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AI App Development
Apr 20, 2026
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Table of contents
Can AI replace due diligence teams?
Is AI reliable for risk assessment?
What type of data does AI analyze in M&A?*
Do small companies use AI in M&A?
Rocket.new streamlines preliminary M&A analysis by leveraging AI to assess opportunities, highlight risks, and accelerate decision-making, giving businesses a smarter, faster way to evaluate potential deals.
How do you run a fast, reliable M&A assessment in 2026 without getting buried in data?
You lean on AI to scan, summarize, and guide early decisions. Modern growth teams now use AI to cut through noise, spot red flags, and move a deal forward with greater clarity in less time.
Instead of getting stuck in manual review, teams focus more on insights and direction. That shift is changing how early-stage work is done.
According to a 2026 report by McKinsey & Company, 40% of respondents using generative AI in M&A said it shortened deal cycles by 30 to 50%.
This blog will help you understand how to approach early M&A work using AI simply.
M&A used to mean long nights, messy spreadsheets, and endless document review. Deal teams had to go through large volumes of legal documents, financial statements, and operational data manually. It took significant time and effort to move even a single deal forward.
Now, AI tools are stepping in and changing how this early work gets done.
With artificial intelligence, deal teams can analyze vast amounts of data in minutes. This includes financial documents, market trends, and even unstructured documents like emails or contracts. Early-stage due diligence is no longer about getting everything perfect. It's about getting the right direction quickly.
This shift is making the entire deal lifecycle faster, clearer, and easier to manage for modern deal teams.
Running a preliminary M&A assessment can feel heavy at first, but AI makes the process much more manageable.
It breaks the work into clear steps and helps deal teams move faster without losing direction.
Let's go step by step.
So, first things first. You need data.
This includes:
Financial statements
Legal documents
Operational data
Market conditions
Most of this sits in virtual data rooms. AI tools can pull and organize this quickly. They can also handle large volumes without slowing down.
This is where diligence AI starts helping right away.
Next, you move into the diligence process.
Traditionally, this meant manual review. Now, AI powered tools scan documents, flag issues, and build summaries.
You can generate:
Due diligence request lists
Risk assessment summaries
Initial findings reports
AI powered systems also help with highlighting potential risks like:
Missing clauses
Unusual financial patterns
Legal inconsistencies
This speeds up the due diligence process and gives deal teams a clear starting point.
Then comes the fun part. Data analysis.
AI and machine learning models review historical data and current deal context. They help in analyzing historical data to compare trends.
This is where predictive analytics comes in.
You start seeing:
Potential synergies
Risk exposure
Market dynamics
AI tools provide insights that humans might miss in early reviews.
Still, legal judgment and human oversight matter. AI is fast, but people understand nuance.
After that, you focus on red flags.
AI helps with risk detection by scanning for:
Key provisions in contracts
Financial inconsistencies
Legal issues
This step is critical for any deal.
AI powered systems highlight key risks so deal teams can decide whether to move forward or pause.
Finally, you compile findings. AI generates structured summaries and initial drafts.
These reports cover:
Financial health
Legal risks
Market position
This saves time and helps deal teams stay aligned.
By following these steps, deal teams can move through early-stage diligence with more clarity and less stress. AI does the heavy lifting, while teams stay focused on making smart decisions.
Here’s a quick breakdown:
| Stage | Traditional Approach | With AI |
|---|---|---|
| Data Collection | Manual gathering | Automated extraction |
| Document Review | Manual review | AI powered document review |
| Risk Detection | Time-consuming | Faster risk detection |
| Reporting | Manual reports | Structured summaries |
| Decision Making | Slower |
AI supports the deal lifecycle from start to post close stages.
Let's talk about generative AI.
This is where things start to feel different for deal teams. Generative AI is changing how daily work gets done. Instead of spending hours on repetitive tasks, teams can now move faster and stay focused on what really matters.
It can:
Draft reports
Summarize findings
Create structured research outputs
Generative AI also helps law firms and legal teams handle large volumes of data without getting overwhelmed. It takes care of routine tasks like document review and basic summaries, which usually take up a lot of time.
That means deal teams can spend more time thinking about strategy, deal context, and the bigger picture.
And honestly, that shift alone is making a noticeable difference in how M&A work flows today.
Solve is Rocket's research engine one of three pillars inside the Rocket platform alongside Build and Intelligence. It is built specifically for turning complex business questions into structured, evidence-backed reports with market data, competitive analysis, and recommendations in one place.
For M&A assessments, this matters because deal teams often need fast, structured answers to specific questions market sizing, competitive positioning, investment analysis, and business case evaluation before committing resources to deeper diligence.
How It Works
Solve works through a structured pipeline from question to finished report.
You type your business question in plain language from the Solve input
Solve classifies it into one of nine intelligence types automatically
For ambiguous questions, Solve asks clarifying questions before starting to sharpen scope
Solve breaks the question into its component dimensions each becomes an independent research stream
All streams run simultaneously through parallel agents, not in sequence
Each agent identifies what it researched, which tools it used, and how it assessed the findings
All findings merge into a structured report with an executive summary, detailed analysis, supporting evidence, and actionable recommendations
After the report is delivered, Solve suggests smart follow-up questions so you can drill deeper without re-framing from scratch
Natural language input: Ask any business question without technical framing
Query decomposition: Your question is broken into dimensions, each researched by a separate agent simultaneously
Structured reports: Every task produces an executive summary, detailed analysis, supporting evidence, and recommendations
Investment analysis: Build business cases, run due diligence research, and evaluate opportunities
Market analysis: Size markets, identify trends, and assess opportunities relevant to a target
Competitive teardowns: Compare features, analyze positioning, and run SWOT frameworks
Follow-up questions: Drill into specific findings after the report without starting over
Share and export: Reports can be shared within your workspace or exported for use outside Rocket
It is built for growth teams who need structured, evidence-backed research to inform deal decisions quickly.
So, how does this relate to M&A?
Solve fits into early-stage assessment work where deal teams need structured answers fast.
It helps deal teams:
Ask specific business questions about a target market and get structured reports back
Run investment analysis to build business cases and evaluate opportunities
Analyze competitive positioning of a target through competitive teardown research
Benchmark pricing models through pricing strategy research
Size the market opportunity a target operates in through market analysis
Drill into specific findings through follow-up questions without re-framing from scratch
Reports live inside your project so findings stay accessible as the deal moves forward.
This makes it easier to move from early research to informed decision-making without losing context or repeating work.
👉Run Preliminary M&A Assessment with Rocket 🚀
Growth teams today need speed and clarity.
They deal with:
Multiple potential targets
Changing market conditions
Complex deal structures
AI tools help them:
Save time during early analysis
Reduce manual research across scattered sources
Make better data driven decisions
And the shift is pretty clear. Teams that use AI are moving faster, staying organized, and handling deals with more confidence.
M&A assessments are often slow and complex, with large volumes of data to review. Early-stage due diligence can take weeks and still miss red flags. This makes it hard for deal teams to move quickly and stay confident in their decisions, especially when handling multiple deals at once.
AI, especially generative AI and machine learning, helps deal teams process data faster, identify key risks, and build reports with greater clarity. Learning how to run a Preliminary M&A Assessment with AI gives growth teams a smarter way to approach deals while keeping human oversight and legal judgment in control.
| Data driven decisions |