Investors are ramping up AI-related M&A to secure cutting-edge capabilities and market-beating returns, but high valuations and open-source disruption are forcing a rethink on deal strategy
Global M&A saw a year-on-year increase in both value and volume in 2024, as dealmakers returned to the table after a lackluster 2023. Several drivers pushed the needle on deals, but one significant factor across all sectors was the rise in artificial intelligence-related M&A. AI dealmaking is thriving as companies seek next-generation capabilities and financial sponsors look to gain exposure to the sector’s immense growth potential. Technology deal value jumped 15 percent year over year, to US$537 billion in 2024, with a chunk of this total driven by the AI revolution.
M&A activity by value 2019 – 2024
Target location: Global Bidder location: Global Sectors: All Sectors
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Effective July 1, 2023, the underlying Mergermarket data supporting the M&A Explorer was consolidated with Dealogic data to produce an even more complete picture of the M&A marketplace. M&A Explorer commentary published before July 1, 2023 may reference data that does not reflect this consolidation.
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Beyond deals like Cisco’s US$28 billion acquisition of Splunk to enhance AI-driven cybersecurity and data analytics, and HPE’s US$14 billion Juniper Networks buyout to bolster its AI-native networking capabilities, buyers from outside the tech arena are also getting in on the act.
This is a matter of necessity. As AI adoption accelerates across industries, companies must adapt or risk being left behind. In 2024, approximately 72 percent of organizations worldwide integrated AI into at least one business function, up from 55 percent the previous year, reflecting the technology’s expanding role in operations and strategy. With AI adoption projected to grow at a compound annual growth rate of 36.6 percent through 2030, businesses across all sectors are under increasing pressure to acquire the right capabilities to remain competitive.
Financial services
Enhancing fraud detection, regulatory compliance, customer personalization and underwriting decisions are all ways in which AI is revolutionizing financial services. Banks, insurers and payment companies are using AI to analyze complex datasets, identify patterns, improve risk assessments and automate decision-making, driving significant M&A activity.
Mastercard’s US$2.7 billion acquisition of Recorded Future last year underscores the growing demand for AI-powered threat intelligence. The deal expands Mastercard’s cybersecurity and fraud prevention capabilities, allowing it to integrate AI-driven, real-time risk monitoring across its global payments network. With AI-driven fraud evolving rapidly, Mastercard’s investment ensures it can proactively detect and mitigate threats before they impact merchants and financial institutions.
In a similar push, Visa acquired Featurespace, a company specializing in AI-driven fraud and financial crime prevention. Featurespace’s adaptive behavioral analytics platform helps financial institutions identify anomalies in transactions, reducing false positives while catching sophisticated fraud attempts in real time. By acquiring Featurespace, Visa aims to enhance its fraud management and risk-scoring capabilities across payments, banking and credit underwriting.
In financial advisory, Houlihan Lokey acquired PSL, a machine learning technology company specializing in financial risk assessment and portfolio analytics. This acquisition enables the investment bank to integrate AI-driven predictive modeling into its valuation and advisory services, offering more accurate risk analysis for institutional investors.
With AI increasingly embedded in financial infrastructure, dealmaking in this space is expected to accelerate as companies move to sharpen efficiency, cut costs and push innovation forward.
Life sciences
AI is accelerating drug discovery, optimization of diagnostics, and personalization of treatment plans. There has been a surge in partnerships and acquisitions, with many of the world’s largest pharmaceutical companies securing AI collaborations. For example, in recent years biotech company Atomwise has entered into partnerships with biopharma giants such as Bayer, Bridge Biotherapeutics, Hansoh Pharma, and GC Biopharma to leverage AI in drug discovery.
Most recently, in 2022, the company entered into a strategic multi-target research collaboration with Sanofi, involving an upfront payment of US$20 million, with potential milestone-based payments exceeding US$1 billion.
Recursion Pharmaceuticals’ US$688 million purchase of Exscientia in August is a textbook example of undertaking M&A to accelerate certain capabilities. The UK-based target’s automated small molecule synthesis capabilities are expected to enhance the combined company’s drug discovery efficiency and effectiveness, potentially reducing the time and cost associated with bringing new therapies to market.
AI’s role in analyzing medical imaging and predicting disease patterns is also fueling M&A, as healthcare providers seek AI solutions to enhance care provision and streamline operations. In a notable deal, Cardinal Health paid US$1.1 billion in an all-stock acquisition of Integrated Oncology Network, which employs AI to enhance clinical workflows by automating tasks like medical notetaking, coding and ordering, thereby streamlining administrative processes and helping healthcare providers dedicate more time to patient care.
