**Global survey:** A recent study reveals 92% of businesses worldwide see AI investments as financially sustainable, with an average 41% ROI. Despite regional adoption differences and operational hurdles like data readiness, nearly all plan to increase AI spending in 2025.
A new global survey reveals widespread confidence among businesses in the financial returns of their investments in artificial intelligence (AI), as the technology becomes an increasingly integral part of company operations worldwide. The research, conducted by Enterprise Strategy Group in partnership with Snowflake, gathered insights from 1,900 business and IT leaders across nine countries, all actively employing AI in various use cases.
The study found that 92% of respondents believe their AI investments are already self-sustaining financially, with an average reported return on investment (ROI) of 41%—meaning that for every dollar spent, companies are seeing $1.41 in savings and additional revenue. Nearly all, 98%, plan to increase their AI spending in 2025.
Baris Gultekin, Head of AI at Snowflake, highlighted to Business Wire the significant shift in AI’s value to businesses: “We’ve finally reached the tipping point where AI is creating real, tangible value for enterprises across the globe.” He noted that over 4,000 customers use Snowflake’s platform regularly for AI and machine learning, indicating a growing demand for data democratization and operational efficiency.
The report outlines notable regional differences in AI adoption and returns. Australia and New Zealand recorded the highest average ROI at 44%, with a strong focus on enhancing customer satisfaction. Canada follows closely with 43%, but many Canadian organisations consider themselves in the early stages of AI adoption. France shows a lower return of 31% and appears to lag in adopting advanced techniques like retrieval-augmented generation for training language models. Germany’s challenges centre around IT infrastructure, particularly storage and computation capacity, which 69% of respondents cited as difficulties.
Japan’s AI strategy is distinct, with organisations least likely to focus on customer service or financial performance but most likely to use AI for cost-cutting purposes. South Korea demonstrates a mature approach, with the highest use of open-source AI models and advanced model training methods. The UK reports a 42% ROI and emphasises AI’s benefits to operational efficiency and innovation. Meanwhile, US companies lead in operationalising AI, with over half declaring ‘very successful’ results in achieving business goals.
Despite the optimistic returns, companies face practical hurdles in selecting and funding AI projects. A majority of early adopters (71%) feel overwhelmed by the number of potential AI initiatives versus available resources, and over half struggle to objectively evaluate projects based on factors like cost and impact. There is also intense pressure to choose correctly, with 71% of respondents acknowledging that poor decisions could harm their market position and 59% fearing job consequences.
Data readiness remains a significant barrier to maximising AI benefits. While 80% of organisations fine-tune AI models using proprietary data, 64% struggle with breaking down data silos, and around 59% report difficulties enforcing governance and monitoring data quality. Preparing data and scaling infrastructure for AI workloads are also cited as ongoing challenges.
Artin Avanes, Head of Core Data Platform at Snowflake, stressed the importance of a unified data platform in this context. “Having an easy, connected, and trusted data platform is imperative not just for helping users see faster returns on their data investments, but it lays the foundation for… scaling their AI apps in a compliant and secure manner,” he said.
The report, titled “Radical ROI of Generative AI,” provides a comprehensive view of AI’s evolving role in business and emphasises the need for sound data management to fully realise AI’s potential across sectors and geographies.
Source: Noah Wire Services
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