Artificial Intelligence (AI) is rapidly transforming the landscape of mergers and acquisitions (M&A). What used to be a slow, labour-intensive process involving countless hours of manual review is now becoming faster, more precise, and data-driven thanks to AI-powered tools. From deal sourcing through due diligence, negotiation and post-merger integration, companies are leveraging AI to gain competitive advantages and unlock greater value.

DEAL SOURCING AND TARGET IDENTIFICATION

One of AI’s most valuable contributions to the M&A process is its ability to identify and evaluate potential acquisition targets with speed and precision. According to McKinsey & Company, AI algorithms can analyse vast and diverse datasets, including financial records, transaction histories, news reports and social media content to highlight targets that align with strategic and financial goals. Beyond target identification, AI supports predictive analytics that allow legal and investment teams to forecast key performance indicators, revenue trends, ROI potential and market fluctuations, providing dealmakers with an opportunity to compare options and make data-driven choices. This reduces the risk of poor fit, and enhances strategic decision-making at the earliest stages of a deal. Additionally, AI platforms can assess softer factors such as corporate culture, customer overlap, and operational compatibility, helping organisations to anticipate integration challenges and prioritise the most promising opportunities.

DUE DILIGENCE AND TRANSACTION DOCUMENTS WITH AI AUTOMATION

Due diligence is often the most time-consuming and resource-intensive stage of an M&A transaction. Traditionally, legal and advisory teams manually review vast volumes of legal, financial and regulatory documents, a process that can take weeks or months. The sheer complexity and volume of data can lead to delays, increased costs and missed red flags, with many high-profile M&A failures stemming from inadequate or rushed due diligence. AI technologies significantly accelerate document analysis by automating the extraction of relevant data from diverse sources, reducing the burden on human analysts and ensuring a more comprehensive and accurate analysis. Whether it’s spotting inconsistencies in contractual clauses, identifying compliance gaps, or highlighting unusual financial metrics, AI empowers deal teams to swiftly conduct deeper and more accurate assessments. As such, AI adoption is quickly becoming essential for private equity firms, legal teams, financial advisors and investment banks seeking a competitive edge in today’s high-stakes deal environment.

AI has emerged as a transformative tool in the preparation and management of transaction documents. According to a recent article by M&A Community, its benefits at this stage include:

i. Reduced manual effort: AI eliminates repetitive and time-consuming tasks such as document review, data extraction and preliminary analysis. This allows deal teams to shift their focus to higher-value activities, including interpreting insights and making strategic decisions.

ii. Accelerated timelines: AI enables rapid generation and review of transaction documents based on the client’s needs, significantly reducing the time required to prepare, negotiate and finalise documentation, allowing deal timelines to move forward faster.

iii. Lower cost: automation of document review, drafting and data extraction reduces dependence on large legal or deal teams, lowering the overall transaction costs while minimising the risk of human error. AI also supports early detection of inconsistencies or missing provision in key documents, helping avoid costly oversight or post signing disputes.

SUPPORTING POST-MERGER INTEGRATION FOR SUCCESS

Ansarada has highlighted the use case for AI in post-merger integration to include:

i. Uncovering hidden synergies by analysing customer behaviour, market trends and internal capabilities, revealing new growth opportunities and suggesting innovative product ideas, optimal marketing and operational strategies, and proposing new business models, enabling integration teams to move beyond simply merging operations to driving a sustainable company.

ii. Generative AI can simulate numerous “what if” scenarios during post-merger integration, using historical data and predictive models to evaluate different potential strategies and their potential outcomes to help make informed decisions.

iii. Natural language processing tools can analyse employee communications and feedback to detect shifts in sentiment and flag potential morale issues early, allowing leaders to address concerns before they escalate.

CHALLENGES OF AI IN M&A

Despite its potential, several challenges hinder the adoption of AI in mergers and acquisitions. These include:

i. Data privacy: the deal cycle often involves handling sensitive and proprietary information about the target company. Firms using AI must implement stringent data protection measures, including data anonymisation and full compliance with relevant data protection regulations.

ii. Data inaccuracy: The effectiveness of AI systems is largely dependent on the quality of the data they analyse. When data is incomplete or contains errors, it can result in misleading analyses, which creates significant risks. This is especially relevant in the African context, which is discussed further below.

iii. High implementation costs: Deploying AI technologies often demands substantial financial resources, including investments in advanced technology, supporting infrastructure and specialised talent. These considerable initial expenses can pose significant barriers, particularly for smaller firms seeking to leverage these technologies.

Additionally, cultural and qualitative factors, such as leadership alignment, employee engagement and stakeholder relationships remain difficult for AI to fully evaluate, underscoring the continued importance of human judgment alongside AI insights.

THE AFRICAN CONTEXT

As AI becomes increasingly embedded in the M&A process, Africa faces the unique challenge of AI recolonisation, due to the reliance on foreign-developed AI technologies. Most AI tools used across the continent are created and controlled by entities outside Africa, often trained on non-African data, and developed with limited understanding of local markets. In the M&A context, this creates a significant barrier, as tools that are not trained on African-specific data are less likely to deliver accurate insights. To overcome this, there must be a deliberate effort to develop locally relevant AI capabilities supported by robust, context-specific data infrastructure. This is not simply a technical requirement, but a strategic imperative for unlocking AI’s full potential in identifying suitable acquisition targets, assessing risks accurately, and guiding post-merger integration within Africa’s M&A landscape.

CONCLUSION

As we enter the era of Industry 5.0, marked by closer human-machine collaboration, AI is set to become an essential partner in the M&A process. Rather than replacing professionals, AI tools are designed to amplify human judgment, streamline decision-making, and unlock deeper strategic insights across every stage of a transaction. In leveraging AI in the M&A process, maintaining human oversight is crucial to ensure the validity and accuracy of AI-generated insights. In this early stage of AI adoption, practitioners must take full responsibility for thoroughly reviewing and verifying all analyses and findings produced by AI before finalising any deal. Successful integration will depend on how effectively organisations combine AI’s analytical power with human experience and intuition. Those who adopt this balanced approach will be better positioned to navigate complexity, reduce risk, and create long-term value in an increasingly competitive deal environment.

Njeri Wagacha is a Director and Wambui Kimamo a Trainee Lawyer | CDH Kenya

This article first appeared in DealMakers AFRICA, the continent’s quarterly M&A publication.

DealMakers AFRICA is a quarterly M&A publication
www.dealmakersafrica.com