Start with repetitive, content-heavy tasks: research reports, deal documentation, compliance summaries.

Build Strong Data Foundations

Quality data pipelines are non-negotiable. Clean, integrated data is fuel for high-ROI AI.

Pilot, Measure, Scale

Run pilot projects with clear KPIs. Measure time savings, revenue uplift, and risk impact. Scale successful pilots enterprise-wide.

Upskill Teams

Generative AI isn’t about replacing talent; it’s about elevating it. Invest in training bankers to work with AI co-pilots.

Final Thoughts: The Real ROI? Becoming Future-Ready.

The real ROI of Generative AI in investment banking is not just about faster pitch decks or cheaper compliance. It’s about transforming how banks operate, deliver value, and compete in a market where milliseconds matter and insights drive billions.

At Indium, we empower investment banks to unlock the full potential of Generative AI by designing, building, and deploying secure, domain-specific AI solutions that automate research, streamline deal documentation, and supercharge decision-making. From rapid proof-of-concepts to enterprise-scale AI governance, our Data & AI experts help leading banks modernize workflows, ensure compliance, and drive measurable ROI with next-gen Generative AI in banking.

So, the message is clear for CXOs: The window to experiment is closing fast. Leaders who act now will set the pace for the next decade.

FAQs: Generative AI in Investment Banking