The Investment Banking AI Market Map

Investment banking is built on information, judgment and relationships. Bankers spend significant time identifying opportunities, preparing for client meetings, researching companies, updating models, building pitchbooks, reviewing diligence, coordinating transactions and maintaining client coverage. AI can compress much of that production work. The bigger opportunity is to connect the bank’s relationship history, transaction experience, market data, financial information and live deal materials so bankers can move from information to advice faster.

The Open Future Forum Investment Banking AI Market Map follows eight core workflows — Originate → Research → Pitch → Model → Diligence → Execute → Cover → Control — with two horizontal layers supporting the entire map: Financial & Market Data and Bank Data, Security & Compliance. This is not a vendor ranking. It is a map of where AI is entering investment banking, what work is becoming easier to automate, and where human judgment remains central.

The core question is not how many analyst hours AI can save. It is whether AI can help the bank win better mandates, execute them more effectively and give clients better advice.

What Is the Investment Banking AI Market Map?

The map tracks AI products used across deal origination, company and market research, pitchbook production, financial modeling and valuation, due diligence, M&A, ECM and DCM execution, client coverage and relationship intelligence, and compliance, confidentiality and production controls. It includes specialist finance AI products, established market-data platforms with AI capabilities, CRM and relationship systems, transaction platforms and enterprise AI infrastructure.

A vendor may span several workflows. The final map should give each company one primary placement, with secondary tags where useful. The inclusion question is: does this product materially improve an investment-banking workflow without weakening the bank’s control over source data, client information or final advice?

Five Findings

1. The real advantage is institutional memory, not model access

Most banks can access capable models. The more defensible advantage is what the bank connects to them: client relationships, prior transactions, sector knowledge, internal research, pitch history, valuation work, meeting records and banker expertise. A system that understands prior client interactions, relevant transactions and internal expertise can be more useful than a generic model with no institutional context.

2. Banking AI is moving from research assistance to deliverable creation

AI-native finance products increasingly help create banker work product: company profiles, meeting briefs, market updates, buyer lists, valuation pages, pitchbook content, CIM sections and model updates. The question becomes whether AI can produce banker-grade work that is source-linked, reviewable and suitable for a live client process. The closer AI gets to the final deliverable, the more important source control and human review become.

3. Financial-data provenance is becoming part of the architecture

Investment banking has little tolerance for unsupported numbers. A banking AI system should make it easy to answer where a number came from, which period it represents, whether it was normalized, whether the source was revised, whether the model was changed, and whether the result can be reproduced. Source-linked financial data and visible calculations are therefore not optional features — they are part of the control model.

4. AI is likely to change the leverage model before it changes the relationship model

AI is well suited to work traditionally performed by analysts and associates: company profiles, market research, meeting preparation, historical spreading, comparable-company analysis, precedent transactions, pitchbook assembly, diligence summaries and model updates. Senior work is harder to compress: winning mandates, reading management teams, positioning transactions, persuading boards, negotiating, judging timing and maintaining trust. The likely change is not the disappearance of investment bankers. It is a different division of work between junior production and senior advice.

5. AI creates a new diligence burden when the company being sold is itself AI-driven

Investment banks advising AI companies face an additional problem: the transaction story has to reconcile customer usage, revenue recognition, delivery cost, model and infrastructure cost, gross-margin sensitivity, customer ROI, vendor concentration and product defensibility. Open Future Forum’s October 2026 Investment Banking AI Report focuses on this issue as a transaction-focused synthesis of operator evidence, not a survey of investment bankers. For IPO, M&A and financing work, growth alone is not enough — bankers increasingly need to understand how the economics behave as usage scales.

The OFF Banking Production Standard

Before AI-generated work reaches a client, it should meet five standards.

Source

Can every material fact and number be traced?

Accuracy

Has the work been checked against authoritative information?

Model Integrity

Are formulas, assumptions and changes visible?

Review

Has an appropriate banker reviewed the output?

Accountability

Is someone clearly responsible for the final client-facing work?

This standard should be especially strict for valuation, transaction analysis, forecasts, financing consequences, public offering materials and client recommendations.

The Investment Banking AI Market Map

The map groups vendors into eight banking workflows. This is not a ranking, and inclusion does not imply endorsement. Control is read here through its practices rather than a named vendor table in this edition — see the Vendor Evidence Standard for how a fuller vendor review would extend this map.

