AI is changing what happens after a dealmaker asks what’s missing, what matters, and what comes next. The opportunity is not just faster work — it is more trusted, traceable, and human-led execution.

Deal work has always advanced through examination. What is the real opportunity? Where are the risks? What will buyers ask? Can we trust the answer? What decision needs to happen next?

What is changing is what happens after those questions are asked.

Increasingly, the question becomes the starting point for the work. A dealmaker can ask for a first-pass room structure, a review of inconsistent file names, a draft response to buyer questions, a readiness check before launch, or a view into whether the data room supports the deal story.

That does not make the dealmaker less important. It makes the human role more focused.

The new deal team is not defined by AI replacing people. It is defined by people using AI to reach the right context, sources and decisions faster.

As Merlin Piscitelli, Chief Marketing Officer at Datasite, put it: 'We’re seeing a real shift in what deal teams look like.'

The first move is changing

For years, deal teams have had to move through work manually: open the folder, search the room, build the index, rename the files, prepare the tracker, review the documents, check the gaps, draft the answer, validate the source.

That work is still important. But it does not always need to start the same way.

Raj Bakhru, Co-Founder and General Manager of Blueflame AI, described the shift as ‘prompt-first dealmaking.’

'The way the dealmakers are leveraging AI now is with what we call prompt-first dealmaking,' Raj said. 'When I’m given a task as an analyst or an associate, VP, or director on a deal, instead of turning to Excel or PowerPoint first, or going to files and trying to find the answer myself, I get my solution and get that work done prompt-first.’

The language matters. Prompt-first does not mean AI-first at the expense of the dealmaker. It means the question becomes the first move.

Instead of starting with manual navigation, the dealmaker starts with intent:

• Set up this room
• Find the gaps
• Draft a first response
• Show me the source
• Check whether this supports the story
• Flag what needs review

The work still needs expertise. The difference is that AI can now help organize the first pass, surface relevant context, and point the team toward what needs human attention.

The new deal team is still human-led

The most important shift is not that AI can do more work. It is that dealmakers can spend more time on the work only people can do.

That is one of the central findings in Datasite’s new report, The new deal team: What 1,000 dealmakers reveal about AI-driven M&A. Based on a global survey of 1,000 dealmakers, the report shows that AI is becoming part of complex deal decision-making, but the human role is evolving rather than disappearing.

Merlin frames it clearly: 'I look at the new deal team and I say the future is not humans versus AI. It’s really how can we enable the humans with AI.'

That is the right lens for M&A. Deals are not completed by information alone. They depend on judgment, timing, confidence, relationships, risk interpretation, negotiation, and trust.

'The human still needs to be in the loop,' Merlin said.

AI can help shorten the path to context. It can help make the work more organized. It can help teams reach the source material faster. But the dealmaker still has to decide what matters.

That distinction is especially important in a market where speed is often treated as the headline benefit of AI. Faster work is useful, but speed does not replace judgment. It only matters if it helps teams make better decisions.

Nevin Raj, General Manager at Grata, put the human side of the equation in simple terms: 'There’s nothing that gets a deal done better than trust.'

That trust is not built by technology alone. Earlier access to information can help. Better preparation can help. More complete diligence can help. But the relationship still takes time.

'So it’s technology that helps you make that decision faster,' Nevin said, 'but ultimately, you can’t speed up how long it takes to build a relationship.'

That is the balance the new deal team has to strike: use AI to move faster through the work that can be accelerated, while preserving the human work that actually closes deals.

From question to workflow

The shift becomes clearest when it moves from idea to workflow.

A prompt is no longer just a way to ask for information. In the data room, it can become a way to start a project, organize files, prepare Q&A, check readiness, or test whether the room supports the story being taken to market.

Alice Esmerian, Product Strategist at Blueflame AI, described the impact this way: 'A lot of tasks that were very manual and time-consuming are going to be accelerated significantly to leave a lot more time for the real interesting part of the job.'

That is the practical promise. Not automation for its own sake. Not a generic AI layer added to deal work. But a way to move through process-heavy tasks faster so the team can spend more time on preparation, review, and decision-making.

A room can start with context

A data room structure has always carried meaning. It shapes how buyers experience the asset, how quickly they can find what matters, and how confidently they can move through diligence.

Setting it up well takes time. It requires context, consistency, and an understanding of what buyers will expect to see.

Now, a team can begin with a prompt and deal context. What type of transaction is this? What industry is involved? What size is the company? What structure would make sense?

From there, AI can suggest an index, create folders, and give the team a first pass to review and refine.

Alice described how a task that once required 'putting together a spreadsheet index or just reorganizing folders and naming and making sure everything was clean and with zero typos' could be 'done from a simple prompt in a few minutes.'

That does not remove the need for review. It gives the team a better starting point.

Q&A can move faster without losing validation

Q&A is one of the clearest examples of where deal work is both manual and high stakes.

Questions arrive from buyers, advisors, diligence providers, and internal stakeholders. They are often unstructured, repetitive, urgent, and tied to source material that needs to be checked carefully.

