AI and ML the go to technologies?

A new survey of 1,420 global IT leaders by Rackspace Technology finds that a growing number of organizations – 69% – rank artificial intelligence and machine learning (AI/ML) as a high priority for their organizations, an increase of 15 percentage points as compared to 2021. The study polled IT professionals across industries, including financial services, manufacturing, retail, hospitality, government, and healthcare to understand the dynamics of AI/ML uptake amid growing economic uncertainty.

Underscoring the spread of AI/ML across businesses of all sectors, almost a third of respondents say they only started to launch AI/ML projects within the past year. In comparison, 17% say they began implementing AI/ML two years ago, 11% three years ago, and just 11% five years ago. Though most respondents said AI/ML is a high priority, only 41% say they have realized substantial benefits. In comparison, 33% have seen modest benefits, and 26% say it's too early to tell. Moreover, 62% of respondents say there has been internal pushback to the degree of adoption of AI/ML within their organizations.

"As AI and machine learning mature and projects mature, we are seeing it expand across the organization and become more ubiquitous, advancing in its importance, visibility, and usage," said Jeff DeVerter, Chief Technology Evangelist, Rackspace Technology. "The fact that almost a third of respondents began their AI/ML journey within the past year is striking and points to the fact that these technologies are seen as the key to driving efficiencies in uncertain economic times. At the same time, the research clarifies that many organizations are still struggling to understand or realize the technologies' full benefits of AI/ML, and many face internal resistance to adoption. All of this traction doesn't consider the meteoric pace of AI attention in recent weeks with the explosion of ChatGPT, which has given AI a front-row seat in every business planning meeting."

Benefits of AI/ML

More organizations are leveraging AI/ML to improve the speed and efficiency of existing processes, with 67% of organizations saying it is an area of focus versus just 52% in 2022. Other popular focus areas include predicting business performance/industry trends (60%) and reducing risk (53%). In addition, AI/ML is used by organizations in an increasingly wide variety of contexts, including hiring/recruiting new talent, increasing understanding of business and customers, and increasing innovation and productivity. In addition to internally improving organizations' productivity, AI/ML has been used by 60% of respondents to meet sustainability targets by improving worker safety/security, 54% of respondents to monitor energy consumption management and 47% of respondents for predictive analytics.

"According to 67% of respondents globally, IT and Business leaders are using AI/ML to improve business operations with low innovation due to moving into an uncertain economic climate," said Nirmal Ranganathan, Chief Architect, Data & AI, Rackspace Technology. "We see customers use AI/ML to build intelligent search capabilities to improve employee efficiencies, reduce manual processes by adopting intelligent document processing and improve customer experience through customer engagement solutions. Innovation through AI/ML is rocket fueling the consumer space with the mainstream adoption of Generative AI technologies." 

Progress and Challenges

The principal barriers to organizations' ability to effectively draw actionable insights from AI/ML include the inability to collate, structure, and integrate data meaningfully and the need for more capabilities or talent to manage data effectively. 82% of respondents say they have tried recruiting employees with AI/ML skills in the past 12 months, while 86% have grown their AI/ML workforce in the past 12 months. In many cases, AI/ML replaces work formerly performed by humans, with 62% of respondents saying that AI/ML implementation has led to a reduced headcount within their organization.

"Organizations that are struggling with AI/ML also tend to be organizations that have immature data cultures, low levels of data literacy, and poor data governance structures because any AI/ML project is only as good as the data being used," added Ben Blanquera, VP, Evangelist, and Senior Architect, Rackspace Technology. "For more projects to be successful and for organizations to derive long-term value, there needs to be a focus on data quality. For some companies, this may mean rethinking their entire data governance approach."

Trust in AI/ML

Despite data concerns and internal resistance, trust in the output of AI/ML projects remains high among IT decision-maker respondents, with 73% saying they have confidence in the answers provided by AI/ML. 72% say sufficient checks and balances are in place to avoid negative consequences from using AI/ML, while 80% of respondents do not think AI/ML answers require additional human interpretation.