UK - bottom of the class for AI?

This is among the key findings of new research from Rackspace Technology, which reveals that the majority of organisations lack the internal resources to support critical AI and ML initiatives. 

The survey, “Are Organizations Succeeding at AI and Machine Learning?” indicates that while many organisations 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of these technologies, others are still trying to navigate common pain points such as lack of internal knowledge, outdated technology stacks, poor data quality or the inability to measure ROI.

Other key findings of the report include the following:

• AI/ML implementation often fails due to lack of internal resources — More than one-third (35%) of UK respondents report AI research and development initiatives 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 (36%), lack of expertise within the organisation (34%), poorly conceived strategy (31%) and lack of an integrated development environment (27%).

• Successful AI/ML implementation has clear benefits for early adopters — As organisations look to the future, IT and operations are the leading areas where they plan on adding AI and ML capabilities. The data reveals that UK organisations see AI and ML potential in a variety of business units, including IT (37%), finance (31%), operations (29%), and marketing (25%). Further, organisations that have successfully implemented AI and ML programs report increased productivity (30%) and improved customer satisfaction (30%) 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 customer satisfaction/net promoter scores (46%), revenue growth (42%), profit margins (42%) and data analysis (38%).
• Organisations turn to trusted partners — Many UK organisations 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, a large proportion of organisations (48%) are, to some degree, working with an experienced provider to navigate the complexities of AI and ML development. 

“Countries across EMEA, including the UK, are lagging behind in AI and ML implementation, which can be hindering their competitive edge and innovation,” said Simon Bennett, Chief Technology Officer, EMEA at Rackspace Technology. “Globally we’re seeing IT decision-makers turn to these technologies to improve efficiency and customer satisfaction. Working with a trusted third-party provider, organisations can enhance their AI/ML projects moving beyond the R&D stage and into initiatives with long-term impacts.”