Every Managing Director evaluating an AI platform eventually asks the same question.
Not "does it work?" The demos answer that. The question that matters is simpler and harder at the same time: "What do we actually get back for what we put in?"
In an industry built on return analysis, it is the right question. And in 2026, for the first time, there is enough real-world deployment data across enough institutions to answer it properly.
The Headline Numbers
Start with the figures that frame the scale of what is happening.
McKinsey estimates generative AI could create $200 to $340 billion in annual value for global banking through productivity improvements and automation. Deloitte's analysis of the top 14 global investment banks projects AI could lift front-office productivity by 27 to 35%, translating to an additional $3 to $4 million in revenue per banker annually. Goldman Sachs has internally modeled AI absorbing work equivalent to 200 junior banker roles across specific workflow categories.
These are not projections about theoretical potential. They describe deployments already underway at institutions already measuring the returns.
Where the ROI Is Being Generated
Understanding the return on AI in investment banking requires breaking it down by workflow, because the ROI is not uniform across the deal lifecycle. It concentrates in specific places.
Research and Screening
The Deal Origination Benchmark Report found PE firms using traditional sourcing processes see only 16.5% of relevant deals in their target markets. AI-powered screening tools expand that coverage dramatically, analyzing hundreds of targets simultaneously against financial metrics, strategic fit indicators, and ownership profiles pulled from live data sources.
The return here is not just efficiency. It is opportunity capture. Every relevant deal that surfaces before the window closes is a potential mandate that a traditional process would have missed entirely.
Due Diligence
This is where the ROI calculation becomes most concrete. A mid-market due diligence process running on traditional workflows consumes three to four weeks of associate time across multiple workstreams. AI document intelligence compresses that to days.
At a fully-loaded cost of $200,000 to $350,000 per year for a junior associate, three weeks of that resource deployed on document review that AI could handle represents a direct and measurable cost per transaction. Multiply that across a firm's annual deal volume and the number becomes significant very quickly.
Financial Modeling
AI financial research tools automate data extraction, model population, and scenario analysis, compressing a process that traditionally takes two to three days of analyst time into hours. At deal teams running 15 to 20 transactions annually, the cumulative time compression across the modeling workstream alone generates returns that justify platform costs many times over.
Pitch Book and Deliverable Production
Pitch book production has historically consumed some of the highest-cost hours in investment banking relative to the strategic value they generate. Senior associates spending three days formatting slides and populating data is an expensive workflow for a firm charging advisory fees at the top of the market. AI drafting tools absorb the mechanical layer, returning those hours to client-facing and strategic work.
The FTI Data Point That Changes the Conversation
FTI Consulting's 2026 Private Equity AI Radar adds the dimension that reframes every ROI conversation: 95% of PE funds report their AI initiatives are meeting or exceeding their original business case.
That figure is extraordinary in context. Enterprise technology deployments in financial services have historically produced years of ambiguous results before generating clear ROI signals. The fact that AI in investment banking has crossed the threshold this quickly, and at this consistency rate across institutions, tells you something structural about where the value is concentrated.
This is not a technology that requires years of refinement before delivering returns. The returns are arriving in the first deployment cycle.
The Cost of Not Adopting
ROI analysis in investment banking automation has a second side that most evaluations underweight: the cost of standing still.
If a competitor's deal team completes due diligence in four weeks while yours takes six, you are not just slower. You are arriving at a binding offer with less time to negotiate, less information to leverage, and a weaker position in a seller's process. In a competitive auction, that gap is not neutral. It costs mandates.
The firms not adopting AI are not holding their position. They are ceding ground on coverage capacity, screening depth, and deal execution speed simultaneously, to competitors who are compounding their advantage with every transaction they close.
What Brexy Delivers on the ROI Equation
For firms doing this calculation seriously, Brexy makes the numbers concrete.
Teams using Brexy evaluate five times more deals with the same headcount. That is not a productivity metric. It is a revenue capacity metric. Five times more deals evaluated means five times more mandates in the pipeline, more transactions at the term sheet stage, and more closed deals generating fees.
Initial investment memos produced in under two minutes at 98.5% accuracy on financial benchmarks means the hours that used to go into memo production go into client relationships and deal strategy instead. With 30-plus live integrations across Bloomberg, FactSet, PitchBook, Capital IQ, and Datasite, Brexy connects to existing infrastructure rather than creating parallel workflows that consume the time they were meant to save.
The ROI of AI deal execution at the platform level is not abstract. It shows up in deals evaluated, mandates won, and analyst hours returned to the work that actually generates revenue.
