The majority of global organizations lack the necessary resources to facilitate artificial intelligence (AI) and machine learning (ML) initiatives, a new survey from cloud firm Rackspace Technology suggests.
Based on a poll of 1,870 IT professionals, the report reveals that the majority of organizations (82%) worldwide have yet to put the required infrastructure in place to implement AI and ML successfully.
Failures of implementation, Rackspace says, come down to a number of different factors, from the lack of quality data (34%) and skills gaps (34%) to poorly conceived strategies (31%).
Further, many organizations have yet to decide whether to outsource AI and ML development or build projects themselves from scratch.
“In nearly every industry, we’re seeing IT decision-makers turn to artificial intelligence and machine learning to improve efficiency and customer satisfaction,” said Tolga Tarhan, Chief Technology Officer at Rackspace Technology.
“But before diving headfirst into an AI/ML initiative, we advise customers to clean their data and data processes. In other words, get the right data into the right systems in a reliable and cost-effective manner.”
According to Rackspace, establishing clear KPIs is also crucial to measuring the success of AI initiatives. The most commonly used KPIs today include profit margin (52%), revenue growth (51%) and customer satisfaction/net promoter scores (46%).