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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
THIS STUDY HIGHLIGHTS SIGNIFICANT GAPS IN EFFICIENT DATA MOVEMENT AND ACCESS ACROSS ORGANISATIONS .
because of models built using inaccurate or lowquality 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 . x
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