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Key takeaways
- AI automates the complex review process in due diligence, saving a significant amount of time.
- The automation mitigates human error, ensuring more accurate business evaluations.
- Advanced AI algorithms analyze vast amounts of data and identify potential risks overlooked by traditional due diligence processes.
- AI-powered enhanced due diligence is most effective when used as a tool to augment human expertise.
Artificial intelligence (AI) streamlines the initial data analysis for dealmakers and stakeholders so that they can focus on strategic decision-making and the due diligence process.
The human role shifts from exhaustive data review to interpreting AI-generated insights and providing critical oversight. The combination of AI efficiency and human judgment leads to a more thorough due diligence outcome.
AI provides a solid technology supporting commercial agreements by:
- Making the due diligence process steps more accurate
- Automating repetitive tasks
- Uncovering hidden risks
- Streamlining workflows and collaboration processes.
This article explores how AI streamlines due diligence during high-stakes transactions, which significantly fosters decision-making.
What is AI due diligence?
AI due diligence is the use of AI and machine learning to ease the due diligence process. AI in due diligence employs advanced software to cope with large volumes of data like contracts, financial statements, communications, and regulatory filings.
Due diligence AI tools scan and manage the right documents, enabling the due diligence teams to:
- Retrieve crucial information from contracts, including dates, clauses, and parties.
- Detect unusual and inconsistent data.
- Check for potential “red flags” by comparing papers to historical data and regulatory compliance standards.
AI tools employ natural language processing (NLP) and machine learning to read and comprehend content on a level beyond human capacity. This speeds up the due diligence procedure and saves parties from the risk of missing information.
Benefits of AI due diligence
Automated due diligence (adding AI to due diligence) is a great idea since it fixes major faults experienced during the old-school manual process:
- Faster and more efficient. Things that used to take weeks or months to perform may now be done in days or even hours. AI makes it simpler to grade papers since it lets legal and financial teams look at and analyse hundreds of them at once. This speeds up the entire transaction process, which gives firms an edge in a market that is changing rapidly.
- Greater consistency and accuracy. Human error is often the case during manual reviews. After reading over hundreds of similar files, a lawyer could miss a key clause. AI, on the other hand, provides a constant and thorough evaluation, detecting every instance of a specific statement or risk to make sure that nothing is overlooked.
- Lower legal and consulting costs. AI eliminates routine assignments by outsourcing tasks that are longer to complete. This makes transactions far less expensive and saves on billable hours.
- Enhanced risk assessment. AI systems detect suspicious items and patterns that are often overlooked by individuals: slight financial data variations, weird contract terms, and compliance inconsistencies.
- Strategy insights. While dealmakers and stakeholders concentrate on strategic analysis and negotiations, AI does the tedious job of going through papers. AI tools utilize the insights offered by AI to negotiate from a strong position, uncover the most critical value drivers, and help M&A investors make informed decisions.
👉 Additional read: Read more on how to prepare a professional M&A due diligence report
How AI works in due diligence
Companies share lots of confidential and sensitive information during due diligence. The target company starts the process by storing the key papers in a virtual data room (VDR).
The major function of generative AI due diligence tools is to collect and evaluate this information through NLP. It enables the AI to “read” and understand the words that humans type in documents:
- The AI platform receives a large number of documents, regardless of their format (such as Word, Excel, or PDF).
- The files are automatically tagged, sorted, and indexed. The AI knows what it’s looking for, which makes the search more intuitive and powerful than utilizing folders by hand.
- The AI removes crucial details, such as dates, amounts of money, legal entities, and specific contract terms.
- AI utilizes machine learning models to look at the data it acquired and compare it to rules that were already set up and millions of historical examples. It reveals “red flags”, limits on changes of ownership, or a lack of regulatory certifications, like GDPR or CCPA.
- Finally, AI writes long reports and summaries of what it has learnt, often with direct connections to the sources to back up its work.
It is also vital to have robust security protocols and restrictions on who may see the information throughout this procedure.
To prevent data privacy concerns, AI-powered VDRs protect private data with bank-level encryption, two-factor authentication, and role-based user permissions. Only authorized VDR users may view sensitive data, while their every action is recorded for an immutable audit trail.
Traditional vs. AI-powered due diligence
The disparity between traditional and AI-powered due diligence reveals how much professional services have evolved recently.
Traditional due diligence
The old-school way is a long process that requires a lot of resources. Due diligence expert teams, like attorneys, accountants, and consultants, process each single document in a data room physically:
- Manual review. The act of going over contracts, financial records, HR documentation, and legal documents by hand.
- Keyword search. Using basic search methods like Ctrl+F to look for specific terms, which frequently miss synonyms and the meaning of the phrases.
- Questions and answers that take a long time. A protracted back-and-forth of questions and answers between the buyer’s and seller’s teams to examine facts and inquire about missing documentation.
- Keeping track with spreadsheets. It’s hard to maintain track of outcomes, risks, and follow-up items on a wide scale, and it’s simple to make errors using spreadsheets.
Eventually, humans overlook critical risks or compliance issues since the procedure is routine over and over again, and there’s so much data to process.
AI-assisted due diligence
AI-driven due diligence is an automated and simultaneous process. When you add mechanical speed and precision to human talents, they become better:
- Automatic document analysis. AI can analyze and assess all texts in a matter of minutes and spot important issues.
