Generative artificial intelligence can lead to strategic transformation in many sectors. If the right factors are in place, technology could also potentially spark a supercycle of mergers and acquisitions, a study suggests. white paper of Goldman Sachs Global Banking & Markets.
Business adoption of the generative system AI Solutions needs to move further from proof of concept to production before arriving at a sustained period of high M&A activity based on this technology, the document says. Additionally, the models themselves must move from training to inference, in which AI systems can identify and respond to new situations based on previous training.
Greater maturity of the legal and regulatory framework is another prerequisite for an increase in mergers and acquisitions. And greater clarity is needed on the form and function of foundational AI models (AI systems trained on huge data sets that can be used for a wide variety of purposes), including whether they will be large and owners or small and open.
“As clarity becomes clearer and AI use cases continue to evolve, the M&A landscape will change,” the document states. “Specialized applications of generative AI will emerge, and buyers will likely go on the offensive, focusing on proven targets with demonstrated product-market fit.”
Certainly, there has been significant strategic activity this year, starting in January. This was an inflection point, after which a wave of large, incumbent tech companies invested in or acquired generative AI startups. Some target companies were early-stage companies with no revenue or were acquired with the goal of adding qualified talent.
Beyond this initial stage, M&A activity may be limited until AI companies prove their potential and the sector matures. However, important theses regarding mergers and acquisitions are beginning to become clearer.
Emerging theses in M&A
Intelligent vertical applications could be one of the focuses of mergers and acquisitions activities, according to the newspaper. When AI capabilities are combined with data sets tailored to a specific industry, the result will improve efficiency, accelerate product time to market, and optimize the end-user experience. This is already seen in sectors such as education, media and law.
The transformation of customer support activities and contact centers could give rise to a new wave of mergers and acquisitions. AI will be able to deliver empathetic, personalized experiences and solve customer and product problems through almost fully automated systems.
The need for companies to “restructure” on the fundamental models and cloud services needed for generative AI systems may be another M&A theme. One of the key features here is the growing importance of links between semiconductors, software and systems. Many elements must work together – from data center design to software applications to privacy systems – to manage increasingly complex AI use cases. In the era of modern computing, the most important control points might be closer to the silicon foundations of AI infrastructure.
Analytics and DevOps-MLOps platforms can converge. Data science and analytics are at the heart of machine learning and an essential part of the new enterprise technology stack. As data science and analytics become more central to enterprise IT, DevOps tools (the integration between software and IT) are poised to combine with enterprise IT platforms. analysis to form coherent systems.
The speed at which generative AI technologies are kissed is almost unprecedented, and for policymakers, investors and the general public, now is not the time to continue with business as usual. “Venture capital firms are eager to invest in the next disruptive AI startup, public market investors are eager to understand the impact of AI on each industry, and companies are eager to understand how AI will fundamentally change the strategic landscape,” the document notes.
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