Fivetran, the global leader in modern data integration, today announced the results of a survey which shows that while 87 percent of organizations consider artificial intelligence (AI) vital to their business survival, 86 percent say they would struggle to fully trust AI to make all business decisions without human intervention. 90 percent of respondents report their organizations continue to rely on manual data processes.
Conducted by Vanson Bourne, the online survey of 550 senior IT and data science professionals across the U.S., U.K., Ireland, France and Germany also found that only 14 percent of organizations consider their AI maturity “advanced” – meaning that they use general purpose AI to automatically make predictions and business decisions. More than two in five respondents (41 percent) conceded there was vast room for improvement in how their organization used AI. That number spiked to 64 percent when looking at U.S. respondents only.
“This study highlights significant gaps in efficient data movement and access across organizations. A successful AI program depends on a solid data foundation, starting with a cloud data warehouse or lake as its base,” said George Fraser, CEO of Fivetran. “Analytic teams that utilize a modern data stack can more readily extend the value of their data and maximize their investments in AI and data science.”
Inefficient data processes curtail AI advancements and revenue gains
Organizations appear to be laying the foundation for more sophisticated AI projects and plan to invest 13 percent of their global annual revenue into them within the next three to five years – compared to the eight percent being invested today. Almost all of the organizations surveyed already collect and use data from operational systems, but their ability to use this data for AI models is hampered by deep-running data challenges:
- 71 percent struggle to access all the data needed to run AI programs, workloads and models
- At least 73 percent find each of the stages of extracting, loading and transforming the data, to translating it into practical advice for decision-makers a challenge
Such inefficient data processes force companies to rely on human-led decision-making 71 percent of the time. Underperforming AI programs are also hitting organizations financially, with respondents estimating they are losing out on an average of five percent of global annual revenues because of models built using inaccurate or low-quality data.
AI talent is left untapped
The prevalence of low-quality, siloed and stale data means that data scientists, employed by all large organizations surveyed, dedicate less than a third of their time to building AI models, spending the rest of it on tasks outside of their job role.
As a result, 87 percent agree that data scientists within their organization are not being utilized to their full potential. Yet, recruitment is cited (by 39 percent) as the biggest barrier to AI adoption, highlighting the responsibility of organizations to urgently empower the talent they already have.
