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New Global Rackspace Technology Study Uncovers Widespread Artificial Intelligence and Machine Learning Knowledge Gap

February 3, 2021

New Global Rackspace Technology Study Uncovers Widespread Artificial Intelligence and Machine Learning Knowledge Gap

LONDON, UK – February 3, 2021 – Companies in EMEA are lagging behind their global counterparts when it comes to artificial intelligence (AI) and machine learning (ML) implementation, with just one in eight organisations (12%) able to boast mature capabilities – compared to one in six (17%) worldwide.

This leaves the vast majority (88%) either at the early stages of exploring the technology’s potential (52%) or still requiring significant organisational work to implement an AI/ML solution (36%).

This is among the key findings of new research from Rackspace Technology, Inc. (NASDAQ: RXT), a leading end-to-end, multicloud technology solutions company, which reveals that the majority of organisations lack the internal resources to support critical AI and ML initiatives. 

The survey, “Are Organisations Succeeding at AI and Machine Learning?” indicates that while many organisations are eager to incorporate AI and ML tactics into operations, they typically lack the expertise and existing infrastructure needed to implement mature and successful programs. 

This study shines a light on the struggle to balance the potential benefits of AI and ML against the ongoing challenges of getting 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of these technologies, others are still trying to navigate common pain points such as lack of internal knowledge, outdated technology stacks, poor data quality or the inability to measure ROI.

Other key findings of the report include the following:
 

  • AI/ML implementation often fails due to lack of internal resources — More than one-third (34%) of respondents report AI research and development initiatives 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 lack of data quality (36%), lack of expertise within the organisation (33%), poorly conceived strategy (31%) and lack of production ready data (30%).
  • Successful AI/ML implementation has clear benefits for early adopters — As organisations look to the future, IT and operations are the leading areas where they plan on adding AI and ML capabilities. The data reveals that organisations see AI and ML potential in a variety of business units, including IT (37%), finance (30%) operations (29%), and customer service (27%). Further, organisations that have successfully implemented AI and ML programs report increased productivity (28%) and improved customer satisfaction (28%) 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 (49%), data analysis (44%), profit margins (42%), and customer satisfaction/net promoter scores (38%).
     
  • Organisations turn to trusted partners — Many organisations are still determining whether they will build internal AI and ML support, or outsource it to a trusted partner. But given the high risk of implementation failure, the majority of organisations (53%) are, to some degree, working with an experienced provider to navigate the complexities of AI and ML development. 

“Countries across EMEA are lagging behind in AI and ML implementation, which can be hindering their competitive edge and innovation,” said Simon Bennett, Chief Technology Officer, EMEA at Rackspace Technology. “Globally we’re seeing  IT decision-makers turn to these technologies to  improve efficiency and customer satisfaction. Working with a trusted third party provider, organisations can enhance their AI/ML projects moving beyond the R&D stage and into initiatives with long-term impacts.”

To download the full report, please  click here. 

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 machine learning adoption, usage, benefits, impact and future plans.

About Rackspace Technology

Rackspace Technology is a leading end-to-end multicloud technology services company. We can design, build and operate our customers’ cloud environments across all major technology platforms, irrespective of technology stack or deployment model. We partner with our customers at every stage of their cloud journey, enabling them to modernize applications, build new products and adopt innovative technologies.

About Rackspace Technology

Rackspace Technology® (NASDAQ: RXT) is the operator of the full enterprise AI stack from governed private cloud to AI inference and agents in production. With an Outcomes-as-a-Service model built on secure infrastructure, data foundations, and forward-deployed engineering, Rackspace delivers business results for regulated and mission-critical industries where governance, sovereignty, and uptime are non-negotiable. Learn more at www.rackspace.com.

Forward-Looking Statements

This press release contains “forward-looking statements” within the meaning of the safe harbor provisions of the Private Securities Litigation Reform Act of 1995. Forward-looking statements generally relate to future events and can be identified by terminology such as “believe,” “may,” “will,” “potentially,” “estimate,” “continues,” “anticipate,” “intend,” “could,” “would,” “project,” “plan,” “expect,” “is putting,” “designed to,” or the negative of such terms or other similar expressions, although not all forward-looking statements contain these words.

 These statements include, but are not limited to, Rackspace Technology’s relationship with NVIDIA and the NVIDIA Cloud Partner Program, its Enterprise AI Cloud strategy, the anticipated capabilities, benefits and timing of its Institutional Sovereign Pod architecture, Rackspace’s outlook on the AI industry and its ability to deliver, operate, and scale AI infrastructure and solutions for regulated, institutional and sovereign enterprises. Forward-looking statements are based on current expectations and assumptions and are subject to risks and uncertainties that could cause actual results to differ materially, including, without limitation: Rackspace's ability to realize the anticipated benefits of its relationship with NVIDIA and the NCP program; the availability, cost, and timing of deployment of NVIDIA and other third-party technology; Rackspace's ability to develop, market, and scale the Institutional Sovereign Pod and related offerings on the anticipated timeline or at all; market acceptance of and demand for sovereign and governed AI infrastructure solutions; competition in the AI infrastructure and cloud services markets; capital expenditure requirements associated with AI infrastructure buildout; availability of financing for the AI infrastructure buildout on terms acceptable to Rackspace; Rackspace’s reliance on a limited number of technology partners, including NVIDIA; cybersecurity, data privacy, and regulatory compliance risks associated with serving regulated and sovereign customers; general economic and industry conditions; and other risks and uncertainties described in Rackspace's filings with the Securities and Exchange Commission, including its most recent Annual Report on Form 10-K and Quarterly Reports on Form 10-Q. Rackspace undertakes no obligation to update or revise any forward-looking statements, whether as a result of new information, future events, or otherwise, except as required by law.