How Investment Banks Use AI in 2026: Resume Tips for the New IB

The AI landscape in IB in 2026 breaks into four categories: deal screening tools, document intelligence, pitch and presentation AI, and valuation/modelling automation. Here is what is actually deployed at banks, not just what is being piloted.

Deal Screening and Target Identification

The most mature AI application in IB is automated deal screening. Banks and their corporate clients use AI tools to scan hundreds of potential acquisition targets, strategic partners, or counterparties against a defined set of criteria — sector, revenue size, EBITDA margin range, geographic presence, and strategic fit signals. Tools in this space include:

• Kensho (S&P Global) — Used by Goldman Sachs and other capital markets firms to automate market data analysis, generate structured financial summaries, and identify pattern signals in transaction data
• Capital IQ's AI-augmented search — S&P Global has integrated AI screening directly into Capital IQ, allowing analysts to run natural language queries that return screened target lists without manual filtering
• Dili and Luminance — AI-powered M&A data room tools that automate document review during due diligence, flagging anomalies in contracts, identifying missing representations and warranties, and summarising key legal terms
• AlphaSense — NLP-powered research tool widely used in IB research teams for earnings call analysis, peer comparison, and monitoring news flow across coverage universes

Document Intelligence and Due Diligence Automation

Due diligence document review is one of the highest-effort, lowest-judgment tasks in M&A. Banks are deploying AI tools to handle first-pass review:

• AI contract analysis (Luminance, Kira Systems, Diligent Contracts) can review thousands of contracts and extract material terms in hours rather than weeks
• LLM-based tools allow analysts to query a data room as if querying a database: "What are the change-of-control provisions in all material contracts?" returns structured answers rather than requiring manual search
• JP Morgan's proprietary intelligent document processing systems automate extraction from financial statements, board minutes, and regulatory filings
• At Goldman Sachs, analysts report using the GS AI Platform — built on LLM infrastructure — to summarise prospectuses, research, and regulatory filings in minutes

Pitch Book and Presentation Generation

Pitch book automation is the most visible AI change for junior analysts. The traditional workflow — build a comp table, paste it into a slide, format to house standards, repeat for 40 slides — is being transformed:

• Tools like Gamma, Tome, and internal bank equivalents generate slide drafts from structured financial data
• Microsoft 365 Copilot, deployed at Barclays, Deutsche Bank, and Nomura among others, writes first drafts of IB commentary, generates slide summaries, and auto-reformats presentations
• Goldman Sachs Analytica (internal) and Morgan Stanley's AI tools can convert Excel model outputs directly into formatted slide decks with house branding

This does not eliminate the analyst's role in pitch books — it eliminates the formatting work and moves the analyst's value to the quality of the underlying analysis and the judgment about what story to tell.

Financial Modelling Assistance

Excel Copilot and similar tools now assist with formula writing, sensitivity analysis setup, and error detection. More materially:

• Python-based modelling tools are increasingly used by banks' quantitative analysts and technology teams to build automated valuation workflows that surface comparable transaction data, auto-populate DCF inputs from earnings estimates, and run scenario analysis at scale
• AI-assisted error detection scans Excel models for formula inconsistencies, circular references, and hardcoded values that should be variable — a genuine time-saver in 200-tab LBO models

Understanding what has changed in the day-to-day tells you what to emphasise on your CV. The shift is not from "doing work" to "supervising AI." It is a reallocation of where analyst time goes within the same overall workload.

What AI has reduced:

• Time spent manually building comp tables from scratch — AI can draft a initial set of comparables from a criteria definition in minutes; analysts verify and refine
• First-pass document review in due diligence — AI handles the extraction; analysts assess the findings
• Boilerplate slide formatting — the visual assembly work is increasingly automated
• Initial research aggregation — reading 50 analyst reports to find key themes is now done with AI summarisation tools

What AI has increased:

• Expectation of analytical depth — because data gathering is faster, the expectation is that the analysis built on that data is more sophisticated
• Speed requirements — if AI can build a comp table in 5 minutes, the MD expects it in 10 minutes with quality checks, not tomorrow
• Precision and accuracy expectations — AI-generated work requires verification; analysts are accountable for errors in AI-assisted outputs
• Creative contribution to the strategic narrative — pitch books that used to take 12 hours to build now take 5, but the quality bar for the reasoning and positioning has increased proportionally

The judgment gap is where analysts still win:

AI cannot currently replicate: sector-specific pattern recognition built through years of reading credit agreements, valuation nuance for complex situations (distressed assets, cross-border transactions with multiple jurisdictions, highly illiquid securities), relationship-based deal sourcing, structuring creativity for bespoke transactions, or the ability to push back credibly on a client's assumptions.

This means your CV should reflect two things simultaneously: that you are AI-fluent (you will not slow the team down by being the last person to learn a new tool) and that you have genuine judgment and deal experience (you are not just a faster data-gatherer).

The senior bankers screening your CV are not data scientists. Most are MD-level or VP-level professionals who know what AI is doing to their division in broad strokes, but do not have detailed technical knowledge. Your CV needs to speak to them, not to a CTO.

The framing principle: AI enables better deal work, not a different kind of work.

The wrong framing for an IB CV: "Built machine learning models and Python scripts for financial analysis." This reads as a technology CV, not an IB CV.

The right framing for an IB CV: "Used Python automation to reduce M&A screening from 3 days to 4 hours, enabling coverage of 200+ targets per process rather than 60." This frames AI as a lever for better IB work.

