A recent Accenture survey reveals that nearly two-thirds of businesses plan to intensify their focus on AI and automation investments over the next two years, as they report that these initiatives meet or exceed expectations. The survey included 2,000 executives and found that companies with fully modernized AI-driven processes experience higher revenue growth and productivity than their peers. However, organizations face significant challenges, including gaps in data readiness and infrastructure and skill shortages that hinder successful AI adoption. Gartner predicts that by the end of 2025, nearly one-third of generative AI projects may be abandoned after the proof of concept stage due to rising costs and unclear business value.
The data center industry is experiencing rapid growth, driven by the increasing demands of AI, with the global market projected to soar from $301 billion to $622.4 billion by 2030, according to P&S Intelligence. Current data centers consume about 4% of the total power in the U.S., a figure expected to double to 9% by 2030, as noted by the Electric Power Research Institute. While startups are emerging to address the energy crisis and environmental impact of data centers, experts warn that adopting new technologies may be challenging due to the industry’s high costs and concentrated customer base.
Microsoft Azure’s Chief Technology Officer, Mark Russinovich, warns that U.S. data centers supporting generative AI, such as ChatGPT, are approaching size limits due to constraints from the aging energy grid. As these facilities could soon consume multiple gigawatts of power—equivalent to the energy needs of hundreds of thousands of homes—the industry may need to develop new methods to connect multiple data centers. The current trend requires AI models to be trained within a single data center containing tens of thousands of processors, making energy demands critical. Microsoft has initiated projects to bolster grid capacity, including a $30 billion fund for AI infrastructure with BlackRock and a deal to reopen the Three Mile Island Nuclear power plant. However, the path to overhauling the grid is slow, and companies cannot afford to wait for governmental funding.
In a recent discussion with Charlotte Keenan, Head of Corporate Engagement International at Goldman Sachs, the impact of AI on small and medium-sized enterprises (SMEs) was highlighted, revealing significant opportunities for growth and efficiency. Nearly 50% of SMEs involved in Goldman Sachs’ program are already utilizing AI to optimize performance. At the same time, a survey conducted by the firm indicates that 80% of SMEs are either using or planning to use Generative AI tools within the year. Despite these advancements, challenges remain, with a need for understanding AI technology identified as a primary barrier to adoption. Keenan emphasizes the necessity for practical training and collaboration among SMEs to bridge this knowledge gap. Financial incentives from the government are deemed essential, with 80% of SMEs supporting the need for new funding initiatives to foster AI innovation.
Why do we care?
While AI adoption is surging, infrastructure and cost concerns could limit its full potential. Companies that can help bridge the gap between AI aspirations and practical implementation—especially around data readiness, skill development, and energy efficiency—will find themselves well-positioned. However, providers must be cautious of inflated expectations and guide clients towards sustainable, value-driven AI projects. Take the CIO advice. Establish clear frameworks and metrics to guide their AI initiatives effectively.