A new Singapore Rackspace Technology report finds AI/ML technologies are increasingly mission-critical, but the full benefits have yet to be realized.
Rackspace Technology, a leading end-to-end, multi-cloud technology solutions company, has announced a new research report that finds that while Artificial Intelligence and Machine Learning (AI/ML) are on nearly every organization’s radar much work remains to be done to tap their full potential.
Rackspace Technology polled 1,870 global IT leaders, across industries, including manufacturing, financial services, retail, government and healthcare to understand the dynamics of AI/ML uptake. A total of 187 Singapore correspondents participated in the survey.
A total 99% of Singapore respondents said that AI/ML is a priority for their organization, and 72% of all respondents reported positive impacts on brand awareness, 68% reported revenue generation and expense reduction. However, 42% agreed that measuring and proving the technologies’ business value remains a challenge.
“As AI/ML budgets continue to increase, we are seeing projects proliferate across more areas of the organization, and it’s clear that the AI/ML is advancing in its importance and visibility,” said Jeff DeVerter, Chief Technology Evangelist, Rackspace Technology.
“At the same time, the research makes clear that many organizations still struggle with getting stakeholder buy-in, addressing issues of data quality, and finding the skills, resources and talent to take advantage of the AI/ML’s full potential.”
According to the report – AI/ML Is A Top Priority for Businesses, But Are They Realizing Its Value? – AI/ML ranks among the top two most important strategic technologies for organizations, alongside cybersecurity.
A total of 69% of respondents say they are employing AI/ML as part of their business strategy and 62% for IT strategy, while 68% of respondents are allocating between 6% and 10% of their budget to AI/ML projects. This compares to a reported spend (as a percentage of the overall budget) of between 1% and 10% in last year’s survey.
AI/ML projects are accelerating
AI/ML are being used by Singapore organizations in an increasingly wide variety of contexts, including improving the speed and efficiency of processes (52%), understanding marketing effectiveness (45%), increasing revenue, gaining competitive edge, and predicting performance (42%), and personalizing content and understanding customers (41%).
In an indication of the increasing maturity of the technologies, 67% of respondents said their AI/ML projects have gone past the experimentation stage and are now either in the ‘optimizing/innovating’ or ‘formalizing’ states of implementation. Most organizations are also citing a wider range of use cases, including computer vision applications, automated content moderation, customer relationship management and biomedical applications.
Progress and challenges
With regard to AI/ML adoption, 35% of Singapore respondents cite difficulties aligning AI/ML strategies to the business. In addition, the cost of implementation rose to 28%, while 37% of respondents see nascent AI/ML technologies as a barrier.
“The fact that many organizations are having trouble aligning AI/ML strategies to the business and navigating the plethora of new tools available indicates that projects are often falling victim to poor strategy,” added DeVerter.
“Garnering support from the right stakeholders, coming to consensus on deliverables, understanding the resources necessary to get there, and setting clear milestones are critical components to keeping projects on track and seeing the desired return on investment.”
From a talent perspective, more than half of respondents said they have necessary AI/ML skills within their organization. At the same time, more than half of all respondents say that bolstering internal skills/hired talent and improving both internal and external training are on their agenda.
Comparing departments, 77% of Singapore respondents say IT staff grasp AI/ML benefits as compared to 48% in R&D, 46% in operations, customer service, senior management and boards understand the technologies. Sales, HR and marketing departments are considered by respondents to be the least AI/ML-savvy.
Implications for the AI/ML journey
For businesses starting or struggling to implement AI/ML learning initiatives, the report says three action steps stand out:
Strategy first: Without a solid destination and organizational buy-in, your AI and ML efforts could waste a lot of money and resources and never become production-ready. Start by gathering the major stakeholders, presenting a strong business case and gaining consensus on deliverables, milestones and timelines to keep your project on track.
Address data quality and accuracy: An AI and ML program requires clean, integrated data. The first step in a successful AI and ML program is cleaning up your data and data processes, which includes setting definitions, eliminating data silos, establishing governance and aligning business processes.
Focus on training talent: Respondents consistently listed lack of understanding and refining talent as concerns. Specific focus on upskilling and internal training will improve understanding of AI/ML. Assess your company’s current training efforts and talent-in-place to determine whether you can fill roles or need to unveil exterior training, raise attendance of conferences/events and recommend online coursework/reading.
For more information on the trends that will shape AI/ML in 2022 and to download a copy of the full report, visit here.
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