Where to put AI skills on an IB CV:

• Work experience bullets (strongest signal): AI applications that happened in internships, analyst programs, or professional roles. Write as normal experience bullets: [tool/method] + [IB task] + [outcome in deal or process terms].
• Skills section (necessary but not sufficient): List specific tools — Python, Excel Copilot, AlphaSense, Capital IQ, Bloomberg, Luminance, any LLM tools used professionally. Keep this compact.
• Do not create a separate "AI Projects" section for an IB CV targeting traditional BBs. It reads as a technology CV. Integrate AI into your deal experience bullets instead.

Sample bullets for different AI applications:

What to avoid:

• Do not claim deep ML expertise if you do not have it — IB interviewers at Goldman and Evercore will probe technical claims
• Do not lead with AI on a traditional IB CV if you have strong deal experience — deal experience ranks above technical skills for most IB MD-level interviewers
• Do not list consumer tools like basic ChatGPT usage without context — it signals you are describing the lowest-common-denominator, not a differentiator

ATS note: Banks including JPMorgan and Morgan Stanley use Workday, which applies keyword scoring. Terms including "Python," "automation," "data analysis," "AI," "machine learning," "NLP," and "Excel Copilot" appear in JD language for new analyst and associate roles. Including them in your bullets increases keyword density beyond just the skills section.

Banks vary significantly in how aggressively they have adopted AI. Your CV calibration should reflect where each firm is on that spectrum.

Goldman Sachs — AI across the franchise

Goldman Sachs launched its internal AI platform — GS AI Platform — in 2024 and has rolled it out across investment banking, asset management, and markets. They were an early partner with OpenAI and have integrated LLM-based tools into their operations at scale. Goldman has also stated publicly that AI will allow them to handle more deal volume with existing headcount rather than scaling headcount at the same rate as deal volume. For Goldman IB roles, demonstrating that you know how to use AI tools to produce Goldman-quality work faster is the right signal.

Hiring priority at Goldman in 2025-2026 beyond traditional IB: technology investment banking (coverage of AI/cloud/SaaS companies requires analysts who understand AI business models), and quantitative strategies within asset management.

JP Morgan — Largest AI investment in banking

JP Morgan spends over $17 billion on technology annually and has made more direct AI investments than any other bank. Their proprietary DocIntelligence platform, IndexGPT (an AI-driven investment suggestions tool for retail), and their research AI tools are all live. They have also been public about the expectation that AI will reduce their analyst headcount needs over time, which means competition for remaining seats will intensify.

For JP Morgan IB roles, demonstrating Python proficiency and comfort with data-heavy workflows is more valued than at most peers. JP Morgan explicitly recruits for its Technology team within IB — the group that builds the internal AI tools used by bankers — and this is a direct path to IB exposure for candidates with technical backgrounds.

Morgan Stanley — AI in wealth and markets

Morgan Stanley's most visible AI deployment is their OpenAI-powered assistant for wealth management advisors, which became a reference case for enterprise LLM deployment in financial services. In IB, they have been rolling out Copilot tools and AI-assisted research capabilities. Morgan Stanley's Parametric and Eaton Vance subsidiaries have significant quant and systematic strategies operations where data science skills are table stakes.

Barclays, Deutsche Bank, Nomura — Microsoft 365 Copilot wave

European banks have largely followed the Microsoft 365 Copilot route for analyst productivity tools rather than building proprietary AI infrastructure. At these firms, demonstrating that you are proficient with the Microsoft AI ecosystem — Copilot in Excel, PowerPoint, Teams, and Teams Premium — is directly relevant.

Boutique and elite advisory banks (Evercore, Lazard, Centerview, Moelis)

Elite boutiques have been slower to deploy AI infrastructure due to smaller IT teams and a cultural emphasis on analyst talent over technology leverage. However, analysts who bring AI skills are increasingly valued here precisely because the boutiques do not have internal AI teams. At Evercore or Lazard, being the analyst who built a Python-based comp table automation or an automated deal screening tool gets noticed by MDs in a way that it might not at a bank with 50 technology staff doing the same work.

2026 hiring signal: Multiple IB houses posted roles explicitly titled "Investment Banking Analyst — Technology and AI" in 2025, suggesting a new category of analyst hire is emerging alongside the traditional IB analyst class. These roles pay at or above standard analyst comp and require both technical and financial skills. See the AI in Finance CV guide for broader context on these hybrid roles.

This section gives you a practical template for how the AI-fluent IB analyst CV reads in 2026, alongside the ATS keywords that matter most for IB roles with an AI dimension.

What the strong 2026 IB analyst CV looks like:

Work experience bullets combine deal execution language with specific AI tool references and quantified time or quality improvements. The AI references are embedded in IB context, not listed separately as "technology projects." The skills section is concise and specific.

Sample experience block (Analyst, Bulge Bracket IB, 2025-present):

Goldman Sachs — Investment Banking Division, Technology Coverage, Analyst (July 2025 – present)

ATS keywords for AI-focused IB applications:

Core IB terms (essential, covered in IB CV guide): DCF, LBO, M&A, deal execution, pitch book, due diligence, financial modeling, comparable company analysis, precedent transactions

AI/technology additions for 2026 IB roles

Python, automation, AlphaSense, Capital IQ API, Bloomberg API, Luminance, Kensho, Microsoft Copilot, Excel Copilot, NLP, data analysis, machine learning, AI-assisted, workflow automation, document intelligence, quantitative analysis, deal screening, screening automation, financial NLP

Formatting reminders:

The format rules that govern traditional IB CVs still apply in full — single page, single column, standard fonts, ATS-safe structure (no text boxes, no tables). AI skills do not get a special section or a different format. They are integrated into the same one-page structure. See the Investment Banking CV guide for the full format specification.

If you are preparing for IB interviews alongside building your CV, the Investment Banking track at Finance Interview Prep covers technical and behavioural questions drawn from real Goldman, JP Morgan, and Morgan Stanley processes.