Alice called Q&A a workflow that 'honestly cost me a few hours and nights back in the days.'

The opportunity is not simply to draft faster answers. It is to draft answers that are grounded in the room, linked back to source documents, and clear about where the AI has confidence and where human review is needed.

Alice emphasized the importance of 'zero hallucination.' In practice, that means the workflow should not just produce a response. It should help the user understand whether the response can be supported, where it came from, and what still needs to be checked.

That is a more useful kind of speed. It gives the team a first pass, but it also keeps validation in the workflow.

Readiness can become an extra layer of review

Before a room opens, deal teams have always faced the same anxiety: Is everything ready? Are the right files published? Are there gaps? Are folders named consistently? Are buyers going to find an issue immediately? Does the room support the story?

Caitlin Murdy, Vice President of Sales Engineering at Datasite, described the launch-ready checklist as 'one of the most tedious, time-consuming, nerve-wracking parts of the process.'

With AI, she said, teams can use it 'kind of as that first pass.'

That first pass can help identify gaps, unpublished folders, draft documents, naming inconsistencies, access issues, and likely buyer questions. It can point the team toward the issues that need attention before the room goes live.

'It’s almost an extra layer of eyes on what you’re doing,' Caitlin said, 'so you can feel more confident in what you’re doing and what you’re executing.'

The role of the person does not go away. The person gets a better way to focus their review.

The room can be checked against the story

Deal preparation is not just about uploading documents. It is about making sure the materials support the story being told.

Caitlin described a practical example: bankers asking whether the data room aligns with the story in the CIM.

'To be able to use a tool like Copilot to say this is a story we’re selling by uploading the CIM and what they’ve crafted there, does the data room align to that?' she said. 'It’s helping you get a sense of the story you’re telling with your data room by the questions people might ask.'

That is where prompt-first work starts to become more strategic.

The AI is not just helping with a task. It is helping the deal team prepare for the conversation buyers are likely to have with the materials.

Trust is the operating requirement

For dealmakers, AI cannot be separated from trust.

The content is too sensitive. The decisions are too high stakes. The workflows are too permissioned. The consequences of getting it wrong are too significant.

A faster answer is only useful if the team can understand where it came from, who had access to it, whether the source material was current, and whether the output can be validated.

Caitlin put the client concern plainly: 'I think first and foremost, we are a security-first company. We want to make sure we’re doing right by our clients and protecting their data.'

That is why AI in dealmaking needs to behave differently than generic AI applied to generic documents. It has to respect the deal environment.

• Permissions need to hold
• Redactions need to hold
• Audit trails need to hold
• Source links need to be visible
• Human validation needs to remain part of the process

Raj explained why it matters for AI to work inside the deal environment instead of forcing teams to move content elsewhere.

'The download/upload process itself is clunky,' he said. 'It’s also error prone.'

The risk is not just wasted time. It is stale content, old copies, wrong data, missing context, or information being handled outside the environment where it should live.

When AI works inside the data room, the system can operate against live deal content while respecting the permissions already governing the project.

'The permissions obviously are really important,' Raj said. 'No one is getting access to anything that they don’t otherwise have access to. The agent is only working through what you have the ability to see.'

That is the difference between AI as a shortcut and AI as part of governed deal execution.

Prompt-first work only becomes useful at scale when deal teams can trust the environment around it.

The human role becomes more focused

The point of AI in dealmaking is not to take the deal away from the dealmaker.

It is to move more of the process-heavy work out of the way, so people can spend more time on the work that remains fundamentally human: judgment, negotiation, relationship-building, risk interpretation, accountability, and decisions.

Anjali Motiani, Chief Financial Officer at Datasite, framed the shift as larger than technology adoption.

'AI is becoming a business transformation conversation,' she said. 'It is not just a technology conversation.'

That is an important distinction. The question is not simply which tool a team uses. It is how the work changes when teams can ask better questions, access better context, and validate information faster.

Anjali also points to the people side of adoption: building new habits, new ways of working, and excitement around the tools employees have in their hands.

That is ultimately what prompt-first dealmaking requires. New habits. New ways of working. New expectations for what can be asked, checked, drafted, validated, and acted on.

But the center of the work remains human.

• AI can help get to the first pass. The person still has to decide whether the answer is good enough.
• AI can surface the source. The person still has to interpret it.
• AI can flag a likely question. The person still has to prepare the response.
• AI can help move faster. The person still has to build trust.

The question becomes the starting point

The future of deal work starts with a question — but not just any question.

• Can we prepare faster without losing control?
• Can we validate the answer before relying on it?
• Can we keep sensitive content inside the governed environment?
• Can we give dealmakers more time for judgment?
• Can we make the work faster and more trusted?

That is the opportunity in front of the new deal team.

Prompt-first dealmaking is not about handing the deal to AI. It is about giving dealmakers a better way to work with the information, context, and decisions already at the center of M&A.

The work starts with a question. The value comes from what the dealmaker does next.

As Merlin said: 'The future of dealmaking will be faster, but it has to be trusted.'