Fivetran’s latest survey reports that 81% of organisations trust their AI/ML outputs despite admitting to fundamental data inefficiencies.
Organisations lose on average 6% of their global annual revenues, or $406 million, based on respondents from organisations with an average global annual revenue of $5.6 billion. This is due to underperforming AI models, which are built using inaccurate or low-quality data, resulting in misinformed business decisions.
The survey found that nearly nine in 10 organisations are using AI/ML methodologies to build models for autonomous decision-making, and 97% are investing in generative AI in the next 1-2 years. At the same time, organisations express challenges of data inaccuracies and hallucinations, and concerns around data governance and security. Organisations leveraging large language models (LLMs) report data inaccuracies and hallucinations 42% of the time.
“The rapid uptake of generative AI reflects widespread optimism and confidence within organisations, but under the surface, basic data issues are still prevalent, which are holding organisations back from realising their full potential,” said Taylor Brown, co-founder and COO at Fivetran. “Organisations need to strengthen their data integration and governance foundations to create more reliable AI outputs and mitigate financial risk.”
Meanwhile, 24% of organisations have reached an advanced stage of AI adoption, where they utilise AI to its full advantage with little to no human intervention. However, there are significant disagreements between respondents who work more closely with the data and those more removed from its technical detail.
Technical executives – who build and operate AI models – are less convinced of their organisations’ AI maturity, with only 22% describing it as “advanced,” compared to 30% of non-technical workers. When it comes to generative AI, non-technical workers’ high level of confidence is coupled with more trust, too, with 63% fully trusting it, compared to 42% of technical executives.
Additionally, while those working in more junior positions see outdated IT infrastructures as the top barrier to building AI models (49%), their more senior colleagues say the problem is primarily employees with the right skills focusing on other projects (51%).
The root of the wasted data talent potential and underperforming AI programmes are the same: inaccessible, unreliable, and incorrect data. The magnitude of the issue is shown by the fact that most organisations struggle to access all the data needed to run AI programmes (69%) and cleanse the data into a usable format (68%).