1. Originate — Deal origination and opportunity intelligence

Identifying potential clients, tracking strategic activity, finding acquisition targets, monitoring sponsor activity, mapping sectors, identifying financing needs, relationship mapping, detecting reasons to call.

How can AI improve investment-banking origination?

AI can combine company data, transaction history, market signals and relationship information to identify situations that deserve senior banker attention. The useful output is not a longer target list — it is an actionable answer to who should be called, why now, and who inside the bank has the strongest route in.

What should banks measure?

Qualified opportunities, senior meetings generated, pitch conversion, mandates won, proprietary origination and time required for market mapping.

Primary value: Mandate pipeline · Coverage quality · Banker leverage

2. Research — Company, industry and capital-markets intelligence

Company research, filings, earnings, sector intelligence, market developments, private-company data, investor information, transaction history, capital-markets conditions.

What are the best AI research tools for investment bankers?

The answer depends on the research problem. Market-data platforms remain important for authoritative financial data. AI-native products are useful where bankers need to combine filings, research, private data, internal materials and live transaction information. The critical tests are source quality, freshness, citation, data completeness, internal-data integration and permissions.

Primary value: Research speed · Information coverage · Source quality

3. Pitch — Pitchbooks, client materials and meeting preparation

Company profiles, industry updates, credentials, buyer lists, investor universes, public comps, precedent transactions, transaction alternatives, meeting briefs, pitchbook drafting.

Will AI automate investment-banking pitchbooks?

A substantial portion of pitchbook production can be automated. AI can gather source information, update repetitive pages, draft narrative and populate valuation or market content. The banker still decides what the client should do, why the timing matters, which alternatives deserve attention, which buyer is credible, which valuation framing is defensible and which message should lead.

Primary value: Pitch speed · Analyst capacity · Consistency

4. Model — Financial modeling, valuation and transaction analysis

Historical financial extraction, spreading, model updates, public-company comps, precedent transactions, valuation, scenario analysis, accretion and dilution, merger models, capital structure, financing analysis.

Can AI build investment-banking financial models?

AI can increasingly populate, update, extend and test models. Near-term strengths include historical data, model population, KPI extraction, earnings updates, scenario generation, formula assistance and error detection. Complex transaction models still require banker review. The more material the valuation or transaction consequence, the higher the review standard should be.

Primary value: Modeling speed · Data accuracy · Scenario coverage

5. Diligence — Transaction diligence and document intelligence

VDR review, CIM analysis, KPI extraction, customer and supplier analysis, contract review, risk identification, diligence Q&A, management preparation.

How can AI improve M&A diligence?

AI can reduce the first-pass burden of reading and comparing large amounts of transaction material. Useful systems should link answers to source documents, preserve permissions, compare information across files, identify inconsistencies, surface missing information and draft diligence questions. The bank should distinguish clearly between extracted fact, AI interpretation and banker conclusion — those are not the same thing.

Primary value: Coverage · Speed · Issue identification

6. Execute — M&A, ECM, DCM and transaction execution

Investment-banking execution extends beyond M&A. In M&A, AI can support buyer tracking, process updates, bid comparison, Q&A and VDR management. In ECM, AI can support IPO preparation, investor targeting, equity-story support, market-window monitoring and comparable issuance. In DCM, AI can support financing alternatives, debt comps, capital structure, investor materials and market conditions. More generally, AI can support timelines, outstanding items, client updates, document management and meeting preparation.

How can AI improve transaction execution?

AI can reduce information-transfer work across a live process: summarizing developments, tracking open questions, comparing alternatives and maintaining transaction context. The point is not to automate negotiation. It is to keep senior bankers focused on decisions where judgment and client influence matter.

Primary value: Execution speed · Process visibility · Senior banker capacity

7. Cover — Client coverage and relationship intelligence

CRM intelligence, interaction history, relationship mapping, meeting preparation, contact intelligence, follow-up, client signals, sector coverage planning.

How can AI improve investment-banking client coverage?

AI can help answer when the bank last spoke with a client, what management cared about, which colleagues know the client, what strategic events have occurred, which deals or financing alternatives are now relevant, and what the best reason to engage is. The objective is better human interaction, not automated relationship management.

Primary value: Client relevance · Relationship continuity · Mandate conversion

8. Control — Confidentiality, supervision and production controls

Investment banks operate under strict information constraints. AI systems may interact with MNPI, confidential client information, restricted information, internal research, deal documents, personal data and financial models. Controls may include information barriers, permissioning, approved models, data retention, logging, human review, vendor controls, restricted-list rules and audit trails.

