Data published last year showed the benefits of AI both in terms of profitability and jobs in those companies that master it.  Alas, the data also showed that this mastery is very unevenly distributed, with a pioneering few marching ahead of the laggards who struggle to adapt AI into their business.

New research from Rackspace suggests that this is a particular problem for UK companies, who the company believes are falling behind their international counterparts.  Indeed, just 10% of British firms are masters in AI, compared to 17% globally.  The report suggests that this slow progress is primarily due to a lack of internal resources.

Faltering progress

There is no lack of motivation to try AI-based initiatives, but the lack of skills is resulting in few initiatives getting off the ground.  The laggards are generally suffering from outdated technology, poor data quality, and a lack of internal knowledge.

“Countries across EMEA, including the UK, are lagging behind in AI and ML implementation, which can be hindering their competitive edge and innovation,” the authors say. “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.”

The report also finds that:

• 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.