Snowflake, the AI Data Cloud company, in collaboration with Enterprise Strategy Group, today released the “Radical ROI of Generative AI,” which found that 92% of respondents reported that their AI investments are already generating ROI, with 98% planning to invest even more on their AI initiatives in 2025.
The research surveyed 1,900 business and IT leaders across nine different countries, including the UK, France and Germany — all of whom are actively using generative AI for one or more use cases.
Just two-and-a-half years since generative AI began dominating every tech conversation, early adopters of AI are realizing success with both internal and external use cases. Over half (55%) of respondents prioritized employee-facing solutions to improve productivity and efficiency, while 44% started with customer-facing solutions to elevate customer experience and satisfaction.
“I’ve spent almost two decades of my career developing AI, and we’ve finally reached the tipping point where AI is creating real, tangible value for enterprises across the globe,” said Baris Gultekin, Head of AI, Snowflake. “With over 4,000 customers using Snowflake for AI and ML on a weekly basis, I routinely witness the outsized impact these tools have in driving greater efficiency and productivity for teams and democratizing data insights across entire organizations.”
Mohamed Zouari, General Manager for the Middle East, Africa, and Turkey at Snowflake, said: “With the UAE set to gain $96 billion from AI by 2030 and Saudi Arabia launching a $100 billion AI initiative, AI is fast becoming the blueprint for business growth in the Middle East. But without a data strategy, there is no AI strategy. Regional businesses face real challenges—from fragmented data infrastructure gaps to talent shortages. At Snowflake, we’re helping organizations lay the secure and scalable data foundations they need to truly capitalize on AI’s potential.”
Driving Success Through AI Investments
The report reveals that early AI investments are proving to be successful for the majority of enterprises, with 93% indicating that their generative AI initiatives have been very or mostly successful. Respondents’ AI initiatives resulted in measurable improvements across efficiency (88%), customer experience (84%), and accelerated innovation (84%). In fact, two-thirds of respondents are already starting to quantify their generative AI ROI today, finding that for every $1 million spent, they are seeing $1.41 million in returns through cost savings and increased revenue.
As global organizations advance along their AI journeys, respondents are allocating additional resources to their AI initiatives, citing data (81%), large language models (78%), supporting software (83%), infrastructure (82%), and talent (76%). This strategic focus underscores a fundamental shift in what businesses prioritize to operate and compete in the future.
Overcoming Data Barriers to Maximize AI Effectiveness
Unlocking AI’s true potential requires a robust data foundation. Organizations are increasingly incorporating their proprietary data to maximize AI’s effectiveness, with 80% of respondents choosing to fine-tune models with their own data. Despite this, many respondents are facing significant challenges in making this data AI-ready, citing the following as the biggest hurdles for driving AI success:
- Higher than expected costs: 96% of early adopters report that one or more components of their gen AI solutions have cost more to date than was initially anticipated, and 78% say that half or more of their gen AI use cases have cost more than expected to get into production.
- Breaking down data silos: 64% of early adopters say integrating data across sources is challenging today.
- Organizing unstructured data: The vast majority of data is unstructured — 80%–90% by many estimates. However, only 11% of the early adopters say that more than half their unstructured data is ready to be used in LLM training and tuning.
- Integrating governance guardrails: 59% say enforcing data governance is difficult.
- Measuring and monitoring data quality: 59% say measuring and monitoring data quality is difficult.
- Integrating data prep: 58% say making data AI-ready is a challenge.
- Efficiently scaling storage and compute: 54% say it’s difficult to meet storage capacity and computing power requirements.
