In an era where Mergers and Acquisitions (M&A) remain fraught with uncertainty, Bain & Company’s 2024 survey reveals that 60% of deals fail to meet strategic or financial expectations, only marginally improved from the 70–90% failure rate documented by Harvard Business Review in 2011— the imperative for innovation is undeniable. These statistics underscore systemic vulnerabilities in traditional methodologies and highlight the urgent need for a new approach to dealmaking.

The M&A landscape is in constant flux, driven by rapid technological advancements, volatile market dynamics, growing global interconnectedness, and heightened geopolitical tensions. Navigating this complex terrain requires a sophisticated approach that transcends traditional practices. Artificial Intelligence (AI) is emerging not merely as a tool or speculative advantage but as a foundational imperative, reshaping how deals are sourced, evaluated, and executed.

Despite M&A’s central role in corporate strategy, the failure rate of transactions remains persistently high. These failures are often attributed to flawed valuation, poor integration, overestimated synergies, and inadequate due diligence. With this backdrop, AI offers the potential to address core inefficiencies by augmenting human decision-making with data-driven precision.

According to Bain & Company’s 2024 Global M&A Report, only 16% of firms currently leverage generative AI in their dealmaking processes—a figure expected to surge to 80% over the next three years. As AI matures, its impact on deal sourcing, diligence, and execution will be transformative. BCG’s 2024 M&A Report further highlights AI’s role in accelerating every stage of the deal lifecycle, from target identification to valuation and post-deal integration.

AI-powered platforms can rapidly assess thousands of companies against strategic fit criteria, outperforming traditional methods of target screening. During due diligence, AI tools like Kira Systems and Luminance parse vast volumes of structured and unstructured data to flag risks, uncover insights, and identify opportunities often missed by human analysts. In valuation and modelling, AI enhances forecasting, incorporates real-time variables, and reduces reliance on subjective assumptions.

Overpaying for acquisitions is a common reason for M&A failure, particularly when competitive tension, optimism bias, or weak due diligence distort the target’s true value. While acquisition premiums are standard practice, unjustified overpayment can lead to shareholder value erosion and operational strain.

AI addresses the inherent limitations of traditional valuation through:

• Expansive data integration: Incorporating real-time and alternative data for deeper analysis.
• Predictive modelling: Refining future cash flow forecasts using machine learning.
• Objective insights: Reducing bias by quantifying previously qualitative inputs.

In South Africa’s mining sector, for example, AI is already enhancing deal rigour through:

• Due diligence automation: Tools validate mineral rights and B-BBEE compliance.
• Resource estimation: AI improves precision in geological modelling.
• Operational risk insights: Predictive maintenance models reduce downtime.
• Commodity forecasting: AI supports risk-adjusted valuations in volatile markets.

AI strengthens M&A in developed and emerging markets by improving scenario planning, detecting latent risks, and enabling proactive risk mitigation. Tools like Bloomberg Terminal and RavenPack scan global data to surface reputational or regulatory threats. Solutions like Palantir and BlackRock Aladdin provide robust scenario planning, while cybersecurity threats are managed via platforms like Darktrace.

Still, these systems require human interpretation to navigate false positives, cultural misalignments, and context-specific challenges, particularly in emerging markets like South Africa.

The ability to integrate data effectively into business strategy is essential. By leveraging both qualitative and quantitative insights, organisations not only gain a clearer understanding of their environment but also position themselves to drive innovation and achieve long-term success. Those who embrace the power of data will be the ones best prepared to navigate change and secure sustained competitive advantage.

Let us analyse your data and unlock value throughout your business model.

While AI optimises data handling and analytical accuracy, M&A success hinges on human insight—relationship building, cultural understanding, negotiation acumen, and strategic alignment. Experienced dealmakers navigate intangible variables such as executive intent, stakeholder sentiment, and political dynamics that AI cannot fully comprehend.

At Step Advisory, we recognise the transformative potential of AI and are committed to integrating it meaningfully within our advisory approach. While we currently prioritise a rigorous, human-driven methodology—grounded in deep industry knowledge and robust sensitivity testing—we are actively exploring AI-powered enhancements.

Our future-forward model combines data-led insights with strategic judgment, helping our clients negotiate from a position of strength in an increasingly complex, data-driven world.

References:
References (Harvard Style):
Bain & Company. (2024). Global M&A Report 2024. [online] Available at: https://www.bain.com/globalassets/noindex/2024/bain-report_global-ma-report-2024.pdf [Accessed 11 Jul. 2025].
Harvard Business Review. (2011). The Big Idea: The New M&A Playbook. [online] Available at: https://hbr.org/2011/03/the-big-idea-the-new-ma-playbook [Accessed 11 Jul. 2025].
BCG (Boston Consulting Group). (2024). AI and the Future of M&A. [online] Available at: https://www.bcg.com/publications/2024/ai-dealmakers-guide-ma-2024 [Accessed 11 Jul. 2025].
Kira Systems. (n.d.). AI for Contract Analysis and Due Diligence. [online] Available at: https://kirasystems.com/ [Accessed 11 Jul. 2025].
Luminance. (n.d.). Legal Process Automation with AI. [online] Available at: https://www.luminance.com/ [Accessed 11 Jul. 2025].
Bloomberg L.P. (n.d.). Bloomberg Terminal. [online] Available at: https://www.bloomberg.com/professional/solution/bloomberg-terminal/ [Accessed 11 Jul. 2025].
RavenPack. (n.d.). Big Data Analytics for Finance. [online] Available at: https://www.ravenpack.com/ [Accessed 11 Jul. 2025].
Palantir Technologies. (n.d.). Palantir Foundry and Gotham. [online] Available at: https://www.palantir.com/ [Accessed 11 Jul. 2025].
BlackRock. (n.d.). Aladdin: Risk Analytics and Portfolio Management. [online] Available at: https://www.blackrock.com/aladdin [Accessed 11 Jul. 2025].
Darktrace. (n.d.). AI Cybersecurity Solutions. [online] Available at: https://www.darktrace.com/ [Accessed 11 Jul. 2025].