Canadian firms achieve 43% ROI in generative AI adoption

New research from Snowflake and Enterprise Strategy Group shows Canadian organisations are seeing a 43% return on investment from generative AI initiatives, slightly surpassing the global average of 41%.

The "Radical ROI of Generative AI" report is based on a survey of 1,900 business and IT leaders across nine countries and six industries, all actively deploying AI solutions for operational or strategic use cases.

Globally, 92% of respondents reported that their AI investments are yielding positive results, with 98% planning to increase their AI spending in 2025. The survey found that for every dollar spent on AI, organisations are realising USD $1.41 in returns through cost savings and additional revenue.

Baris Gultekin, Head of AI at Snowflake, said, "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. With over 4,000 customers using Snowflake for AI and ML on a weekly basis, I routinely see the outsized impact these tools have in driving greater efficiency and productivity for teams, and democratizing data insights across entire organizations."

The Canadian subset of the survey indicated that local organisations are generally earlier in their AI adoption journeys, with 45% pursuing initial AI use cases compared to the global figure of 36%. Generative AI usage in Canada is largely concentrated within technical teams, outpacing the global average in IT (74% versus 70%), cybersecurity (69% versus 65%), and software development (56% versus 54%).

The report also highlighted that Canadian organisations are less likely to be applying generative AI in functions such as marketing, human resources, procurement, or sales compared to global peers.

Internationally, the report reveals regional variations in AI returns and areas of focus. Australia and New Zealand reported the highest ROI at 44%, while France and Japan showed lower AI investment returns at 31% and 30% respectively. Respondents from Germany cited infrastructure challenges, whereas South Korea reported the most frequent use of open source models and advanced model training techniques. The United Kingdom posted a 42% return with a particular focus on operational efficiency and innovation, while United States-based companies matched Canada's 43% ROI and led in successful AI operationalisation, with 52% of respondents stating they have been "very successful" at achieving their business goals.

Despite the broadly positive returns, data quality remains a significant barrier to more widespread AI adoption. In Canada, 49% of respondents cited data quality issues as a challenge in deploying generative AI. Globally, 64% mentioned integrating data across sources as a challenge, while issues around data governance, quality monitoring, preparation, and scaling storage and computing resources were also frequently reported.

Artin Avanes, Head of Core Data Platform at Snowflake, commented, "The rapid pace of AI is only accelerating the need for organizations to consolidate all of their data in a well-governed fashion. Having an easy, connected, and trusted data platform like Snowflake is imperative not just for helping users see faster returns on their data investments, but it lays the foundation for users to easily scale their AI apps in a compliant and secure manner — without requiring specialized or hard to find technical skills. A managed, interoperable data platform provides seamless business continuity as global enterprises tap into their entire data estate to lead in the evolving AI landscape."

Globally, the report found heightened pressure on organisations to select the right AI use cases, with 71% of early adopters noting they have more potential opportunities than they can fund, and 54% saying it is difficult to choose between opportunities based on criteria such as cost, business impact, and their capacity to execute. The same proportion (71%) reported concern that pursuing the wrong use cases could damage their market standing, while 59% feared for their job security if they advocated for unsuccessful AI projects.

The research shows a growing trend among organisations to enhance AI effectiveness by fine-tuning models using proprietary data – 80% are already doing so. However, 58% continue to find preparing data for AI a challenge.

The report is based on research conducted between November 2024 and January 2025, focusing exclusively on early adopter organisations already utilising commercial or open-source generative AI in production environments.