Industrials
Manufacturing and industrial companies are rapidly embracing AI to enhance automation, predictive maintenance and supply chain resilience. A survey by EY found that 60 percent of manufacturing leaders prioritize AI-driven technology investments, with many opting for M&A to accelerate adoption rather than building in-house capabilities. AI-powered robotics and industrial automation have driven dealmaking as companies seek to improve efficiency, reduce costs and future-proof operations.
With AI-driven predictive analytics optimizing service provision, production, maintenance and logistics, the industrial sector is witnessing a shift toward AI-integrated acquisitions, as companies position themselves to capitalize on the next wave of automation. This trend is evident in Siemens’ recent US$10 billion acquisition of Altair Engineering, a provider of industrial software. The deal strengthens Siemens' digital transformation strategy, allowing it to integrate AI-driven design and engineering technologies into its industrial automation portfolio.
Similarly, French aerospace multinational Safran’s €220 million (US$226 million) acquisition of Preligens highlights the growing demand for AI-powered data analysis in defense intelligence. Preligens' machine learning algorithms specialize in satellite imagery interpretation, enabling automated object detection for military applications.
Consumer and retail
Retailers are turning to AI to optimize supply chains and personalize customer experiences, with inventory management and chatbot automation motivating M&A activity in the sector.
In January, Level Equity acquired a majority stake in Upshop, a retail technology provider specializing in AI-powered supermarket management. The company’s platform integrates various aspects of store operations, including fresh food management, inventory optimization, e-commerce fulfillment and direct store delivery processes.
Meanwhile, Symbotic's recent US$520 million acquisition of Walmart's advanced systems and robotics business aims to develop an AI-enabled, automated delivery platform for America’s largest retailer’s stores, leveraging robotics to improve online pickup and delivery systems.
Particularly noteworthy in light of the fierce arms race to advance generative AI and large language models, Amazon doubled down on its US$4 billion investment in Anthropic by committing a further US$4 billion, for the biggest outside investment ever made by the e-commerce giant.
Amazon’s aim is to integrate advanced AI technologies into its products and services. Anthropic, in turn, will use Amazon Web Services as its primary cloud provider and training partner, leveraging AWS’s Trainium and Inferentia chips to develop, train and deploy its own future AI models.
Strategic acquisitions in the space are expected to rise as retailers increasingly rely on AI for demand forecasting, personalized customer experiences and cost efficiency.
The price of innovation
There is no sector AI has not yet touched, but the curveball thrown by DeepSeek’s recently launched R1 model may change the M&A calculus. Allegedly developed on a tight budget, the model has been shown in many cases to outperform OpenAI’s o1 and Anthropic’s Claude 3.5 Sonnet.
Critically, DeepSeek’s R1 is also fully open source, disrupting the competitive moat that major AI companies such as OpenAI, Google and Anthropic have built around their closed-source models, potentially reducing the necessity for costly acquisitions. API integration with existing software systems allows for real-time data analysis, natural language processing, and other AI-powered functionalities. With OpenAI commanding a US$157 billion valuation in its most recent funding round, the subsequent success of open-source models could lead to a reappraisal of these valuations. Companies lacking unique, proprietary access to industry data are likely to become less compelling if AI models themselves become increasingly commoditized.
This is noteworthy, since AI valuations have surged to extraordinary levels compared to other sectors, reflecting investor enthusiasm for its transformative potential. As of late 2024, the median revenue multiple for AI companies stood at 25.9x. This is far above the average across the software space, where the mean revenue multiple is 7.6x and the median multiple is 5.5x, according to Public SaaS Companies.
While AI is a premium-priced sector, this lofty valuation threshold comes with an important caveat. The highest multiples are typically observed in capital-raising rounds, not full acquisitions, as investors compete to back the most promising AI startups. Moreover, the data is skewed toward the largest and most successful AI companies, such as OpenAI and Hugging Face, the latter commanding a 150x revenue multiple in its most recent round.
Further complicating matters, many corporate boards outside of the tech sector still lack expertise in the field. This can result in poor M&A decision-making, when underestimating the challenges of integrating AI into existing business models and operations. The rise of cost-effective AI models like DeepSeek R1 will undoubtedly have a tremendous bearing on board discussions as companies decide whether to build, buy or partner for AI capabilities.
Progress with purpose
M&A motivated by AI ambitions is moving at a breakneck pace, but recent developments are forcing buyers to reassess. A well-defined AI acquisition strategy is more essential than ever. Companies must clearly articulate their objectives, whether it is securing proprietary datasets, enhancing automation capabilities or integrating AI into core operations.
Bidders will be prioritizing differentiation through exclusive data, industry expertise and real-world applications. The relentless pace of AI development and its disruptive market impacts mean companies must pinpoint exactly what AI capabilities they want from their target and understand how those capabilities will drive competitive advantage and deliver sustained strategic value.
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