Fivetran, a global leader in modern data integration, has announced the results of a survey which shows that while 87% of organisations consider Artificial Intelligence (AI) vital to their business survival, 86% say they would struggle to fully trust AI to make all business decisions without human intervention. The majority (90%) of respondents report their organisations continue to rely on manual data processes.
Conducted by Vanson Bourne, the online survey of 550 senior IT and data science professionals across the US, UK, Ireland, France and Germany also found that only 14% of organisations 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%) conceded there was vast room for improvement in how their organisation used AI.
“This study highlights significant gaps in efficient data movement and access across organisations. A successful AI programme 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 utilise a modern data stack can more readily extend the value of their data and maximise their investments in AI and data science.”
Inefficient data processes curtail AI advancements and revenue gains
Organisations appear to be laying the foundation for more sophisticated AI projects and plan to invest 13% of their global annual revenue into them within the next three to five years – compared to the 8% being invested today. Almost all of the organisations 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:
- Only 71% struggle to access all the data needed to run AI programmes, workloads and models.
- At least 73% 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% of the time. Underperforming AI programmes are also hitting organisations financially, with respondents estimating they are losing out on an average of 5% 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 organisations 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% agree that data scientists within their organisation are not being utilised to their full potential. Yet, recruitment is cited (by 39%) as the biggest barrier to AI adoption, highlighting the responsibility of organisations to urgently empower the talent they already have.
