Less than half of firms succeed in AI, ML R&D

Photo by Franck V.

The majority of organisations worldwide lack the internal resources to support critical artificial intelligence (AI) and machine learning (ML) initiatives, a report from Rackspace Technology shows.

Conducted by Coleman Parkes Research in December 2020 and January 2021, the survey covered 1,870 IT decision-makers across the Americas, Europe, Asia and the Middle East, including 53 who are based in Singapore.

Results 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.

Respondents from Asia Pacific and Japan were more likely to be using AI/ML in more applications and use cases, and are spending significantly more on average than global participants — $1.3 million versus $1.06 million.

Respondents in the APJ region also noted seeing more benefits of their AI/ML efforts such as increased productivity and better streamlined processes.

One of the biggest impacts of AI/ML for businesses in APJ  has been the “blurring of lines between human and technology factors”, which is 5% higher from what the global respondents have stated.

Among respondents in Singapore, 25% of report mature AI and machine learning capabilities with a model factory framework in place. In addition, 75% said they are still exploring how to implement AI or struggling to operationalise AI and machine learning models.

Close to one-third (32%) of respondents report artificial intelligence R&D initiatives that have been tested and abandoned or failed. The top causes for failure include poorly conceived strategy (43%), lack of expertise within the organisation (34%), lack of data quality (36%) and lack of production-ready data (36%).

Also, organisations see AI and machine learning potential in a variety of business units, including operations (68%), IT (57%), customer service (45%), and Supply Chain Management (45%).

The top key performance indicators used to measure AI/ML success include, revenue growth (69%), data analysis (66%), and Process enhancement/ improvement (66%).

Given the high risk of implementation failure, the majority of organisations (66%) are, to some degree, working with an experienced provider to navigate the complexities of AI and machine learning development.

“The research survey suggests that Singapore businesses want to improve the speed and efficiency of existing processes improve productivity and the understanding of business and customers,” said Sandeep Bhargarva, managing director of Rackspace Asia Pacific and Japan.

“That said, before diving headfirst into an AI/ML initiative, we advise customers to clean their data and data processes,” said Bhargarva.