AI in M&A: lessons from Clipperton and Edmond de Rothschild
Clipperton and Edmond de Rothschild on their AI rollouts: their setups, what works, what takes time. ROI, the impact on staffing, and where the job is heading.
The M&A Tech Circle, hosted by Entropia, brings M&A professionals together every quarter to explore how technology is transforming the profession, through real cases and first-hand accounts.
Here is our recap of the Paris edition, held on 23 September 2026 at Entropia's offices.
Announcements
Entropia's partnerships with Claude (Anthropic), Rogo and Model ML
The evening opened with a look back at the partnerships Entropia recently signed with the leading AI platforms used by investment banks. Entropia was the first data room in the world with a native connector in Claude, Rogo and Model ML. M&A teams can connect Entropia to most AI platforms on the market: every one that supports MCP (Model Context Protocol). Learn more
It was also a chance to spell out Entropia's differentiation strategy. Some data room market leaders sell their own AI layer as a paid add-on and deliberately limit what their MCP server can do, when they have one at all. Yet their clients have already chosen their AI platform, built their skills on it and connected their sources. And even after paying for the add-on, they still lack essential tools, such as reading the full text of the documents in the data room.
Entropia sided with its clients: from their own AI platform, their teams work on the data room with far more complete tools, full document reading included, at no extra cost. Learn more
Those capabilities make all the difference for teams that work with AI every day.
That is the case for the M&A teams at Edmond de Rothschild, who rolled out Rogo, and at Clipperton, who work with Claude. Manon Elbaz and Olivier Combaudou then took the stage to talk through both rollouts.
Panel
Edmond de Rothschild x Clipperton
Manon Elbaz, Chief of Staff at Edmond de Rothschild, and Olivier Combaudou, Partner at Clipperton, both lead the AI rollout at their bank. They came to share how it is going.
How to choose an AI platform
Choices that are anything but final.
Edmond de Rothschild uses Rogo, a platform specialised in financial analysis. Manon Elbaz detailed the criteria that tipped the balance. AI models move very fast, and Rogo lets teams switch from one to another without changing tools.
Clipperton built its own environment, with Claude as its main platform, and handles orchestration, connections and skills itself.
Both stressed that these choices are anything but final and may well change, because the bulk of the work does not lie in picking the platform. After all, if every bank works with the same tool, how do you stand out? For Clipperton, the first step was to write down its processes and its knowledge base.
Rolling out AI in an M&A team: what takes time
Structuring knowledge, making methods explicit, training the teams.
At Clipperton, the time went into the conditions of use more than into the technology. The biggest workstream is still the knowledge base: the partners wrote down methods, guidelines and processes that had never been formalised. That pushed them to think more explicitly about what makes them distinctive. The team also drafted a charter, and one rule sums up its spirit: "The AI did it" is never an excuse.
At Edmond de Rothschild, Manon Elbaz described a pilot phase in which the team is learning fast. The time goes into training, so people learn to use the tool and to challenge it, into defining the framework of use, into tracking usage and into working with internal "champions". Her goal is to measure how each person uses it, so that AI stays in the service of judgement. Usage varied widely at first. Today the whole team uses it, and real power users have emerged among the analysts. The challenge now is to make usage consistent.
The AI use cases that work in investment banking
On use cases, Manon Elbaz and Olivier Combaudou agreed on the essentials. Their teams already use AI every day across many use cases:
- Sourcing and buyer identification: quickly finding prospects, potential buyers and sector data.
- Sparring: challenging an approach, exploring scenarios, finding angles nobody had thought of.
- Knowledge management: building knowledge bases on specific topics. One example at Clipperton is comparables: building lists of multiples to use as benchmarks.
- First drafts: bankers still write the final version of an investment thesis themselves, but save significant time on the first one.
- Checking: spotting typos and inconsistencies between the sections of a document.
- Q&A: preparing answers to buyers' questions. Olivier Combaudou walked through Clipperton's setup, the tools connected to Claude and the in-house skills, then the Q&A skill applied to a recent mandate. The whole team uses it.
What banks don't want to automate
You can ask AI to write an IM, but you shouldn't.
Both speakers draw the same line. Negotiations and direct exchanges with clients stay entirely human: the relationship, the nuance and reading what goes unsaid cannot be delegated.
As for automating key deliverables such as the IM, the tests have not been conclusive. Bankers get generic text, while the IM is one of the documents where a bank most wants to stand out. The variety of sources and formats also makes the AI's job harder.
Olivier Combaudou pointed to the difficulties that come with the nature of Clipperton's mandates, mostly with tech companies, where you price a vision more than an EBITDA multiple.
Automating deliverables is probably more within reach for mature companies in standardised markets, such as the accounting-firm build-up Aesus Advisory presented at the previous edition of the M&A Tech Circle.
How to measure the ROI of AI in M&A
Ask a better question.
The ROI of AI is still hard to put a number on. You can get a rough idea by dividing the annual cost by the number of pitches and mandates. You can try to estimate the time saved per person and per grade, but a complete costed simulation is not realistic at this stage. The cost of AI is far from neutral and keeps rising, current prices probably don't reflect the real cost of these tools, and nobody knows what they will be in six or twelve months.
Above all, it isn't really the right question to ask for now. Nobody measured the ROI of the internet or of laptops overnight, but everyone saw the way people work change profoundly.
M&A processes will evolve and teams will work differently. Both speakers agreed that the best bankers will pull further ahead thanks to AI. The point is to raise the quality of deliverables, not so much productivity. The ROI will become clear over time.
Will AI reduce junior hiring in investment banking?
No, but it changes what is expected of them.
The short answer: nothing points that way today. Neither bank has changed its hiring.
Edmond de Rothschild is still observing. For Manon Elbaz, even as AI lifts every level, interns must keep mastering the fundamentals on real cases: building a business plan, a DCF, a valuation, calculating a WACC. A junior who has never built a DCF by hand cannot challenge the AI, just as a manager must know how to do what their team does.
Clipperton is not cutting its hiring at all, but it is looking for slightly different profiles, and its expectations of juniors have risen markedly. Olivier Combaudou doesn't want "prompt operators" but analysts who can articulate a line of thought. He values new skills: in the past, a tedious task done well was enough, and the senior brought the thinking afterwards. Once that task can be automated, the bank expects the junior to deliver a first layer of added value.
The question is how to use AI to augment every member of the bank, not how to automate the interns' work. Comparing an analyst's productivity with a model's only holds for certain tasks. And a bank that hired fewer juniors would deprive itself of its future associates, managers and partners.
The investment banker's job in ten years
The bank's added value doesn't change.
What is becoming a commodity is the exploration and analysis of large amounts of information: the first layer of market analysis, summaries of public data, data structuring. Differentiation will therefore come down to judgement, vision, the ability to ask the right questions and see what others miss, and knowledge of markets, sectors and players.
The job stays competitive: a client comes looking for the best deal and the right investor. An investment bank's deliverables are a means to that end, not the end itself. If part of the work becomes a commodity, the bank's added value doesn't change. Bankers will be able to spend more time advising, and the bank will be able to take more risks. Execution speeds up, but deciding what to do with it remains an in-house craft. And AI only works on part of a deal's raw material: the data, provided it is well organised!
Cocktails
The conversations went on late into the evening, sparking many introductions and plenty of new projects.
In the room
A big thank you to everyone who joined us:
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