In a global survey of AI and machine learning adoption among 1,870 tech leaders, we revealed widespread challenges related to underlying issues with data quality and infrastructure.
That matters because the mountains of data that most businesses have continue to grow. As they do, so to do the opportunities to predict customer behavior, automate customer service and make operational forecasts – but only if organizations can reliably get their AI and machine learning projects from pilot to production.
The challenges include low confidence in data quality
To make this happen, businesses need secure, real-time data and — with more people accessing data from more locations — data platforms that are more flexible, scalable and secure than ever before. This whitepaper looks at how they’re managing data and the role of data modernization on their journey to achieving this, whether they’re exploring, formalizing or innovating in AI and machine learning.
Across these groups – and to varying degrees – barriers to adoption commonly arise from infrastructure and processes. But, digging deeper, we these organizations are also plagued by issues directly related to strategic challenges, like data security, identifying appropriate business cases, aligning AI and machine learning and a lack of confidence in data quality.
Find out more in the full report
You can find out more in the full whitepaper, including the importance of a formal data modernization program and the most commonly reported resource gaps. Also featured is the story of how Humen.AI quickly solved its data infrastructure challenges, reducing costs along the way without impacting response times.Download the full report
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