Rackspace Technology study uncovers widespread AI and ML knowledge gap – Intelligent CIO ME

  • Lauren
  • February 10, 2021
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A Rackspace Technology study that looks into whether AI and Machine Learning initiatives are working for organizations finds only 45% are successful in the AI/ML R&D they undertake.

Rackspace Technology, a leading end-to-end, multi-cloud technology solutions company, has announced the results of a global survey that reveals that the majority of organizations globally lack the internal resources to support critical Artificial Intelligence (AI) and Machine Learning (ML) initiatives. The survey results included responses from 53 Singapore based respondents.

The survey, ‘Are Organizations Succeeding at AI and Machine Learning?’, was conducted in the Americas, Asia Pacific and Japan and EMEA (Europe, the Middle East and Africa) regions, and indicates that while many organizations 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.

This study shines a light on the struggle to balance the potential benefits of AI and ML against the on-going challenges of getting AI/ML initiatives off the ground. While some early adopters are already seeing the benefits of these technologies, others are still trying to navigate common pain points such as lack of internal knowledge, outdated technology stacks, poor data quality or the inability to measure ROI.

Participants of the survey in the APJ region rated themselves slightly higher at 18% compared to global statistics a 17% for advanced maturity in AI/ML). APJ 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 (US$1.3 million vs US$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.

Interestingly, the APJ region rates the inability to find the right data as a bigger challenge (26%) compared to global participants (22%). Also, 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.

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 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 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 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. We are constantly working with customers in Singapore and the region to support them by providing the necessary expertise and strategy to ensure AI/ML projects move beyond the R&D stage and into initiatives with tangible cost and long-term gains.”

Survey methodology

Conducted by Coleman Parkes Research in December 2020 and January 2021, the survey is based on the responses 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. The survey questions covered AI and ML adoption, usage, benefits, impact and future plans.
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Source: https://www.intelligentcio.com/apac/2021/02/10/rackspace-technology-study-uncovers-widespread-ai-and-machine-learning-knowledge-gap/