Harnessing Traditional and Gen AI for Peak Efficiency

Traditional AI has already transformed mergers and acquisitions (M&A) by simplifying time-consuming tasks and facilitating decision making at key steps. AI can fast-track labor-intensive M&A processes before, during, and after a deal.

While human expertise is still key to successful relationships and outcomes, AI has assisted in making smarter decisions by analyzing buyer sentiment or generating reports from massive data sets.

Now, with the rise of generative AI, we’re seeing an even bigger shift. From cutting deal costs to boosting dealmakers’ efficiency, let’s dive deep into how these advancements are reshaping the M&A industry. 

It offers greater speed, accuracy, and insight into complex transactions while also providing the advantages of data analysis, risk assessment, and process automation.

These benefits don’t just make AI a useful tool for M&A – they’ve also made AI companies highly desirable acquisition targets in 2024, despite sluggish market conditions. 

In the largest tech deal since Broadcom purchased VMWare, chip-design toolmaker Synopsys acquired Ansys for $33.6 billion in early 2024. It gave Synopsys access to AI-augmented simulation software that analyses and simulates engineered parts and systems before manufacturing. 

As sectors, including defense, health, and aerospace, explore ways to boost AI capabilities, M&A provides an option for rapid transformation and onboarding of new technologies and knowledge.

As big tech corporations continue to invest in AI, high-growth startups offer a lower-risk acquisition target, providing access to cutting-edge technology and easier financing options. These acquisitions enable larger companies to enhance their AI technology while streamlining operations and expanding into new markets.

Other than acquisitions of AI technology via M&A, deals powered by AI have the advantages of speed, thorough data analysis, and early issue detection. AI also automates the labor-intensive processes of organizing, redacting, and classifying information. 

For example, sentiment analysis based on buyer behavior can predict the optimal moment to proceed with a transaction. Likewise, regression analysis can find correlations, detect missing information or inconsistencies in the data, and generate initial draft briefs – all through automation. 

Let’s look at the key ways AI is setting a new standard for effectiveness in the M&A sector, from initial target identification to post-merger integration.

Simplifying M&A due diligence with AI

Artificial intelligence accelerates due diligence timelines, enabling parties to capture the maximum value from the transaction. 

Large transactions may require sharing hundreds or thousands of files containing personal identifying information (PII) and intellectual property (IP) of the seller’s business. Extended deal times and poor entity management practices can increase risks, impact seller reputations, and reduce the final deal price. This is where efficient due diligence helps strengthen the deal’s progress. 

Here’s how AI can help improve the process:

Improved compliance

Machine learning and AI improve the efficiency and effectiveness of due diligence by identifying anomalies, inconsistencies, or patterns in annual reports, financial statements, and corporate datasets. These eliminate human error in repetitive tasks that require high attention to detail. 

AI is particularly useful in detecting fraud events in financial and corporate data by recognizing patterns and categorizing expenses. This reduces information silos or gaps and ensures critical details aren’t overlooked. 

Rapid risk assessment

AI allows for rapid risk assessments by examining publicly available information on the target company. Combined with disclosure documentation, this identifies risks and issues for further investigation. 

Because AI draws from a database of past transactions, it can also predict deal outcomes with greater objectivity and minimize human subjectivity in risk analysis.

Information synthesis and analysis

AI for M&A typically operates in a virtual data room, often commissioned by the buyer when due diligence begins. These highly secure digital environments promote quicker access, easier collaboration, and secure file hosting, with traceability reports showing who accessed which documents. 

When documents, contracts, and financial data are uploaded, AI tools can mine large volumes of text and automatically organize documents into the preferred structure. Legal large language models (LLMs) analyze the text, quickly identifying relevant sections of contracts and other documents. AI can also rapidly redact, categorize, and identify gaps where more information is needed to complete the analysis.

Enhance discovery processes

AI saves valuable time during the M&A process by summarizing documents and detecting gaps so that missing documents can be requested early. Smart AI also reduces duplicate work by identifying similar questions and ensuring each one is answered only once.

What’s more, AI can identify relevant information found in “non-essential” documents and surface it. Since the document review process is more efficient and thorough, this leads to low due diligence costs and reduced turnaround time. 

Predictive and analytical AI can combine and collate similar questions, while generative AI drafts initial memoranda for fast communication between parties.

Gathering real-time insights with AI

AI enables the generation of real-time reports that provide actionable insights, reducing administration time and increasing outcomes-focused behavior. 

Predictive AI can even score sentiment by analyzing how dealmakers interact within the virtual data room. It offers insights into their level of interest and readiness to move forward with the transaction.

