FEATURE : AI
Additional key findings of the report for Singapore respondents include the following :
• Organizations are still exploring how to implement mature AI / ML capabilities – A mere 25 % of respondents report mature AI and ML capabilities with a model factory framework in place . In addition , the majority of respondents ( 75 %) said they are still exploring how to implement AI or struggling to operationalise AI and ML models .
• AI / ML implementation fails often due to lack of internal resources – More than one-third ( 32 %) of respondents report AI 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 poorly conceived strategy ( 43 %), lack of expertise within the organization ( 34 %), lack of data quality ( 36 %) and lack of production-ready data ( 36 %).
• 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 operations ( 68 %), IT ( 57 %), customer service ( 45 %) and Supply Chain Management ( 45 %). Further , organizations 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 ( 66 %) are , to some degree , working with an experienced provider to navigate the complexities of AI and ML development .
“ In line with Singapore ’ s Smart Nation initiative , the country has been tapping on AI and automation to preserve its competitive advantage over other economies across industries ,” said Sandeep Bhargarva , Managing Director of Rackspace Asia Pacific and Japan .
“ In almost every industry today , we ’ re seeing IT decision-makers turn to AI and ML to improve efficiency and customer satisfaction .
“ The research survey suggests that Singapore businesses want to improve the speed and efficiency of existing processes improve productivity and the
WE ’ RE SEEING IT DECISION- MAKERS TURN TO AI AND ML TO IMPROVE EFFICIENCY AND CUSTOMER SATISFACTION .
that have successfully implemented AI and ML programs report increased productivity ( 47 %) and increased understanding of your business and customers ( 42 %) 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 : revenue growth ( 69 %), data analysis ( 66 %) and process enhancement / improvement ( 66 %).
• Organizations turn to trusted partners – Many organizations are still determining whether they
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