New Rackspace Technology Report Finds AI/ML Technologies Increasingly Mission-Critical, But Full Benefits Have Yet to Be Realized

(US and Canada) Rackspace Technology, a leading end-to-end, multicloud technology solutions company based in San Antonio, Texas, 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.

While 62% of respondents said that AI/ML is a high priority for their organization, and 70% of all respondents reported positive impacts of on-brand awareness and reputation, as well as revenue generation and expense reduction, 36% 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. 72% of respondents say they are employing AI/ML as part of their business strategy, IT strategy or both, while 69% 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 overall budget) of between 1% and 10% in last year’s survey.

AI/ML Projects are Accelerating

AI/ML are being used by organizations in an increasingly wide variety of contexts, including improving the speed and efficiency of processes (52%), personalizing content and understanding customers (44%), increasing revenue, gaining competitive edge and predicting performance (42%), and understanding marketing effectiveness (36%).

In an indication of the increasing maturity of the technologies, 66% 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, 33% of respondents cite difficulties aligning AI/ML strategies to the business – a year-over-year increase of 10%. In addition, the cost of implementation rose from 26% to 33%, while 31% of respondents of nascent AI/ML technologies as a barrier, representing an increase of 13%.

“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.”

Organizational Understanding

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, 69% of respondents say IT staff grasp AI/ML benefits while 43% say that operations, R&D, 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.