Will AI and ChatGPT Replace Investment Banking Analysts?
How Automation and Advanced Tools Reshape Presentations, Modeling, and Deal Efficiency
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, Last Updated :
Mar 12, 2025
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Investment banking often conjures images of 80- to 100-hour workweeks filled with intricate financial models and endless pitchbooks. Rapid advances in artificial intelligence, however, are sparking conversations around how AI-powered platforms could reshape this demanding field. Tasks that once consumed entire days may be handled with greater speed and precision, raising critical questions about how job responsibilities will evolve. From slide creation to financial forecasting, AI tools promise new efficiencies while also driving higher expectations for output quality and deal velocity. Over the next five years, investment banking may shift toward increasingly automated processes, yet the underlying demands of perfection and intense client service are likely to remain.
Presentation creation has long been a labor-intensive endeavor for analysts in investment banking. Pitchbooks typically require data on market trends, valuation metrics, and potential deal structures, all packaged in a consistent, polished format. The adoption of AI-driven tools stands to reduce repetitive tasks and enhance overall productivity.
Most pitchbooks share a common template, featuring market updates, client overviews, and detailed valuation sections. Traditionally, assembling these pieces could take hours per company profile, often replicated across multiple versions of the same deck. AI solutions are now emerging to generate slides automatically based on existing references.
Common Pitchbook Components:
Company profiles (e.g., background, financial highlights)
Trading and transaction comps
Valuation summaries (DCF, LBO, or other models)
Potential buyer or seller lists
⭐Tip: Where possible, centralize all data—such as financial statements, press releases, and stock performance—into a single database. AI tools thrive when they can pull from consistent, organized sources.
With technology capable of scanning old presentations and importing new data, bankers can significantly reduce manual slide creation. As AI streamlines common pitchbook sections, teams can shift attention toward higher-level analysis and client interaction. Yet, this same efficiency may drive demand for more frequent updates and new variations of the materials.
2. Streamlining Financial Modeling with AI Tools
Financial modeling is another focal point of AI in investment banking. Analysts regularly build Discounted Cash Flow (DCF) models, leveraged buyout (LBO) analyses, and comparative valuation exercises. Although the fundamentals remain the same, AI platforms promise to automate data gathering and preliminary calculations.
Street-Based vs. Management-Based DCFs
A street-based DCF relies heavily on external equity research estimates, which can be relatively straightforward to integrate. Meanwhile, a management-based DCF draws on complex internal data that vary significantly from one organization to another. AI shows considerable potential in streamlining simpler models, while more intricate forecasts may still require hands-on oversight.
Advantages of AI-Enhanced Modeling:
Faster incorporation of industry growth rates and competitive benchmarks
Automated retrieval of stock price history and key financial metrics
Real-time error checks, flagging discrepancies across different worksheets
For more basic tasks—like plugging in updated revenue forecasts for a well-known public company—analysts can direct AI to retrieve and integrate data automatically. Yet truly bespoke models, especially when a client’s spreadsheets are riddled with inconsistent figures or incomplete tabs, demand additional human intervention.
3. AI’s Influence on Research and Deal Management
While presentations and financial modeling dominate the analyst’s workload, research and deal management also stand to benefit from AI. Large language models and advanced machine learning platforms can digest enormous volumes of data in seconds. This capability has far-reaching implications for tasks that typically require detailed examination and cross-referencing.
Potential Applications of AI in Research:
Rapid scanning of financial filings, press releases, and industry reports
Natural language processing for identifying key trends or competitive insights
Real-time updates on relevant market-moving news, consolidated into summaries
📌Example: An AI tool might scrape ten years of SEC filings for multiple companies, highlight recurring risk factors, and present them in a concise list. Analysts then focus on deeper strategic interpretation, rather than manual data extraction.
In deal management, AI can streamline tasks such as:
Automating parts of the due diligence process, including document categorization
Tracking changes in regulatory or compliance requirements
Managing communications with advisors and counterparties through centralized platforms
Though automation eases administrative burdens, the human element remains indispensable for interpreting data, negotiating contracts, and forming client relationships.
4. Overcoming Challenges with Complex Internal Models
Many large companies maintain detailed financial forecasts that incorporate thousands of data points. These models can be inconsistent, featuring multiple tabs without uniform logic or interconnected formulas. AI excels at pattern recognition and data aggregation, but interpreting a chaotic spreadsheet often requires human judgment.
Analysts typically invest significant time reviewing each formula and referencing older versions to verify assumptions. AI may flag discrepancies—like mismatched revenue figures in separate tabs—but the final call rests with skilled professionals who understand the business context.
Persistent Complexities in Internal Forecasts:
Hardcoded data that does not update with market changes
Tabs that contradict each other in revenue and expense calculations
Minimal documentation of how assumptions are derived
🔍Definition: “Management-based DCF” refers to a valuation model grounded in a client’s internal forecasts, which can vary significantly from standard Wall Street or equity research estimates. These models often require manual scrutiny to ensure consistency.
As AI matures, it may eventually decode these tangled webs more effectively. In the next five years, though, bank analysts will likely still shoulder the responsibility of cleaning and consolidating large, messy datasets before building final valuation models.
5. Charting the Next Five Years of AI in Investment Banking
Over the near term, AI’s main contribution to investment banking lies in accelerating routine tasks and enabling deeper analytical insights. Whether it’s automating pitchbook slides, fetching industry benchmarks for a valuation analysis, or expediting due diligence, these tools can significantly enhance productivity.
Yet, the demands of perfection, speed, and quantity suggest that analyst hours may not see a dramatic reduction. Historically, each wave of automation has led to broader, more frequent project work rather than lighter individual workloads. Nonetheless, the nature of the analyst’s role will continue to evolve, likely with a stronger focus on high-level reasoning and client-facing responsibilities.
Forecast for AI Adoption in Banking:
Expanded reliance on AI for data retrieval and simple model setup
Gradual incorporation of machine learning in due diligence and compliance tasks
Heightened expectations for quick turnaround on deliverables, as teams handle more concurrent projects
The Bottom Line
AI is poised to transform the workflow of investment banking analysts by automating tasks like presentation creation, simple financial modeling, and targeted research. These innovations can reduce the tedium of preparing repetitive slides and combing through lengthy reports. In turn, analysts may spend more time on strategic work—whether it involves detailed negotiations, big-picture valuation analysis, or in-depth client discussions.
Still, the industry’s emphasis on precision, tight deadlines, and an ever-growing deal pipeline suggests that long hours will persist. The next five years promise more productive tools, but the core demands of M&A and capital markets—accuracy, responsiveness, and breadth of coverage—are unlikely to diminish. As AI capabilities expand, so will expectations for faster, more comprehensive support. In short, technology will change the nature of the job, yet the commitment and intensity defining investment banking will likely endure.