What are the biggest AI risks for investment banks?

Major risks include confidential information entering uncontrolled models, unsupported numbers reaching clients, inappropriate information crossing internal barriers, hallucinated transaction facts, incorrect source attribution, insufficient records and overreliance on automated analysis. The strongest architecture incorporates banking controls from the beginning rather than layering them on later.

Primary value: Confidentiality · Supervision · Defensibility

Financial & Market Data Layer

Company financials, private-company data, filings, transaction data, pricing, capital markets, ownership, investor information, sector intelligence.

The quality of banking AI is constrained by the quality, timeliness and provenance of the financial data underneath it.

Bank Data, Security & Compliance Layer

The second horizontal layer includes CRM, internal research, deal history, email and communications, permissions, identity, information barriers, compliance records and approved model access. This is where the bank’s institutional advantage and institutional risk often meet.

AI and Investment-Banking Economics

The strategic question is not simply whether AI improves productivity. It is how that productivity changes the economics of the franchise. AI may affect deal-team size, analyst-to-MD ratios, number of clients covered, pitch volume, cost per mandate, execution capacity, fee pressure and junior training. A bank that saves analyst time but changes nothing else may realize limited economic value. The benefit becomes more tangible if the bank can cover more clients, win more mandates, execute more deals, run leaner teams, increase senior-client time and reduce production cost.

How should investment banks measure AI ROI?

Measure mandate conversion, pitch turnaround, banker capacity, transaction throughput, execution cycle time, data quality, error rates, outside-data or production cost, actual cash savings, and revenue attributable or credibly influenced. Do not treat analyst hours saved automatically as cash savings.

AI and the Junior Banker Model

Investment banking has historically trained bankers through production work. That model comes under pressure if AI performs more research, spreading, comps, pitchbook assembly, model updates and diligence summaries. Banks still need junior bankers to learn accounting, valuation, financial judgment, transaction mechanics, presentation, client communication and process management. If AI removes part of the traditional apprenticeship workload, banks need a more deliberate development model.

AI-Company Transaction Diligence

Bankers advising AI companies need an additional diligence lens. Open Future Forum’s October Investment Banking AI Report focuses on revenue quality, delivery cost, margin sensitivity and disclosure, and makes clear that its source populations are separate and that it is not a survey of investment bankers.

Revenue

How is revenue priced and recognized?

Usage

Does customer usage support revenue assumptions?

Delivery Cost

What happens to inference, compute and infrastructure cost as usage grows?

Margin

How sensitive is gross margin to model prices, compute, vendor mix and customer usage?

Customer Economics

Can customers demonstrate why the product is worth the spend?

Concentration

How dependent is the company on one model provider, one cloud, one customer or one distribution channel?

Defensibility

What is proprietary — data, workflow, distribution, product, customer integration or model IP?

Why does this matter?

Because these assumptions ultimately affect valuation, forecasts, disclosure, investor diligence, buyer diligence and transaction credibility.

Build, Buy or Integrate?

Investment banks should not assume every AI capability should be built internally.

Build

Build where advantage depends on proprietary relationships, deal history, internal research, bank-specific workflows or highly sensitive information.

Buy

Buy where the workflow is standardized, external data matters, mature specialist products exist, or speed matters more than differentiation.

Integrate

Integrate where the best outcome comes from combining specialist products with proprietary bank data. For many banks, integration may be the most important category. The strongest system may not be one platform — it may be a controlled layer connecting market data, transaction systems, CRM, models and approved AI.

What Should Banks Automate First?

Strong early candidates tend to have high repetition, reliable sources, measurable review time, low ambiguity and straightforward human checking — company profiles, meeting preparation, market updates, historical spreading, repetitive pitchbook pages, initial buyer lists and diligence extraction. Move more cautiously where AI output directly affects valuation, fairness-related analysis, transaction recommendations, disclosure or binding client communication.

What Investment Banks Should Not Do With AI

• Do not allow unsupported numbers into client materials.
• Do not connect confidential deal information to uncontrolled models.
• Do not confuse faster pitch production with better advice.
• Do not accept model outputs without inspecting the underlying financial logic.
• Do not let every sector or product team build a separate AI stack.
• Do not measure ROI only in analyst hours.
• Do not institutionalize poor internal data simply because AI can retrieve it.
• Do not remove junior work without redesigning junior training.

