MInd the learning gap

The survey, “Are Organizations Succeeding at AI and ML?” was conducted in the Americas, APJ and EMEA regions of the world, and indicates that while many organizations are eager to incorporate AI and ML tactics into operations, they typically lack the expertise and existing infrastructure needed to implement mature and successful AI/ML programs.

This study shines a light on the struggle to balance the potential benefits of AI and ML against the ongoing challenges of getting AI/ML initiatives off the ground. While some early adopters are already seeing the benefits of these technologies, others are still trying to navigate common pain points such as lack of internal knowledge, outdated technology stacks, poor data quality or the inability to measure ROI.

Additional key findings of the report include the following:

• Organizations are still exploring how to implement mature AI/ML capabilities — A mere 17% of respondents report mature AI and ML capabilities with a model factory framework in place. In addition, the majority of respondents (82%) said they are still exploring how to implement AI or struggling to operationalize AI and ML models.

• AI/ML implementation fails often due to lack of internal resources — More than one-third (34%) of respondents report artificial intelligence R&D initiatives that have been tested and abandoned or failed. The failures underscore the complexities of building and running a productive AI and ML program. The top causes for failure include lack of data quality (34%), lack of expertise within the organization (34%), lack of production ready data (31%), and poorly conceived strategy (31%).

• Successful AI/ML implementation has clear benefits for early adopters — As organizations look to the future, IT and operations are the leading areas where they plan on adding AI and ML capabilities. The data reveals that organizations see AI and ML potential in a variety of business units, including IT (43%), operations (33%), customer service (32%), and finance (32%). Further, organizations that have successfully implemented AI and ML programs report increased productivity (33%) and improved customer satisfaction (32%) as the top benefits.

• Defining KPIs is critical to measuring AI/ML return on investment — Along with the difficulty of deploying AI and ML projects comes the difficulty of measurement. The top key performance indicators used to measure AI/ML success include profit margins (52%), revenue growth (51%), data analysis (46%), and customer satisfaction/net promoter scores (46%).

• Organizations turn to trusted partners — Many organizations are still determining whether they will build internal AI/ML support or outsource it to a trusted partner. But given the high risk of implementation failure, the majority of organizations (62%) are, to some degree, working with an experienced provider to navigate the complexities of AI and ML development.

“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. At Rackspace Technology, we’re proud to provide the expertise and strategy necessary to ensure AI/ML projects move beyond the R&D stage and into initiatives with long-term impacts.”