One global study suggests your organization is not alone, and suggests prerequisites to be fulfilled for gaining a good ROI.
While many organizations are eager to incorporate AI and machine learning (ML) processes into operations, they may lack the expertise and infrastructure needed.
Between December 2020 and January 2021, Rackspace conducted a global survey of 1,870 IT decision-makers across manufacturing, digital native, financial services, retail, government/public sector, and healthcare sectors in the Americas, Europe, Asia and the Middle East to get a better picture of AI/ML learning adoption, usage, benefits, impact and future plans.
This study data suggests that while some early adopters are already seeing the benefits of implementing these technologies, others are still trying to navigate common pain points such as lack of internal knowledge, outdated technology stacks, poor data quality or the inability to measure return-on-investment (ROI).
How APJ fared
In the Asia Pacific and Japan region, respondents rated themselves similarly (at 18%) compared to global sentiments (17%) for advanced maturity in AI/ML. These APJ participants were more likely to be using AI/ML in more applications and use cases, and were spending significantly more (on average) than global participants (US$1.3 million vs US$1.06 million).
APJ respondents noted seeing more benefits from their AI/ML efforts such as increased productivity and better streamlined processes. However, the region found it more difficult (26%) to find the right data as compared to global figures (22%).
In Singapore, around 25% of respondents reported mature AI and machine learning capabilities with a model factory framework in place. In addition, the majority of respondents (75%) said they were still exploring how to implement AI or struggling to operationalize AI and machine learning models.
Finally, one of the biggest impacts of AI/ML for APJ businesses was the “blurring of lines between human and technology factors”, which was 5% higher than the global figure.
Preparing to succeed with AI/ML
Some general conclusions from the APJ data were that:
- Early adopters of AI/ML have had sufficient time (by the time of the study) to reap benefits from the investments.
- The top key performance indicators of AI/ML implementation success were revenue growth, data analysis and process enhancement/improvement.
- AI/ML adoption outcomes may be hampered by lack of internal resources. In order of highest impact, this ranged from poorly conceived strategy to lack of expertise within the organization to lack of data quality and lack of production-ready data.
- Organizations experiencing hurdles in implementing AI/ML internally may need to work with experienced external vendors
According to Sandeep Bhargarva, Managing Director, Rackspace Asia Pacific and Japan: ““The research survey suggests that businesses (in the region) want to improve the speed and efficiency of existing processes, and improve productivity and the understanding of business and customers. That said, before diving headfirst into an AI/ML initiative, customers should clean their data and data processes. In other words, get the right data into the right systems in a reliable and cost-effective manner first.”