Even if US companies redirected their entire on-premise IT budgets to the cloud and reinvested savings from applying AI in areas such as sales, marketing, customer support, and R&D into new data centers, the total would still be insufficient.<\/p>\n
According to Bain, AI\u2019s compute requirements are rising at more than twice the pace of Moore\u2019s Law, intensifying the funding challenge.<\/p>\n
![\"stargate](\"https:\/\/www.newsbeep.com\/ie\/wp-content\/uploads\/2025\/10\/1760722455_262_w=9999.jpeg\")
Bain projects that global AI computing needs could climb to 200 gigawatts by 2030, with the US accounting for half of that demand<\/p>\n
AI to contribute $19.9 trillion to global economy by 2030, says SDAIA NIC director<\/a><\/p>\n Agentic AI races ahead, promising new gains<\/p>\n As computing demand increases, leading companies have moved beyond AI pilots to scale the technology across core workflows \u2014 delivering 10 to 25 percent EBITDA gains in just two years.<\/p>\n Most, however, remain in experimentation mode, settling for modest productivity improvements, Bain\u2019s report notes. Tech-forward enterprises are now investing heavily in agentic AI, fueling unprecedented innovation.<\/p>\n Over the next three to five years, 5 to 10 percent of tech spending could go toward foundational AI platforms, protocols, and real-time data access, with up to half eventually devoted to enterprise-wide AI<\/a> agents.<\/p>\n Bain identifies four levels of maturity \u2014 ranging from large language model (LLM)-powered retrieval agents to multi-agent constellations \u2014 where investment and innovation are accelerating most at levels 2 and 3. Leaders are rapidly compounding their advantages, while followers fall further behind. Meanwhile, IT architectures remain a barrier, struggling to enable secure, context-aware agents to collaborate across applications and databases.<\/p>\n SaaS and the AI disruption choice<\/p>\n Generative and agentic AI are transforming software-as-a-service (SaaS), yet this shift does not spell obsolescence. In many cases, AI can actually expand the total addressable market for SaaS providers. To capture this opportunity, companies should assess two dimensions: AI\u2019s potential to automate user tasks and its ability to penetrate SaaS workflows.<\/p>\n Incumbent providers are well-positioned to lead but must make bold strategic moves \u2014 whether through selective open-sourcing, evolving monetization models, or embedding AI-first standards. Success will depend on owning critical data, shaping industry norms, and pricing by outcomes rather than log-ins.<\/p>\n \u201cIn the Middle East, SaaS providers are already beginning to see the impact of AI across critical workflows,\u201d said Brahim Laaidi<\/a>, partner at Bain & Company Middle East. \u201cThe opportunity is to use AI not just to optimize tasks, but to reimagine how value is created for customers. Providers that act boldly, by adapting monetization models, investing in data ownership, and embedding AI-first standards, will secure long-term leadership in a rapidly evolving market.\u201d<\/p>\n Tariffs, export controls, and governments\u2019 drive for sovereign AI are accelerating the fragmentation of global technology supply chains, according to Bain & Company. Once primarily an engine of economic growth, AI is now also a lever of political power and national security.<\/p>\n As semiconductor supply chains fragment, the US and China continue to dominate the decoupling trend \u2014 China alone represents about 20 percent of global chip manufacturing capacity this year, the report notes. \u201cSovereign AI capabilities are increasingly seen as a strategic advantage on par with economic and military strength,\u201d said Anne Hoecker<\/a>, head of Bain\u2019s Global Technology practice.<\/p>\n \u201cWhile sovereign AI is a global priority, individual countries\u2019 goals vary. Therefore, for most countries, achieving full-stack independence is not feasible, at least not today. Considering these differences, global AI standards are unlikely to converge. \u201cTo succeed, multinational firms will need to localize not just compliance, but also their technology architecture. Businesses need to make decisions with optionality, moving boldly where confidence is high and prioritizing flexibility where uncertainty rules.\u201d<\/p>\n Companies explore quantum computing advancements, rise of humanoid robots<\/p>\n![\"AI\"](\"https:\/\/www.newsbeep.com\/ie\/wp-content\/uploads\/2025\/09\/1758788422_972_w=9999.jpeg\")
According to Bain, AI\u2019s compute requirements are rising at more than twice the pace of Moore\u2019s Law, intensifying the funding challengeSovereign AI redefines global business strategy<\/p>\n