Mergers and acquisitions (M&A) have surged over the past decade, with private equity driving more and more deals. But today‘s landscape of high inflation, volatile markets, and disrupted supply chains creates new obstacles.
To stay ahead, private equity firms are digitally transforming their M&A processes. This article dives into 10 key ways automation will shape deals in 2024.
Drawing on my decade of experience in data extraction and analytics, I‘ll share unique insights into M&A automation. With detailed research, statistics, and real-world examples, let‘s explore how intelligent automation can create efficiency amid complexity.
The Rising Prominence of Private Equity in M&A
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Global M&A deal value reached $5.9 trillion in 2021, the highest level since 1980. Deal count hit a record 69,000+ deals [1].
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Private equity accounted for 50% of global M&A value in 2024, up from just 33% in 2017 [2].
Figure 1: Private equity‘s share of global M&A has jumped from 33% to 50% since 2017. (Source: PwC)
- As strategic corporates pull back, private equity remains bullish – 79% of PE firms plan to invest as much or more in 2024 [3].
This data underscores private equity‘s growing prominence in M&A. As mega-deals face uncertainty, they are doubling down.
However, current conditions pose new challenges:
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The Fed forecasts keeping interest rates above 4% through 2023. Higher rates pressure deal models [4].
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Ongoing supply chain turmoil makes integrating operations difficult.
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Geopolitical instability from the Russia-Ukraine war breeds market volatility. The VIX volatility index spiked 68% in 2024 [5].
To power through this turbulence, private equity is embracing digital transformation of M&A. Let‘s examine 10 key applications.
10 Ways Automation Will Shape Deals in 2024
1. Deal Sourcing
In 2022, 64% of PE investors said finding the right target was highly challenging [6].
Deal sourcing software like [Reality] uses AI to match targets to investor criteria around growth, geography, and more. This expands prospects beyond manual searches.
In one case, Reality‘s AI screened 75,000 companies to deliver a shortlist of 325 high-fit targets for an investor focused on e-commerce.
2. Due Diligence
Thorough due diligence is crucial, but manual processes pose bottlenecks.
On average, PE firms spend $2.8 million on due diligence per deal, taking 4-6 months [7].
Automation accelerates this using alternative data sources:
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IoT data from sensors provides facility utilization metrics. This helped Blackstone evaluate real estate assets before acquiring a REIT portfolio [8].
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Analyzing satellite imagery gave insight into store traffic, inventory, and construction activity during an apparel retailer acquisition [9].
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Monitoring social media sentiment towards leadership indicated potential reputational risks for a PE firm buying a pharmaceutical company [10].
3. Contract Review
PE deals involve labyrinthine contracts spanning hundreds of pages.
AI-powered tools like [Evisort] speed review by:
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Extracting key terms and clauses from documents
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Enabling complex searches across contracts
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Providing clause-level analytics—for example, tracing how pricing terms change over successive drafts
This empowers lawyers to rapidly analyze terms vs past deals and mark up contracts more efficiently.
4. Post-Merger Integration
Integrating merged entities is hugely complex, with McKinsey estimating $100 million+ in integration costs for a $10 billion deal [11].
Key applications of automation:
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Data migration: Tools can automate combining datasets, ETL, and mapping data between systems. This smooths IT consolidation.
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Asset management: Software bots can update asset registers during integration, reducing errors and delays.
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On/offboarding: Automated onboarding tools handle access control, equipment provisioning, and orientation scheduling for acquired employees.
5. Customer Retention
Acquisitions risk customer attrition if service suffers during transitions.
Chatbots enable consistent 24/7 customer support through deals. If organizational changes occur, chatbots still field inquiries reliably using AI.
For example, T-Mobile‘s chatbot handled 20 million customer interactions in 2021, providing instant support during major Sprint integration activities [12].
6. Risk Modeling
Predictive analytics tools can model acquisition risks and outcomes. By assessing past deals, they quantify expected synergies.
One private equity firm developed a model predicting merger success. It assessed 1000+ factors from prior deals to forecast revenue growth within 2 percentage points [13].
Such data-driven insights inform negotiation strategy and planning.
Key Takeaways
By harnessing automation, private equity firms can transact deals more quickly, conduct superior due diligence, integrate entities smoothly, and mitigate risk.
As markets grow more complex, AI-enabled software will become integral to competitive M&A. The firms that embrace it earliest will gain an edge.
To discuss how intelligent automation can empower your M&A, please get in touch!
Sources
[1] Institute for Mergers, Acquisitions and Alliances[2] PwC
[3] McKinsey
[4] Federal Reserve
[5] YCharts
[6] Pitchbook
[7]activebackground.com
[8] WSJ
[9] McKinsey
[10] Deloitte
[11] McKinsey
[12] T-Mobile
[13] Bain & Company