BOZEMAN, Mont. – April 15, 2025 – AI knowledge cloud firm Snowflake (NYSE: SNOW), in collaboration with Enterprise Technique Group, right now launched the “Radical ROI of Generative AI,” a analysis report surveying 1,900 enterprise and IT leaders throughout 9 totally different international locations — all of whom are actively utilizing AI for a number of use instances.
Of all respondents, 92% reported that their AI investments are paying for themselves, and 98% plan to take a position extra on AI in 2025. As AI adoption accelerates throughout international enterprises, a sturdy knowledge basis has emerged because the cornerstone of profitable implementation, but respondents are nonetheless grappling with learn how to make their knowledge AI-ready.
Researchers from Enterprise Technique Group recognized, and performed deeper analysis between Nov. 21, 2024, to Jan. 10, 2025, with early adopter organizations — these already augmenting and executing enterprise processes in manufacturing, utilizing business and open-source fashions relatively than consumer-grade, subscription software program similar to ChatGPT. Of three,324 respondents, 1,900 (57%) mentioned they’re utilizing business or open supply generative AI options. Further particulars round methodology might be discovered throughout the report.
“I’ve spent nearly twenty years of my profession growing AI, and we’ve lastly reached the tipping level the place AI is creating actual, tangible worth for enterprises throughout the globe,” mentioned Baris Gultekin, Head of AI, Snowflake. “With over 4,000 prospects utilizing Snowflake for AI and ML on a weekly foundation, I routinely see the outsized affect these instruments have in driving larger effectivity and productiveness for groups, and democratizing knowledge insights throughout whole organizations.”
Early AI investments are proving to achieve success for almost all of enterprises, with 93% indicating that their AI initiatives have been very or principally profitable. In reality, two-thirds of respondents are beginning to quantify their generative AI ROI right now, discovering that for each greenback spent, they’re seeing $1.41 in returns (a41% ROI) via value financial savings and elevated income.
Nevertheless, there are international nuances round the place organizations are focusing their AI efforts that instantly correlate to every nation’s AI maturity, and their outcomes by way of driving ROI throughout areas:
- Australia and New Zealand (ANZ) respondents have seen a 44% return on their AI investments. In comparison with the worldwide common, organizations in ANZ have been extra more likely to cite enhancing buyer satisfaction as a key aim for his or her AI initiatives (53% versus 43%), and fewer more likely to prioritize internal-facing tasks (47% versus 55%).
- Canada respondents have seen a 43% return on their AI investments. Canadian organizations have been extra more likely to say that they’re solely pursuing preliminary AI use instances (45% versus 36%), suggesting that many are earlier of their AI adoption journeys than international counterparts.
- France respondents have seen a 31% return on their AI investments. In comparison with the worldwide common, French corporations are much less more likely to prepare or increase massive language fashions (LLMs) with proprietary knowledge utilizing retrieval-augmented era (RAG) (59% versus 71%), suggesting a lag in maturity for his or her AI methods.
- Germany respondents have seen a 34% return on their AI investments. German organizations have been extra more likely to report challenges with infrastructure, significantly in assembly storage and compute necessities for AI (69% versus 54%).
- Japan respondents have seen a 30% return on their AI investments. Japanese organizations differed of their strategic targets for AI, being least more likely to focus their AI efforts on customer support and help (30% versus 43%) and monetary efficiency (18% versus 30%), however the most probably to harness AI to assist reduce prices (43% versus 32%).
- South Korea respondents have seen a 41% return on their AI investments. South Korean companies are using mature AI use instances, reporting the best use of open supply fashions (79% versus 65%), and usually tend to prepare or increase fashions with RAG (82% versus 71%).
- United Kingdom respondents have seen a 42% return on their AI investments. When it comes to strategic targets, UK-based organizations have been extra more likely to prioritize the worth AI brings to finish customers, with respondents beating the worldwide common in citing each operational effectivity (57% versus 51%) and innovation (46% versus 40%) as main enterprise drivers.
- United States respondents have seen a 43% return on their AI investments. American corporations led in profitable AI operationalization, with respondents extra usually than some other nation to say that they’ve been “very profitable” at operationalizing AI to attain their enterprise targets (52% versus 40%).
Many organizations additionally report that they’re grappling with tough selections to construct on the momentum. Respondents reported challenges with figuring out probably the most impactful use instances and elevated strain to make the precise selections — all whereas grappling with restricted sources:
- Too many use instances, too few sources: 71% of early adopters agree they’ve extra potential use instances that they need to pursue than they’ll probably fund.
- Choice-making blind spots: 54% agree that choosing the precise use instances primarily based on goal measures like value, enterprise affect, and the group’s potential to execute is tough.
- Aggressive strain mounts: 71% acknowledge that choosing the mistaken use instances will harm their firm’s market place.
- Job safety considerations come up: 59% of respondents say that advocating for the mistaken use instances might value them their job.
Organizations are more and more incorporating their proprietary knowledge to maximise AI’s effectiveness, with 80% of respondents selecting to fine-tune fashions with their very own knowledge. Regardless of this widespread recognition of information’s significance — with 71% of respondents acknowledging that efficient mannequin coaching and fine-tuning requires multi-terabytes of information — vital challenges persist in making this knowledge AI-ready. With the bulk struggling to utilize their most useful asset, organizations declare that the next are the most important knowledge hurdles for driving AI success:
- Breaking down knowledge silos: 64% of early adopters say integrating knowledge throughout sources is difficult right now.
- Integrating governance guardrails: 59% say implementing knowledge governance is tough.
- Measuring and monitoring knowledge high quality: 59% say measuring and monitoring knowledge high quality is tough.
- Integrating knowledge prep: 58% say making knowledge AI-ready is a problem.
- Effectively scaling storage and compute: 54% say it’s tough to fulfill storage capability and computing energy necessities.
There’s a vital alternative for companies to beat these challenges and unlock the complete potential of their knowledge for extra correct, related, and impactful AI outcomes with a unified knowledge platform.
“The speedy tempo of AI is simply accelerating the necessity for organizations to consolidate all of their knowledge in a well-governed style,” mentioned Artin Avanes, Head of Core Knowledge Platform, Snowflake. “Having a simple, related, and trusted knowledge platform like Snowflake is crucial not only for serving to customers see sooner returns on their knowledge investments, however it lays the inspiration for customers to simply scale their AI apps in a compliant and safe method — with out requiring specialised or laborious to search out technical abilities. A managed, interoperable knowledge platform supplies seamless enterprise continuity as international enterprises faucet into their whole knowledge property to guide within the evolving AI panorama.”
The total analysis report is right here: “Radical ROI of Generative AI.”