Powering smart contracts using AI technology

Smart contracts can self-execute once pre-defined conditions are met. By combining AI with blockchain technology, administrative tasks like regulatory filings, compliance checks, and NDAs can be automated. 

This ensures contractual terms are enforceable while promoting transparency. In turn, it saves time and reduces a deal’s legal costs. 

AI and post-merger integration

Once the deal is sealed, AI can support a smoother transition by assessing and predicting the cultural and operational mix. AI tools help reduce the risk of knowledge loss by automating workflows and using insights gained from due diligence. 

Sentiment analysis and communication patterns 

With AI analyzing employee sentiment, communication patterns, and workflows, potential conflicts or blocks can be identified early and addressed with effective alignment strategies. This clean room approach to integration increases the combined company’s effectiveness. 

Performance monitoring 

Automated performance tracking with AI provides insights that highlight key data points and alert managers and leaders to emerging issues or areas of improvement. With AI-generated data, company leaders can focus on strategic thinking and problem-solving to keep the newly combined company tracking toward its goals. 

A 2024 Bain & Company survey of 300 M&A practitioners reveals that generative AI is used in just 16% of deals but is expected to grow to 80% within three years.

Early adopters find that generative AI, or gen AI, meets or exceeds their expectations when identifying targets and conducting document reviews. These early adopters typically operate in industries like tech, healthcare, and finance, where AI is widely used, and transact three to five deals each year. 

On the buy side, gen AI can scan public information and source and screen potential targets by keyword or sub-industry before a deal even begins. It can rapidly parse press releases, published annual reports, announcements, and media coverage, narrowing down the information request list to focus areas when the deal process begins. 

During due diligence, gen AI is most often used to rapidly scan large volumes of documents to highlight deviations from a model contract so that teams can focus on extrapolating problem areas. Just over a third of early adopters also used gen AI to develop an M&A strategy.

In post-merger integration, gen AI can foster innovation by generating ideas based on the complementary strengths of the merging companies. This can drive operational efficiency, new product development, or market expansion. When used effectively, generative AI can support long-term growth and create a lasting competitive advantage. 

With the rise of legal AI software, practitioners leveraging proprietary data or models will gain a competitive edge. Practitioners who differentiate and identify how to apply owned insights may create a sustainable advantage. 

The potential of AI in M&A to enhance virtual data rooms, provide predictive analytics and risk assessment, and speed up document analysis is sky-high. Integrating across platforms to facilitate smooth mergers and providing insights into effective synergies is just the beginning. 

While using AI means companies can transact faster and more often, it’s not without obstacles. The initial challenge for AI in M&A is sourcing data on both the buy and sell sides for training purposes. 

Here are some more common challenges companies need to watch out for. 

Legal and regulatory challenges for AI in M&A 

With gen AI developing rapidly, legislation is struggling to keep pace. Current laws rely on human skills, knowledge, and ability and will need to evolve to reflect the capabilities and limitations of AI. 

While AI can source legislation and case law relating to the deal, it’s worth remembering that using open-source software can risk privacy, copyright, and confidentiality.

With new laws emerging in the US and EU, it’s integral for legal teams to stay informed and understand their obligations at every step of the process. 

The European Union was the first to sign an Artificial Intelligence Act in June 2024 to regulate the supply and use of AI systems using a risk-based approach. This followed US President Biden’s executive order on October 2023 to establish new standards regulating AI safety and security.

Australia currently lacks specific AI regulations, though existing privacy, online safety, corporations, intellectual property, and anti-discrimination laws still apply. Indicators from initial statements say that testing and audit, transparency, and accountability will be key areas of regulatory focus.

AI in M&A presents unique legal challenges. Laws that govern mergers and acquisitions currently uphold standards that refer to human skills, expertise, capabilities, and fallibilities.

For instance, current legal language refers to a “reasonable person” or whether a person or entity “ought to have been aware” of a particular fact. As AI becomes more integral to the deal-making process, these legal frameworks will need to evolve. 

A key issue is whether generative AI can legally use web-scraped data, including copyright work and personal data, during training. Regulation and case law will also need to address bias, explainability, and trustworthiness of AI models. 

Representation and warranty insurance for M&A will also need to cover AI-associated risks, and indemnities in transaction agreements will need to cover known risks.

Ethical use of AI means putting guardrails in place to protect all parties and mitigate the risk of IP infringement. Addressing biases that can occur in AI algorithms, especially if they perpetuate unfair assessments based on historical data, ensures fairness and sincerity. Parties must be transparent about their use of AI and establish accountability for decisions and outcomes that rely on AI outputs. 

