Organizations don’t trust AI enough to give up human decision-making

Fivetran announced survey results showing that while 87% of organizations view artificial intelligence (AI) as vital to the survival of their business, 86% say they will struggle to fully trust AI to make all business decisions without human intervention. 90% of respondents say their organizations continue to rely on manual data processes.

Conducted by Vanson Bourne, the online survey of 550 senior IT and data science professionals in the US, UK, Ireland, France and Germany also found that only 14% of organizations rate their IT maturity as “advanced,” meaning they use general-purpose applications. AI. to automatically make predictions and trading decisions. 41% of respondents admitted that there is a lot of room for improvement in the way their organization uses AI. That number jumped to 64% when looking only at US respondents.

“This study highlights significant gaps in the effective movement and access of data between organizations. A successful AI program depends on a strong database, starting with a cloud data warehouse or data lake as its foundation,” he said. George FraserCEO of Fivetran. “Analytics teams using a modern data stack can more easily extend the value of their data and maximize their investments in AI and data science. »

Inefficient data processes reduce AI breakthroughs and revenue gains

Organizations appear to be laying the groundwork for more sophisticated AI projects and plan to spend 13% of their global annual revenue on it over the next three to five years, up from 8% today. Almost all of the organizations surveyed already collect and use data from operating systems, but their ability to use this data for AI models is hampered by deep data issues:

  • 71% struggle to access all the data needed to run AI programs, workloads, and models.
  • At least 73% consider every step of extracting, loading and transforming data, until translating it into practical advice for decision-makers, to be a challenge.

These inefficient data processes force companies to rely on human decision-making 71% of the time. Underperforming AI programs are also hurting organizations financially, with respondents estimating they lose an average of 5% of their global annual revenue due to models built with inaccurate or poor quality data.

AI talent remains untapped

The prevalence of poor quality, isolated and outdated data means that data scientists, employed by all the major organizations surveyed, spend less than a third of their time building AI models, devoting the rest to tasks in outside of their job. .

As a result, 87% agree that data scientists within their organization are not utilized to their full potential. However, recruitment is cited (by 39%) as the biggest barrier to AI adoption, highlighting the responsibility of organizations to urgently leverage the talent they already have.

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