Illustration: Maura Kearns/Axios
AI copilots are reshaping how bankers source, analyze and act on opportunities.
- The result is sharper execution and faster decision-making.
Inside the room: AI is reducing tedious work that consumes junior analysts' time, primarily across deal origination, diligence and research.
- It can automatically update buyer lists and personalize client outreach, summarize dense CIMs, and redline both NDAs and term sheets in minutes.
- Bankers use copilots to query models or past presentations, eliminating hours of searching through archived reports.
Between the lines: Each task might save only a few hours a week, but across hundreds of deals, those gains compound into real returns.
By the numbers: Evident, which monitors AI adoption at the world's largest banks, says ROI is finally starting to show up in its most recent AI Index.
- Of the 50 banks it tracks, 32 now disclose AI use cases that deliver a financial business impact.
- In some cases, the savings are in the low hundreds of millions of dollars. For one bank, it's around $2 billion.
Case in point: BMO Capital Markets
BMO offers one blueprint for scaling AI inside an investment bank while keeping risk in check.
How it works: The firm only funds AI projects that are expected to add at least one basis point to return on equity, BMO chief AI and data officer Kristin Milchanowski tells Axios Pro.
BMO has built several internal systems:
- One system reads bankers' inboxes and automatically updates buyer logs, eliminating hours of manual tracking.
- Another consolidates leveraged-finance term-grid data so teams can compare structures instantly.
- A third chat interface lets bankers pull approved insights from research and deal databases in plain language.
Between the lines: "AI is a teammate, not a decision-maker," Milchanowski stresses.
- Every output is logged and reviewed by a human, she says, and "our focus is accuracy and auditability... making people faster, not replacing them."
By the numbers: BMO's research group already has cut report-prep time from more than four hours to less than one. Similar gains are emerging across capital markets teams, Milchanowski adds.
What's next: BMO plans to expand the same tools into diligence and documentation.
The other side: PE perspective
Private equity firms also are becoming AI power users.
Charlesbank Capital Partners, which has $22 billion-plus in AUM, has taken a hybrid approach, chief information officer Ozzie Genc tells Axios Pro.
- "The majority of our AI use goes through three tools," Genc says, naming ChatGPT Enterprise, Microsoft Copilot and Blueflame AI — the last of which is tailored to private markets.
- Genc says the general copilots handle drafting and reasoning, while the finance-specific tools plug into CRM, modeling, and deal-flow processing.
- "The goal is to sharpen the logic, not hand over capital allocation to a machine," Genc says.
Brightstar Capital Partners, with $5 billion-plus in AUM, uses AI frameworks like ChatGPT to build custom in-house agents, according to CEO Andrew Weinberg.
- The firm's head of AI and automation has led development on dozens of internal agents, which are streamlining key processes like CIM review, market mapping and memo drafting.
- "A CIM review that used to take a few hours now takes a few minutes," he says. For example, one agent will produce first-pass reviews while another drafts an internal red-team memo that lists reasons for deal skepticism.
Ethos Capital, with about $3.1 billion in AUM, built its own platform called Petra, co-founder Fadi Chehadé tells Axios Pro.
- Petra — which stands for "private equity transformation research agent" — works as an operating system that connects email, Slack, internal data and external sources, versus using a CRM and shared drivers.
- "It is foundational, not tangential," Chehadé says. "Everybody in our firm uses it every day."
- He adds that Petra can produce a complete company analysis in about 15 minutes, work that used to take weeks.
The bottom line: AI adoption isn't one-size-fits-all, but across the board, the goal is the same: to see more deals, faster, with fewer manual bottlenecks.
Reality check
AI may be transforming dealmaker workflows, but its success depends as much on people and policy as on the technology.
Context: Large language models still require careful human supervision, since accuracy hinges on the quality of prompts and data specificity.
- Some early pilots have stalled when teams discovered that AI could not interpret complex financial nuance without clear guardrails.
- "A lot of studies talk about plug-and-play POCs. That does not generate ROI. The big unlock is rethinking how you do things with the tools," Wells Fargo EVP Kunal Madhok said at last month's Evident AI Symposium.
Friction point: Each AI use case must undergo lengthy security and model risk reviews, and procurement hurdles often slow scale.
- Compliance also remains a constraint, as every prompt and output must be logged, stored, and auditable.
- As JPMorgan chief data and analytics officer Teresa Heitsenrether said at the same event, "data protection is job No. 1, and centralized capabilities let us bake controls into the ecosystem."
Between the lines: Even the best systems fail if employees don't trust or know how to use them.
- Firms are investing heavily in training and change-management to position AI as an assistant — not a replacement — and teaching teams to validate results and build confidence in the tools.
- Describing BMO's approach, Milchanowski says, "We created 'AI for all' training that went to every single employee ... change management and AI proficiency of the workforce is our focus."
Yes, but: Corporate AI use will tip more and more toward automation — actually doing the job — and it may happen soon, Anthropic CEO Dario Amodei told Axios in May.
- AI could wipe out half of all entry-level white-collar jobs in the next one to five years, he estimated.
The bottom line: Right now, the biggest risk isn't runaway AI, but underperformance if firms skip the hard work on process, controls and people.
Follow the money
Even as investment banks and PE firms build their own tools, venture dollars are pouring into AI startups targeting capital markets workflows. Recent deals include:
- Juniper Square raised $130 million in Series D funding at a $1.1 billion valuation to build JunieAI, an AI layer on top of its fund-admin platform.
- Model ML raised a $75 million Series A to automate pitch decks, financial modeling and due diligence for banks and buy-side clients.
- Saphyre secured a $70 million growth equity investment to scale AI software that automates pre- and post-trade workflows for global banks and asset managers.
- Rogo closed a $50 million Series B, bringing total funding to about $75 million, for an "AI analyst" used by investment banks and hedge funds.
- Farsight raised a $16 million Series A to automate pitch books, comps and models for investment banks, private equity firms, hedge funds and wealth managers.
- Finster AI announced $15 million in combined seed and Series A funding for its AI-native research and memo-drafting platform for investment banks and asset managers.
- Daloopa raised a $13 million strategic investment to scale an AI-powered financial data platform used by equity research and investment teams.
- OffDeal raised a $12 million Series A to build what it calls an AI-native investment bank for lower-midmarket sell-side M&A.
- Trove AI (fka Mako) landed a $7.1 million seed round for an "AI teammate" that connects to private equity firms' document stores and knowledge hubs.
- Grasp raised $7 million in Series A funding for a multi-agent AI analyst that automates investment banking and management consulting workflows.
On the M&A front, PE portfolio companies and independent deal-workflow vendors are starting to bolt on more capabilities through acquisitions. Expect the consolidation trend to continue.
- Datasite, a PE-backed M&A SaaS, acquired Blueflame AI to power agentic AI across its dealmaking tools.
- It also acquired Grata, an AI-native private-market intelligence platform for PE firms, and Sourcescrub, a private company data and deal-sourcing provider.
- Inveniam, which pitches itself as a data operating system for private market assets, bought Tractiv and Hedgehog to build "intelligent data infrastructure" for private markets.
- Rogo bought Subset, an AI spreadsheet engine backed by Index Ventures, to build a spreadsheet agent for investment bankers and investors.
- Farsight acquired Presentable AI to add natural-language PowerPoint editing to its workflow stack.