Data privacy and security 

Virtual data rooms provide excellent data security as the seller usually authorizes them. Developing and training algorithms for AI in M&A requires access and permission to analyze anonymized content of virtual data rooms. Such access may only be available to participants in limited transactions.

Further, LLMs can sometimes leak parts of their input training data, making it important to use gen AI in M&A transactions with due care. 

Integration with existing systems

While AI can greatly enhance internal capabilities, its integration requires careful planning. Teams must be well-versed in using these tools and should apply them strategically, starting with the most impactful areas. 

From creating personalized training programs to providing timely coaching based on existing M&A playbooks, AI has the potential to enhance robust systems, but it may exacerbate faulty processes. Knowing where to implement for the biggest impact is key. This is one area where starting small won’t yield dramatic outcomes. 

For example, companies acquiring multiple small businesses might benefit most from using AI for target sourcing and assessment. For large transactions, the biggest value comes from using AI to accelerate due diligence and simplify smart contracts. 

Data quality and availability 

The quality of AI insights depends on the quality of the training data. Relying on public data to value deals can lead to inaccuracy.

Generative AI, while efficient, is prone to hallucinations where it generates information without a reliable source. Whether to develop proprietary AI tools or adopt existing ones is a critical decision to mitigate risks from bias, errors, or limited data sets. 

Open-source software comes with the risk of exposing derivative work to public platforms, though this has yet to be enforced in some jurisdictions, like Australia. 

Overreliance on AI models

While predictive AI provides huge advantages in data analysis, it’s important to keep the limitations in mind. AI models can amplify bias found in their training data or rely too heavily on historical data. This makes real-time data and external sources vital for ensuring models stay relevant.

Another challenge with complex AI models is their opacity. AI excels in identifying correlations but falters with causation. This means that human oversight and strategic thinking paired with simpler models that rely on explainable AI techniques provide more certainty and clarity for deal advisors.

Inaccuracies can arise from AI modeling its training data too closely, resulting in prediction bias or inaccurate predictions. Human review and validation of AI data will remain essential to data analysis processes in M&A for the foreseeable future. 

Finally, when assessing the impact of an identified risk, humans rely on soft information from their lived experience, such as conversations with colleagues, their education or professional development, and familiarity with human nature. To make AI more effective, this information should be integrated into the decision-making process, either by feeding it into the algorithm or by overlaying it with human judgment. 

Readiness for change

Organizational readiness is key to maximizing the potential of AI in M&A. Staff must be confident in adopting the technology, and leadership teams must be prepared to put guardrails in place to protect reputation and ensure ethical use. 

AI can significantly enhance M&A processes where strong systems already exist. However, team structures must be equipped to support this capability, with clearly defined roles and appropriate training for junior staff. Providing room for experimentation and continuous learning will enable teams to stay current with AI advancements and make meaningful process improvements. 

For example, a major finance company in the Netherlands has used AI redaction to redact over 700 documents simultaneously, using more than 30 search terms. This, in turn, reduces deal preparation time by hours. Once uploaded to a virtual data room, AI systems can begin scanning for PII or IP that needs to remain confidential. 

Rather than reading through every document to remove PII, AI pattern recognition automatically detects patterns for the user to select for redaction. Employees then check the work, reversing changes across the entire document pool with a single click, drastically reducing manual labor.

Accelerating due diligence for buyers

When M&A due diligence has large volumes of documentation or across different languages, AI can support buyers by summarizing information and identifying missing documents. 

For example, an annual report may record the sale of property. AI identifies this and can scan relevant documentation to determine if any key information is missing. If discrepancies arise, such as a tax declaration not matching the financial statements, AI highlights these inconsistencies for further review.

AI in M&A presents both opportunities and challenges for dealmakers

Using AI strategically in M&A has the potential to boost confidence on both sides of the transaction, speed up timelines, and potentially increase deal value. 

However, faster deal closures don’t always mean better outcomes. 

While AI can optimize processes, dealmakers still need to ensure that the quality of the deal matches its speed. Organizations face the challenge of gaining a competitive edge using AI tools without sacrificing people’s unique ability to plan, build relationships, and unlock potential in the real world. 

Understanding and mitigating the risks that AI brings to M&A is key to ensuring that AI technologies drive value for practitioners and companies. Success will come from a balanced collaboration between AI-powered tools and experienced professionals.

Looking to optimize your contract management with AI? Explore our expert guidance and best practices for seamless implementation.

Edited by Monishka Agrawal