Due diligence is a critical component of company analysis in high-stakes financial workflows, including mergers and acquisitions (M&A), debt financing, and equity investment decisions.
These processes are often fast-paced and highly competitive, leaving little room for error or delays. However, traditional due diligence methods can be slow and error-prone, hindering timely and strategic decision-making.
AI-powered due diligence is transforming this landscape. By automating and streamlining the analysis of company data, AI enables investment teams to move faster and with greater confidence. In this article, we’ll examine what AI due diligence entails and how it is revolutionizing M&A analysis.
What Is AI Due Diligence?
AI for due diligence is the application of large language models (LLMs) to extract key information from vast amounts of documents and reason over it to provide the expert-level insights required when conducting due diligence on a company.
Finance teams can use AI solutions to automate time-consuming aspects of the due diligence process (like reading and analyzing documents and generating presentations) so they can spend more time conducting deeper strategic assessments, ultimately leading to faster, more informed decisions.
How Can AI Automate and Enhance Due Diligence Procedures?
AI-powered software tools comprehensively support due diligence workflows using the following features:
- Machine learning (ML): ML algorithms enable AI to conduct deep analysis on vast datasets, identifying patterns, risks, and insights faster and more accurately than humanly possible. So, instead of spending weeks or months manually scouring the contents of a virtual data room (VDR) and piecing takeaways together, professionals can rely on AI to build a solid foundation for their analysis within hours or days.
- Natural language processing (NLP): NLP lets AI understand, analyze, and generate human language. For example, a professional can query AI to scrutinize confidential information memorandums (CIMs), legal contracts, financial reports, or any other written document to determine whether a deal is worth pursuing. The AI can then return a specific, informed answer in clear language.
- Generative functionality: AI platforms often have robust generative features that go beyond just answering user queries. AI built for due diligence can generate fully detailed reports, from internally facing documents like investment memos and strip profiles to externally facing presentations like CIMs.
Benefits of Using AI for Due Diligence
With AI, professionals can save time and create a distinct competitive edge by avoiding the common bottlenecks and delays often associated with manual due diligence.
Traditional Process | AI Due Diligence | |
|---|---|---|
Analysis | Involves cognitive limitations on how much information humans can process and analyze at once, leading to higher operational costs, suboptimal resource allocation, and delayed timelines | Reviews multiple data sources at once, limited only by an LLM context window or token usage limits, creating accelerated timelines through better resource allocation |
Prediction | Predictive capabilities are susceptible to cognitive biases | Predictions are almost exclusively reliant on the context provided to AI, while still incorporating human judgement |
Risk of error | Constant risk of human error, with the possibility of small details incurring losses if not caught by the human eye | Reduced risk of human error with granular insights and in-line citations that make auditing easier |
Comprehensive Analysis
AI enables due diligence teams to review thousands of documents and generate key deliverables like discounted cash flows (DCFs) within minutes, accelerating workflows that once took weeks. By handling time-consuming tasks like data extraction, document review, and initial analysis, AI allows professionals to focus on deeper strategic assessment and refinement.
This partnership yields more robust, defensible outputs and supports better decision-making. The combination of AI-driven automation and human oversight allows firms to analyze more opportunities, scale their capacity, and move through deals faster, all while maintaining the quality and rigor that clients expect.
Better Predictions
Due diligence AI tools offer powerful predictive capabilities by analyzing vast datasets and identifying patterns, but human expertise remains indispensable in the process. AI can efficiently flag anomalies, forecast risks, and highlight opportunities, but its accuracy is highly dependent on the context and assumptions provided by human professionals.
For example, if a company changes its accounting practices from year to year, it takes human judgment to interpret these changes and adjust financial models accordingly, ensuring that predictions reflect the true business reality. Human professionals play a critical role in selecting relevant variables, interpreting AI-generated outputs, and making strategic decisions based on those predictions.
Ultimately, the most effective due diligence process combines AI’s analytical power with human insight. AI handles the heavy lifting of data analysis, while humans apply judgment, context, and strategic oversight, resulting in more accurate valuations and better-informed negotiations.
Reduced Risk of Errors
AI due diligence platforms can drastically reduce the risk of costly human mistakes. This is critically important in a field where even one incorrect signal or takeaway can adversely affect negotiations and influence the terms of a deal.
With human oversight, due diligence automation platforms can provide teams with solid foundations that significantly reduce the likelihood of fundamental problems that trigger lengthy review cycles, delay business, or harm negotiations.
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How AI Due Diligence Is Transforming M&A Procedures
Using AI for due diligence is changing the way bankers, investors, and corporate development teams handle M&A processes. Traditionally, due diligence workflows in M&A often take up weeks or months of staff time to get a complete picture of a company’s position.
AI can speed up the analysis part of this process, avoid the risks of human error and uncover novel insights that can yield an edge in negotiations. Below are just a few examples of how AI is improving M&A due diligence workflows:
Automated Ingestion and Analysis of Deal Documents
AI due diligence software can ingest heaps of VDR documents at once, from financial statements and legal contracts to standard operating procedures and marketing materials. From there, it can identify critical points, themes, and risks with citation-linked insights almost immediately.
For example, an investment banker generating due diligence questions can query AI to search for patterns, trends, insights, and information gaps across thousands of disparate data sources from a VDR. They can then use the information they receive to formulate precise queries that achieve clarity and influence the dealmaking process.
