Early AI investments pay off: 98% of surveyed leaders plan to invest more in 2025

However, organisations are grappling with how to make their data AI-ready and face increased pressure to select the right use cases.

The early adoption of AI is yielding positive returns, with 92% of respondents in recent research by Snowflake reporting that their AI investments are already paying for themselves.

As such, 98% plan to invest more on AI in 2025.

Among the 1,900 surveyed business and IT leaders across nine different countries – all of whom are actively using AI for one or more use cases, 93% indicated that their AI initiatives have been very or mostly successful.

More specifically, two-thirds of respondents are already starting to quantify their generative AI (gen AI) ROI, finding that for every dollar spent, they are seeing $1.41 in returns (or 41% ROI) through cost savings and increased revenue.

Focusing on the APAC region, the returns on AI investments cited are as follows:

• Australia and New Zealand (ANZ) – 44%
  
• Japan – 30%
  
• South Korea – 41%

In general, respondents mainly use gen AI in IT operations (70%), cybersecurity (65%), customer support (56%), and marketing (44%). More than half (55%) have prioritised employee-facing solutions to improve productivity and efficiency.

However, there are global nuances around where organisations are focusing their AI efforts. In ANZ, organisations were more likely to cite enhancing customer satisfaction as a key goal for their AI initiatives (53% versus 43%), and less likely to prioritise internal-facing projects (47% versus 55%).

Japanese organisations, on the other hand, had different strategic goals for AI, thus being least likely to focus their AI efforts on customer service and support (30% versus 43%) and financial performance (18% versus 30%), but the most likely to harness AI to help cut costs (43% versus 32%).

In South Korean, businesses were employing mature AI use cases, reporting the highest use of open-source models (79% versus 65%), and are more likely to train or augment models with RAG (82% versus 71%).

Nonetheless, the success with AI initiatives also comes with challenges in building on the momentum. According to the report, many organisations are grappling with identifying the most impactful use cases, increased pressure to make the right decisions, and limited resources.

In addition, as organisations are increasingly incorporating their proprietary data to maximise AI’s effectiveness, the biggest data hurdles for driving AI success are:

• 64% of early adopters said integrating data across sources is challenging today.
• 59% said enforcing data governance is difficult.
• 59% said measuring and monitoring data quality is difficult.
• 58% said making data AI-ready is a challenge.
• 54% said it’s difficult to meet storage capacity and computing power requirements.

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