Investment Banking AI: Key Questions Answered

What are the best AI tools for investment bankers?

The strongest products differ by workflow. Origination, research, pitches, modeling, diligence, execution and coverage require different capabilities.

How is AI used in investment banking?

For origination, research, pitchbooks, models, diligence, M&A and capital-markets execution, client coverage and compliance.

Can AI create investment-banking pitchbooks?

AI can automate substantial first-pass production, but banker judgment still determines the recommendation and narrative.

Can AI build financial models?

AI can increasingly populate, update and test models, but transaction analysis still requires human review.

How can AI improve deal origination?

By connecting company, transaction, relationship and market data to identify more relevant reasons for senior bankers to engage.

How can AI improve M&A diligence?

By extracting facts, comparing documents, identifying inconsistencies and preparing source-linked questions.

Can AI help ECM and DCM bankers?

Yes. AI can support market monitoring, comparable issuance, investor materials, financing alternatives and execution coordination.

Will AI reduce investment-banking headcount?

It is likely to reduce repetitive production work, but the larger effect may be changes in leverage, coverage capacity and junior roles.

How will AI change junior bankers?

Junior bankers may perform less repetitive production work and need more deliberate training in judgment, modeling, transaction mechanics and client communication.

Can AI replace investment bankers?

It can automate more production work than senior judgment, negotiation, relationship management and client influence.

What are the biggest AI risks for investment banks?

Confidentiality, MNPI leakage, unsupported numbers, information-barrier failures, hallucinations and weak auditability.

What creates an AI advantage for an investment bank?

Connecting proprietary relationship and transaction history with trusted external financial and market data.

How should banks measure AI ROI?

Through mandate conversion, banker capacity, transaction throughput, execution speed, quality and cash cost, not just hours saved.

What should banks automate first?

High-volume work with reliable sources, clear checking and limited downside from correctable errors.

How should banks evaluate AI vendors?

Evaluate source quality, financial accuracy, confidentiality, permissions, auditability, workflow fit and integration with bank systems.

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What OFF’s Existing Research Adds

This market map is the supply-side view. Open Future Forum’s existing Investment Banking AI Report examines a different question: how bankers should evaluate the revenue quality and delivery economics of AI companies during transactions. Its methodology explicitly states that it is not a survey of investment bankers and that its founder, common-instrument and AI Leaders evidence comes from separate populations. That distinction should remain explicit: the market map shows how AI changes investment-banking workflows; the Investment Banking AI Report examines how AI changes the companies bankers are underwriting and selling. Together they address both sides of the market.

Methodology and Disclosure

Vendor Evidence Standard

A fuller market map should be backed by a master evidence table recording, for every vendor: current company name, exact product, primary workflow, specific documented AI capability, banker (analyst, associate, VP, MD, execution, coverage, compliance), vendor type (finance AI, data, CRM, transaction, or infrastructure), evidence status (documented, publicly demonstrated, announced, or limited public evidence), official source and date checked. This edition names representative vendors by category; the full per-vendor evidence table is pending a future data pass, and no vendor here should be read as scored, ranked or formally endorsed in the meantime. A company should not appear simply because it serves financial institutions — its AI capability should be documented and relevant to an actual investment-banking workflow.

Vendor Inclusion Note

Vendor inclusion is editorial. The map is based on public information. No vendor paid for placement. Categorization is not an endorsement. The map does not score, rank, or evaluate vendor quality. During source review, no vendor in the map was identified as an IA Seed Ventures portfolio company or as a Murray Newlands advisory client.

About Open Future Forum

Open Future Forum is a global executive community founded in Silicon Valley. Its network reaches tens of thousands of executives and investors worldwide. It runs a year-round calendar of events for senior executives and investors, including CEOs, CFOs, CMOs, CISOs, General Counsel, private equity leaders, founders, and AI leaders, through Forum Select, its invite-only private gatherings, and Forum Events, its open panels and gatherings. Beyond events, Open Future Forum convenes peer groups and executive boards and publishes original research built on first-party survey and qualitative data from its executive network. The Investment Banking AI Market Map is a companion to the Investment Banking AI Report, alongside the CEO, CFO, CMO, CISO, AI Leaders, Private Equity and General Counsel AI Market Maps and the wider Enterprise AI Buying & Budget Index.

External vendor descriptions reflect public product positioning reviewed during source research. They are not affiliated with this report and do not endorse it.