Automated Reporting, Modeling, and Deliverable Creation
AI doesn’t just find the data—it helps teams turn insights into actionable outputs. From dynamic financial models and risk summaries to board-ready presentations, AI platforms can automate the creation of standardized, defensible deliverables in a fraction of the usual time.
On the buyer’s side, teams conducting due diligence processes can utilize AI to analyze information provided in materials like CIMs and VDRs. From there, they can pull the data into compelling, defensible investment committee (IC) memos that move deals forward. Leveraging AI in this analysis process minimizes the chance of fundamental errors and significantly reduces time to completion.
Contractual and Risk Intelligence
AI transforms the once-tedious process of extracting covenants, liabilities, and compliance issues hidden in contracts and agreements. By scanning documents in bulk, it highlights relevant clauses—such as non-competes, debt covenants, or regulatory risks—pinpointing areas that could threaten value or complicate integration post-close.
For example, buyers can quickly surface and quantify obligations buried across dozens of agreements, enabling smarter negotiation and risk management. With the help of AI, teams are no longer bogged down in minutiae and can instead focus on interpreting the big picture and mitigating the most pressing risks to the deal.
Investment and Equity Research
Public-market style equity research often feeds into buy-side due diligence in M&A, as analysts synthesize large volumes of unstructured and structured data to assess a company’s value, risks, and growth prospects.
AI enhances this work by automating data extraction, trend analysis, and report generation, enabling teams to produce more accurate, defensible insights for negotiations and investment decisions. This mirrors core M&A diligence activities, where understanding the true value and risk profile of a target is essential.
Curious to know more about how AI is transforming equity research? We published a study on how equity research teams are using Hebbia to gain an edge.
Legal Research
Legal research is a cornerstone of M&A due diligence. Teams must review contracts, regulatory filings, and compliance documents to identify risks, obligations, and potential deal-breakers.
AI streamlines this process by quickly surfacing critical clauses, compliance gaps, and legal risks that could impact deal value or post-close integration. This is directly aligned with the contract and covenant analysis that is fundamental in M&A due diligence.
Financial Planning and Analysis (FP&A)
FP&A teams often support M&A due diligence by building financial models, forecasts, and performance analyses. AI accelerates these tasks by automating data aggregation, scenario modeling, and report creation, allowing for faster and more accurate financial assessments. This is a direct extension of the financial modeling and valuation work that is central to M&A due diligence.
Evaluating AI Due Diligence Platforms
To choose the right AI platform, first identify clear workflows and use cases where AI due diligence software could have the biggest impact, and tune your evaluations based on those.
Consider the following criteria and probing questions when making your decision:
Criteria | Questions |
|---|---|
Feature set and capabilities | - Can the platform help teams execute end-to-end due diligence workflows? - Does it have a robust set of applications and use cases, like enhanced VDR analysis and output generation? - Is it as accurate and fast as our team requires? |
Platform specialization | Is the platform specifically designed for finance workflows? |
Data security practices | - What guarantees are in place that ensure the protection of user data? - Will they use our data and inputs to train the models? - Are there contingencies, tools, and control options in place for account owners to protect and restrict sensitive data? |
Regulatory compliance | - Is the platform in compliance with industry-standard data protection laws like GDPR, CCPA, and SOC 2 - Does the platform incorporate enterprise-grade encryption? - Does the platform have a zero data retention (ZDR) policy? |
Usability | Is the platform easy for a financial professional to understand, navigate, and utilize to its full extent? |
Scalability | - Does the platform enable significant gains in process speed? - Will it open up bandwidth for our team to handle more deals and projects than before? |
Pricing model | - Is the platform’s pricing model in alignment with our budget? - Are there customized pricing options for enterprise clients? |
Using Hebbia for AI Due Diligence
Hebbia stands out as the premier solution for AI due diligence, equipped with unique, cutting-edge capabilities that provide financial professionals with market-beating insights and unmatched speed to signal.
With the capacity to automate 90% of financial and legal workflows, Hebbia has everything a financial professional would need to execute a complete due diligence workflow from end to end. This includes, but is not limited to:
- 10x greater input capacity: Matrix lets professionals ingest and process terabytes of data. This unmatched depth enables bankers to identify nuanced signals and patterns that yield true advantages.
- Agentic workflows: Hebbia lets users deploy agents, such as the Deeper Research Agent, which use one of many available LLM models to execute various due diligence workflows. With agents, teams can complete months-long tasks in just one click.
- High-quality deliverables: Hebbia lets investment bankers and deal teams generate near-complete, audit-ready deliverables in minutes, from pitch decks and CIMs to financial models and IC memos. These come complete with accurate branding, data structuring, and footnotes.
- Superior information retrieval: Unlike most competitors that use a retrieval-augmented generation (RAG) engine for information retrieval, Hebbia uses an iterative source decomposition (ISD) engine, which overcomes all the limitations associated with RAG. Bankers gain insights that remain accurate and traceable at scale.
Right now, thousands of investors and investment bankers are using Hebbia to gain an edge on the competition, accelerating due diligence procedures and supercharging deal velocity. Interested in staying ahead of the curve? Visit our product page to learn more and book a free demo to get started.