The latest Snowflake report titled “The ROI of Gen AI and Agents” shows that GenAI seems to be working quite well in the enterprise setting – contrary to other reports that either point to feverish hype or stubborn skepticism. An overwhelming 92% of early adopters say they are seeing positive returns from the GenAI investments.
According to Snowflake, those who measured the ROI reported an average return of 49% – up from 41% from last year’s report. These are encouraging numbers in a market full of half-built demos, overpromised copilots, and endless claims about transformation.
In terms of the drivers for AI adoption, operational efficiency is at the top of the chart – 88% of respondents said they had seen material gains in efficiency. Innovation and customer experience followed close behind, with 83% and 84% respectively reporting measurable improvements.
According to Snowflake’s research, AI has also had a net positive impact on the workforce with 77% of organizations reporting AI-driven job creation compared to 46% experiencing role reductions. This could be a signal that as adoption accelerates, AI is driving overall job growth rather than consolidation.
Nearly half of all code (48%) is now generated by AI. This highlights how deeply the technology is embedded in day-to-day workflows for developers. Companies are also seeing measurable benefits from AI coding tools and apps, with 82% reporting improvements in code testing, bug detection, and resolution.
“AI’s impact won’t be uniform — some roles will dramatically amplify their influence and productivity, while others risk being left behind. The difference comes down to how effectively it’s used: breaking down problems with first-principles thinking and guiding AI agents like high-performing teams,” said Anahita Tafvizi, Chief Data Analytics Officer, Snowflake.
She further added that “The strongest ROI isn’t coming from experimentation alone,” and that it will likely come from “embedding AI into core operations while strengthening data readiness and governance policies.”
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We know AI agents have become central to conversations around AI and are becoming the next stage of adoption. Around 32% of organizations say they already have AI agents in production, and another 25% expect to deploy them within the next year. We see more companies using AI for multi step tasks with limited human input. The use cases for AI agents are expanding to IT operations, compliance monitoring, and customer service.
Things get even harder when companies deal with unstructured data. Most organizations are sitting on huge amounts of it, things like documents, emails, chat messages, images, and other files that do not live in neat database tables. In theory, all of that information could help power AI systems. In reality, much of it is not ready to use.
The report says that on average only about 20% of unstructured data is actually ready for AI. Even more striking, just 7% of organizations say that more than half of their unstructured data is AI ready. That means most companies are trying to build AI systems while the majority of their data still needs work.
Making that data usable is not a quick or easy task. It often has to be cleaned, organized, labeled, and governed properly before AI systems can use it reliably. That process can take time and effort. As a result, projects that otherwise look promising initially can often stall.
Another notable finding from the research is the cost pressure surrounding these AI deployments. The report notes that 95% of organizations said at least one part of their GenAI stack exceeded initial budget expectations. Spending on infrastructure was a major contributor. This includes the budget allocated to compute resources and data platforms required to support AI workloads.
“The data shows that AI is delivering tangible returns, but scaling it successfully requires a strong data foundation and governance framework,” said Adam DeMattia, Senior Director of Research, Omdia by Informa TechTarget.
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“Organizations that can unify their data, improve quality, and operationalize AI responsibly will be best positioned to sustain ROI and workforce gains. With its focus on secure, governed data and AI integration at scale, Snowflake is well positioned to help enterprises move from experimentation to enterprise-wide impact.”
Despite the data challenges, companies continue to increase investment. Organizations expect about on average 22% of their technology budgets to go toward GenAI initiatives over the next 12 months. This suggests that many still view the long-term value as outweighing the near-term costs.
A core theme in this year’s report is that GenAI is settling into something more practical where companies are starting to see reasonable returns. However, the excitement around models is only part of the story – the bigger hurdle now is data. And until that improves, many organizations will keep running into the same limits.
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Author: Ali Azhar
