The market for AI-related hardware and software is projected to grow at an annual rate of 40% to 55%, potentially reaching between US$780 billion and US$990 billion by 2027, according to new research from Bain & Company.
In its fifth annual Global Technology Report, Bain highlights three key areas of opportunity—expansion of data centers, enterprise and sovereign AI initiatives, and software efficiency—that could drive AI hardware and software to near-trillion-dollar levels within the next three years.
“Generative AI is the driving force behind this transformation, but businesses must adapt quickly as post-globalization trends and evolving business processes introduce new complexities,” said David Crawford, chairman of Bain’s Global Technology practice. “Companies are transitioning from experimentation to scaling AI across the enterprise, which means CIOs must adopt an ‘AI everywhere’ strategy to maintain competitive AI solutions in a rapidly shifting landscape.”
Data Centers Set for Major Expansion Amid AI Growth
Bain estimates that AI workloads will grow by 25% to 35% annually through 2027, significantly increasing demand for large-scale data centers. As AI continues to scale, the size of data centers could expand from today’s 50–200 megawatts to more than a gigawatt in the next five to ten years. The cost of large data centers, currently ranging from $1 billion to $4 billion, could rise to between $10 billion and $25 billion by 2029, placing pressure on infrastructure engineering, power production, cooling systems, and supply chains.
In addition to data center growth, the demand for graphics processing units (GPUs) driven by AI is expected to increase by 30% or more by 2026. Bain warns that this surge, combined with geopolitical tensions, could result in another semiconductor shortage similar to the one triggered by the COVID-19 pandemic.
Sovereign AI: Challenges and Opportunities for Global Players
The rise of sovereign AI ecosystems is adding complexity for technology companies. Governments across the globe, including those in Canada, France, India, Japan, and the UAE, are investing billions in sovereign AI efforts, focused on domestic infrastructure and locally developed AI models. Bain predicts these sovereign AI blocs will further complicate the global AI landscape as countries look to safeguard data and privacy.
“Building sovereign AI ecosystems is expensive and complex,” noted Anne Hoecker, head of Bain’s Global Technology practice. “Hyperscalers and big tech firms will need to localize AI operations to gain competitive advantages in this fragmented global environment.”
The introduction of generative AI has increased the pressure on software development companies to improve efficiency. While generative AI currently saves about 10% to 15% of total software engineering time, Bain’s research indicates that companies could achieve efficiency gains of 30% or more if they fully integrate AI into the software development lifecycle.
Roy Singh, global head of Bain’s Advanced Analytics practice, emphasized that companies need to go beyond simple coding assistants, incorporating advanced techniques like static analysis, product management, and testing to maximize the benefits of generative AI.
M&A in Tech Becomes More Focused on Scope, Less on Scale
Bain’s research also reveals a shift in M&A activity within the tech sector. Companies are moving away from scale-driven acquisitions and instead targeting scope deals—acquisitions that provide new capabilities, products, or market access. Scope deals now account for 80% of tech industry mergers and acquisitions, a trend Bain expects to continue as regulatory scrutiny intensifies.
“The tech sector thrives on disruption, and this decade’s success stories will come from those who master the AI transformation,” concluded Crawford. “The ‘winner takes most’ dynamic will shape the future, with companies that can successfully commercialize AI innovations poised for significant gains.”
The report also covers the impact of generative AI on various sectors and explores why some software companies are experiencing declines in customer success despite advancements in AI capabilities.
