Organizations Bullish on AI Adoption Despite Yearly Losses from Underperforming AI Models
Research by Fivetran and Vanson Bourne highlight the importance of data quality and addressing the AI skills gap.
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Fivetran, a global leader in data movement, has released the results of a survey that shows 81% of organizations trust their AI/ML outputs despite admitting to fundamental data inefficiencies. Organizations lose on average 6% of their global annual revenues, or $406 million, based on respondents from organizations with an average global annual revenue of $5.6 billion (USD). This is due to underperforming AI models, which are built using inaccurate or low-quality data, resulting in misinformed business decisions.
Conducted by independent market research specialist Vanson Bourne, the online survey polled 550 respondents across the U.S., U.K., Ireland, France, and Germany from organizations with 500 or more employees. It found that nearly nine in ten organizations 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, organizations express challenges of data inaccuracies and hallucinations, and concerns about data governance and security. U.S. organizations leveraging large language models (LLMs) report data inaccuracies and hallucinations 50% of the time.
“The rapid uptake of generative AI reflects widespread optimism and confidence within organizations, but under the surface, basic data issues are still prevalent, which are holding organizations back from realizing their full potential,” said Taylor Brown, co-founder and COO at Fivetran. “Organizations need to strengthen their data integration and governance foundations to create more reliable AI outputs and mitigate financial risk.”
Different “AI Realities” Exist Across Various Job Roles
Approximately one in four (24%) organizations reported having reached an advanced stage of AI adoption, where they utilize 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 organizations’ 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.
There is a further dissonance between data experts at various levels of seniority within an organization. Although 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%). It is true that data workers are forced to direct their resources towards manual data processes such as cleaning data and fixing broken data pipelines. In fact, organizations admit that their data scientists spend the majority (67%) of their time preparing data, rather than building AI models.
Bad Data Practices Still Prevalent
The roots of the wasted data talent potential and underperforming AI programs are the same: inaccessible, unreliable, and incorrect data. The magnitude of the issue is shown by the fact that most organizations struggle to access all the data needed to run AI programs (69%) and cleanse the data into a usable format (68%).
New generative AI use cases have introduced further complications, with 42% of respondents experiencing data hallucinations. These can lead to ill-informed decisions, reduce trust in LLMs, or the willingness of staff to use the tool and consume staff time in locating and correcting the data. With 60% of senior management using generative AI -- and their responsibility to make strategic decisions -- any issues with the quality and trustworthiness of data will be further amplified.
Data Governance a Key Focus Area for AI Use
Fears of generative AI use also remain, with “maintaining data governance” and “financial risk due to the sensitivity of data” tying for the top spot of concerns among organizations (37%). Solid data governance foundations will be particularly important for organizations that plan to either build their own generative AI models or use a combination of existing external and internally developed models. However, as the majority (67%) of respondents plan to deploy new technology to strengthen basic data movement, governance, and security functions, there is reason for optimism.
To download the complete report, please visit Fivetran + Vanson Bourne report: AI in 2024 (short registration required).
For additional insights from the Fivetran AI survey, please visit the Fivetran blog.