- Intelligent risk flagging. The AI scans the whole dataset for any problems, inconsistencies, risks, or red flags, saving due diligence teams from scanning thousands of pages.
- Real-time insights. Professionals receive dashboards and reports that show them the whole AI data room, highlight high-risk areas, and provide quick insights.
- Smarter Q&A. The AI typically finds the solution to a popular query right away by searching the document repository. This shortens the Q&A cycle and enables M&A dealmakers to concentrate on more strategic issues.
👉 Also read: Due diligence is a critical process for businesses to thoroughly investigate a company, person, or asset before a transaction to assess risks and opportunities. Here is our comprehensive due diligence checklist template to make that process more effective.
While the traditional method relies on manual human effort, AI-powered due diligence leverages technology to streamline and enhance the process.
Traditional vs. AI-powered due diligence comparison
| Aspect | Traditional due diligence | AI-powered due diligence |
| Process and speed | Slow and manual. Involves extensive manual review of key documents, data collection, and analysis. This can take weeks to months. | Automated and fast. AI algorithms automate data collection and predictive analytics, processing millions of documents in minutes or hours. |
| Accuracy and consistency | Prone to human error. Consistency can vary due to human factors like fatigue, bias, and differing skill levels. | High accuracy. Minimizes human error through automated, standardized analysis and pattern detection. |
| Data volume | Limited scalability. Manual teams can only handle a finite amount of data, making large-scale projects difficult. | Massive scalability. Easily handles vast datasets (e.g. millions of documents) from various sources without performance degradation. |
| Insights and analysis | Surface-level analysis. Relies on human expertise to find key information, often missing subtle patterns or correlations in large datasets. | Deeper insights. Uses machine learning, large language models, and natural language processing (NLP) to uncover hidden risks, anomalies, and insights from structured and unstructured data. |
| Cost | High labor costs. Requires significant human resources, legal teams, and consultants, leading to high operational costs. | Lower long-term costs. Reduces the need for extensive manual labor, leading to significant cost savings over time. |
| Risk detection | Reactive. Detects risks based on a checklist approach and human review, which may overlook complex or non-obvious issues. | Proactive and continuous. Can monitor real-time data and continuously assess risks, providing early warnings and flagging issues before they escalate. |
| Reporting | Static reports. Produces manually-written, static reports that can be difficult to update or share in real-time. | Dynamic dashboards. Generates automated, dynamic reports and dashboards, offering a clear, real-time view of findings. |
| Key limitation | Limited scalability. Cannot scale to handle complex, modern data environments without significant cost and time increases. | Lack of nuance. May lack the qualitative, nuanced understanding that human experts can bring to a situation, such as assessing company culture or management quality. |
AI-powered solutions help due diligence teams meet deadlines faster, save money, and secure a more precise deal evaluation.
Leading AI-powered data rooms for due diligence
AI is now incorporated into many of the enhanced VDRs. These AI-powered data rooms streamline the due diligence process by securely storing sensitive documents (data protection) and utilizing intelligent document indexing, risk detection, and automation.
Here are some of the most popular VDR systems, distinguished by their AI capabilities and ability to support due diligence.
Ideals
Ideals is a well-known virtual data room provider that has improved its due diligence tools by adding AI.
The goal of its platform is to make M&A and other sophisticated agreements secure and simple.
The key features include:
- Eight levels of granular access permissions
- User-friendly interface
- AI-powered document organization
- Strong search capabilities
The AI capabilities assist users in finding and arranging material by automatically indexing and arranging documents as they are turned in.
It doesn’t conduct as much AI analysis as some of its competitors, but its smart search and comprehensive security features make it a good alternative for enterprises that want a secure and easy-to-use experience.
FirmRoom
FirmRoom is a virtual data room developed for M&A dealmakers. It is noted for being fast, easy to use, and focused on AI-powered procedures.
The platform contains an AI-generated analysis tool for M&A transactions. This method can rapidly find essential terms and clauses in contracts, such as change-of-control provisions, non-compete agreements, and liabilities.
This cuts down on the time it takes to evaluate things. FirmRoom’s AI also provides summaries that cover more than one contract.
Many businesses that want to speed up their transaction times appreciate this option since it automates the most monotonous elements of due diligence.
Ansarada
Ansarada is a well-known company in the VDR industry, and they have spent a lot of money on AI to create an intelligent deal assistant (AiDA):
- Makes forecasts based on data from more than 30,000 historical transactions
- Forecasts how likely a transaction will go through by looking at how bidders act in the data room
- Arranges papers on its own
- Speeds up the Q&A process
- Provides crucial results on dashboards.
This all-in-one, AI-powered system allows dealmakers to make smarter decisions and watch for negotiations.
DealRoom
DealRoom is a complete artificial intelligence M&A platform embracing project management and a secure data room.
The AI elements make the complete transaction process smoother and easier.
The AI Analysis tool on the platform automatically extracts crucial information from contracts. This makes it easy for attorneys and financial analysts to swiftly produce summaries and review data points.
Depending on the kind of deal, the industry, and the rationale for the deal, DealRoom’s transaction Builder feature also easily creates a personalised diligence tracker and folder structure. This makes it easier to form teams more quickly.
The platform is a great tool for teams that want to optimize their whole M&A process, not just the data review, since it works so well with both AI and project management.