Study Uncovers Widespread AI and ML Knowledge Gap in APJ
Most organizations lack the internal resources to support critical artificial intelligence (AI) and machine learning (ML) initiatives, according to a new global study by multi-cloud technology solutions company Rackspace Technology,
While many organizations are eager to incorporate AI and ML tactics into operations, the study “Are Organizations Succeeding at AI and Machine Learning?” found that they typically don’t have the requisite expertise and existing infrastructure to implement mature and successful AI/ML programs.
Reaping benefits of AL and ML
Organizations typically struggle to balance the potential benefits of AI and ML against the ongoing challenges of getting AI/ML initiatives off the ground.
And while some early adopters are already seeing the benefits of these technologies, common pain points include lack of internal knowledge, outdated technology stacks, poor data quality, or the inability to measure ROI.
It is worth noting that APJ (Asia Pacific and Japan) participants 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 vs $1.06 million). Respondents in the APJ region also noted seeing more benefits of their AI/ML efforts such as increased productivity and streamlined processes.
While APJ participants rated themselves slightly higher at 18% compared to global statistics a 17% for advanced maturity in AI/ML), organizations here rate the inability to find the right data as a bigger challenge (26%) compared to global participants (22%).
Notable findings
Sandeep Bhargarva, the managing director of Rackspace Asia Pacific and Japan noted that businesses want to leverage 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 understanding of business and customers.”
“That said, before diving headfirst into an AI/ML initiative, we advise customers to 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,” he said.
Some additional findings from the study:
- Organizations still exploring how to implement mature AI/ML capabilities: Just one in four (25%) of respondents report mature AI and machine learning capabilities with a model factory framework in place. Most respondents (75%) said they are still exploring how to implement AI or struggling to operationalize AI and machine learning models.
- AI/ML implementation fails often due to lack of internal resources: More than one-third (32%) of respondents report AI initiatives that have been tested and abandoned or failed. 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 offers clear benefits: Organizations that have successfully implemented AI and machine learning programs report increased productivity (47%) and increased understanding of your business and customers (42%) as the top benefits.
The full report can be accessed here (free registration).
Image credit: iStockphoto/Sensay
