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Research Study: Driving the rise of AI and ML with data

This study, conducted for Exasol by research firm Vanson Bourne, looked at how and why organisations across two major European markets are transforming from business intelligence to data analytics in order to make better use of their data, power modernisation efforts and service organisational demand. It surveyed a mix of IT and business decision makers in order to fully understand the variances in perception and actions between these two pivotal, but often differently motivated, decision-maker groups.


The rise of Loading...artificial intelligence (AI) and Loading...machine learning (ML) in business and industry is undoubted. It is disrupting a number of areas including point of entry (incident and request creation), automated backend processes and knowledge management.

Everything from supply chain and stock control to the automation of factories and repetitive data entry tasks is leveraging either AI or ML, with a ready supply of clean, accurate and detailed data essential to drive these algorithms. Being able to fully use the wide range of data collected within the organisation to inform automated decision-making is therefore critical. Asking an AI system to make informed judgements and recommendations when it only has half the information available to it can quickly undermine the effectiveness of any AI or ML investment.

While the value and importance of data is clear to businesses, the same people are not fully convinced about the immediate importance of AI to their organisations. Over a third (37%) of businesses consider AI to be somewhat important, with a further 27% considering it very important to short and medium-term business strategy.

Only 14% consider it to be critical to strategic aims. The comparisons with ML reveal an interesting difference in viewpoint and importance. We find that 36% perceive ML as somewhat important, with a further 30% considering it very important to short and medium-term business strategy. On top of this, 18% consider it to be critical. It is notable how, as we view the increasing perception of importance, we see that ML is considered to be more critical to business strategic objectives than AI – though both are important.

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